CN111179117A - Calculation method and device for situation awareness effect evaluation of intelligent power distribution network - Google Patents
Calculation method and device for situation awareness effect evaluation of intelligent power distribution network Download PDFInfo
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Abstract
The invention relates to a method and a device for evaluating situation perception effect of an intelligent power distribution network, which comprises the following steps: step 1, collecting power grid operation data required by situation awareness effect evaluation of the intelligent power distribution network; step 2, constructing an index system for evaluating the situation perception effect of the intelligent power distribution network; step 3, quantitatively calculating indexes for evaluating the situation perception effect of the intelligent power distribution network; the situation perception effect of the power distribution network can be reasonably evaluated, and guarantee is provided for self-optimization operation of the intelligent power distribution network.
Description
Technical Field
The invention belongs to the technical field of power distribution networks, relates to a situation perception method of an intelligent power distribution network, and particularly relates to a calculation method and device for evaluating situation perception effects of the intelligent power distribution network.
Background
With the continuous improvement of social economy and development level, the urbanization process of China is obviously accelerated, the demand of the society on electric power is continuously increased, and the electric power load is increased year by year, so that higher requirements on the construction and the electric power supply of a power distribution network are provided. The intelligent power distribution network which is safe, reliable, economical, efficient, advanced in technology and environment-friendly is an important support for the healthy development of the power system and is a fundamental guarantee for the stability and flourishing of social economy. In recent years, global climate changes very often due to the aggravation of greenhouse effect, natural disasters such as typhoons and ice disasters frequently occur, and challenges are brought to situation perception of the intelligent power distribution network; along with the popularization and application of various novel devices, the difficulty of power distribution network source data acquisition is greatly reduced, the measurement precision is obviously improved, the working environment of distribution network personnel is more comfortable, but the data processing pressure of a system is inevitably increased by well-spraying type data; the continuous grid connection of distributed power generation and new energy power generation and the popularization of electric vehicles reduce the power supply pressure of the power distribution network to a certain extent, but also bring new absorption pressure and load impact, so that the running state of the intelligent power distribution network is more flexible and changeable; the integration application of emerging technologies such as 'cloud big object moving intelligence' and the like in the power distribution network greatly accelerates the interactive sharing of data information, but also adds a lot of uncertainty to the power distribution network, and the frequent change of the operation state brings difficulty to the comprehensive evaluation of the situation perception effect of the power distribution network.
The situation awareness is a technical means for the overall dynamic insight based on the environment and covering perception, understanding and prediction, has been widely applied to various fields such as safety, information, judicial law, intelligent traffic and the like, gradually becomes a research direction of digitalization and informatization in the future, is an important basis for safe, stable and reliable operation of the power distribution network, determines the intelligent development level and the operation benefit of the power distribution network according to the situation awareness effect, evaluates the situation awareness effect, can discover weak links and potential dangers of the power distribution network in time, improves the visibility of the power distribution system, and provides reference information for active intelligent prevention and control. Therefore, a set of multi-level and multi-dimensional comprehensive evaluation model for situation and effect of the intelligent power distribution network is required to be constructed, monitoring data of the power distribution network is fully utilized, operation reliability and stability of the power distribution network are maintained, and a powerful judgment basis is provided for dispatching operation and demand response of power enterprises.
In recent years, situation awareness technologies have penetrated the aspects of intelligent power distribution networks, but most of the existing achievements focus on specific performance or specific links of the power distribution networks for state assessment, and the existing comprehensive assessment methods provided for situation awareness indexes of the power distribution networks are very deficient, most of the adopted comprehensive assessment methods are based on the combination of a least square method, an entropy weight method and other methods, and the situation awareness technologies have one-sidedness.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method and a device for evaluating situation perception effect of an intelligent power distribution network, which are reasonable in design and efficient in system.
The invention solves the practical problem by adopting the following technical scheme:
a calculation method for evaluating situation awareness effects of an intelligent power distribution network comprises the following steps:
step 1, collecting power grid operation data required by situation awareness effect evaluation of the intelligent power distribution network;
step 2, constructing an index system for evaluating the situation perception effect of the intelligent power distribution network; the index system for evaluating the situation perception effect of the power distribution network is established from three different angles of situation awareness, situation understanding and situation prediction based on the situation perception key technology of the intelligent power distribution network and the target planning of the operation of the power distribution network; obtaining situation perception effect evaluation indexes of the intelligent power distribution network through the technology in the step 1;
and step 3, quantitatively calculating indexes for evaluating the situation perception effect of the intelligent power distribution network.
The specific step of the step 3 for evaluating the situation perception effect of the intelligent power distribution network comprises the following steps:
(1) constructing a calculation formula according to the situation perception effect evaluation index system of the intelligent power distribution network screened in the step 2;
(2) and (4) quantitatively calculating each index value of the situation perception effect evaluation of the intelligent power distribution network by combining the data collected in the step (1).
(3) And multiplying the calculated index result by the average weight, and summing to obtain the evaluation value of the situation perception effect of the intelligent power distribution network.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method steps of claims 1-2. … … are provided.
The invention is also implemented by adopting the following technical scheme:
an effect evaluation device of a calculation method for evaluating situation awareness effects of an intelligent power distribution network comprises a gateway, a computer medium, a display module and a case;
the gateway is used for transmitting information to a computer by adopting power grid operation data of a SCADA system, a distribution network automation system, a distribution network energy management system, a GIS system, a power grid information management system and other terminals through an optical fiber private network and a wireless network, and carrying out protocol conversion by virtue of the gateway so as to collect the information according to the data range in the step 1;
the computer medium is used for constructing and screening the intelligent distribution network situation perception effect evaluation index system in the step 1 and the intelligent distribution network situation perception effect evaluation index calculation method in the step 1, performing quantitative calculation work on the intelligent distribution network situation perception effect evaluation index, and finally calculating the intelligent distribution network situation perception effect evaluation result in the comprehensive evaluation method in the step 2 to finish evaluation of the intelligent distribution network situation perception effect;
the display module is used for completing interaction between equipment and a user and realizing visualization of situation perception effect of the intelligent power distribution network;
the case comprises a mouse, a keyboard and a machine body shell; the mouse and the keyboard are used for completing the interaction between a user and the equipment; the case shell is provided with protective facilities for the operating console and the machine body, so that the mouse and the keyboard are prevented from being damaged when not used, and meanwhile, people around can be prevented from peeping privacy input such as passwords in the operating process, and the using process is optimized.
The invention has the advantages and beneficial effects that:
the invention establishes a scientific and systematic situation effect evaluation index system aiming at the key technology of situation perception of the intelligent power distribution network. Through the situation effect evaluation index system and the scientific evaluation method, the situation effect evaluation effect index is in a loop with the key technology of power distribution network situation perception, the effect of power distribution network situation perception can be reasonably quantized, the influence of various factors on the power distribution network can be adapted, objective measurement information is fully utilized, the subjective judgment of decision makers and managers is fused, the one-sidedness and the irrationality of the conventional power distribution network evaluation work are eliminated, and powerful theoretical support and guidance are provided for the work of operation planning, dispatching, overhauling, trend operation and the like of the power distribution network.
Drawings
FIG. 1 is a key technical framework diagram of situation awareness of a smart distribution network according to the present invention;
FIG. 2 is a diagram of a situation awareness assessment index architecture for a smart distribution network in accordance with the present invention;
fig. 3 is a diagram of the distribution network elasticity analysis of 4 phases in the present invention.
Detailed Description
The embodiments of the invention will be described in further detail below with reference to the accompanying drawings:
a calculation method for evaluating situation awareness effects of a smart distribution network is shown in fig. 1 to 3, and comprises the following steps:
step 1, collecting power grid operation data required by situation awareness effect evaluation of the intelligent power distribution network;
the specific method of the step 1 comprises the following steps:
collect the data that intelligent power distribution network situation perception effect evaluation needs acquireed, divide into electric power enterprise measurement data, electric power enterprise operation data and electric power enterprise external data 3 types altogether, include: the system comprises GIS data, town planning data, economic development data, user characteristic data, user electricity consumption data, meteorological data, distributed power supply data, electric vehicle data, electric energy quality data, customer service data, equipment reliability statistical data, equipment overhaul data, equipment operation data, power grid scheduling data, power grid planning data, fault data and other historical data and current data.
Step 2, constructing an index system for evaluating the situation perception effect of the intelligent power distribution network;
the specific method of the step 2 comprises the following steps:
(1) establishing a power distribution network situation perception effect evaluation index system from three different angles of 'situation awareness, situation understanding and situation prediction' based on a situation perception key technology of the intelligent power distribution network and a target plan of power distribution network operation; the situation perception effect evaluation index system comprises:
1. situation awareness effect index:
1.1PMU configuration level A1
Phasor Measurement Units (PMUs) are a novel measurement device for sensing the real-time state of the operation state of a power system. The PMU receives high-precision time signals of the unified clock source to realize synchronous acquisition of data of each node of the wide area power system, and directly measures node voltage phasor and related branch current phasor. In the actual power distribution network, a large number of nodes are provided with measuring devices unrealistically and uneconomically, so that more reasonable distribution of the measuring devices is determined, the data quality and the observability of the system are guaranteed, the economy is considered, and the reasonable configuration of PMUs is also a key technology for sensing the situation of the power distribution network.
1.2 observability degree A2
Whether the measurement system can estimate all state quantities or not is characterized by the observation degree of the power distribution network, and the power distribution network with good observation degree is the basis for situation understanding and situation prediction.
1.3 measuring redundancy A3。
The redundancy of the measurement system is the guarantee that state estimation and load flow calculation are accurate and reliable, and the improvement of the observability of the smart power grid firstly requires the improvement of the measurement redundancy. Measuring redundancy A3Refers to the ratio of the number of independent measurements of the system to the number of state variables of the system.
1.4 degree of identifiability A4
In the operation process of the power distribution network, a metering system and a communication system are influenced by environmental factors, human factors and the like in different degrees, so that bad data are inevitably generated, the situation awareness accuracy of the SDN is influenced, and the decision-making error is caused. Identifiability A of power distribution system4Representing the proportion of bad data in the monitoring data of the distribution network.
1.5 coverage rate A of intelligent ammeter5
The situation awareness is realized by firstly requiring the construction of an Advanced Measurement Infrastructure (AMI), and the smart electric meter is a key ring for constructing an advanced two-side system, and is beneficial to bidirectional interaction between a power grid and users, and is used for acquiring, metering, transmitting and processing electric energy data. Coverage rate A of intelligent electric meter5The ratio of the number of the intelligent electric meters to the number of the nodes of a certain power distribution network is indicated.
1.6 undetectable depth A6
The automation level of the distribution network at the present stage is not perfect, the rural distribution network cannot realize good observability, and partial voltage nodes which are not observable exist. Depth A not observable6The node i can not be observed to reach other observable nodesThe number of branches of the shortest path of the node.
1.7 mean time delay of communication A7
The time lag is a non-negligible factor affecting the situational awareness effect. The data with the time mark transmitted by the intelligent electric meter and other equipment in the power distribution network can obtain communication delay according to the difference value between the data receiving time and the data time mark time of the distribution network automation system, and average the communication delay of a certain amount of data with the time mark to obtain average communication delay. Average delay of communication A7And the difference value between the time when the network automation system receives the data and the time scale time of the data is assigned.
2. Situation understanding effect indexes are as follows:
2.1 distribution network elasticity B1
Assessment of the elasticity of the distribution network is an important component of situation understanding, which mainly measures the supporting and restoring capacity of the distribution network to critical loads in natural disasters. Distribution network elasticity B1The method is defined as whether the power distribution network can take active measures to ensure that the power supply of key loads in disasters is supplied and the capacity of the power failure load is rapidly recovered.
As shown in fig. 3, the distribution network elasticity analysis is divided into 4 stages: t is t0-t1In the prevention stage, the power distribution network is in a stable operation state in the stage; t is t1-t2In the infiltration stage, after the power distribution network suffers from disasters, the power supply capacity is reduced; t is t2-t3In order to adapt to the stage, the distribution network system takes emergency measures to improve the power supply capacity of the distribution network system; t is t3-t4In the recovery stage, faults are removed gradually, so that the power distribution network is recovered to a stable operation state gradually. Elasticity B of distribution network1The ratio of the integral of the actual power load curve to the integral of the desired performance curve may be quantified.
2.2N-1 line specific gravity B2
The N-1 principle means that after any one independent element (generator, transmission line, transformer, etc.) in N elements of the power system is cut off due to a fault, no power failure of a user caused by overload tripping of other lines should be caused. Satisfying the N-1 principle can improve the reliability of the system, and improve the indexes of situation understanding such as power supply capability, viability and the like. B is2The lines in the electrical system that satisfy the N-1 principle are assigned a high proportion of all lines.
Specific gravity B of 2.3N-1 transformer3
Specific gravity B of N-1 transformer3The transformers meeting the N-1 principle in the power system are assigned to account for the proportion of all the transformers.
2.4 Low-voltage distribution network power supply capability B4
The power supply capacity analysis technology of the power distribution network is a key technology for situation understanding, and the power distribution network with strong power supply capacity can provide more excellent, stable and reliable electric energy.
2.5 power supply capacity B of medium-high voltage distribution network5
Compared with a low-voltage distribution network, the medium-high voltage distribution network is complex in network structure, needs to consider the influence of connection modes such as a contact switch and the like, and is distinguished from the power supply capacity of the low-voltage distribution network.
2.6 degree of network Voltage offset B6
The situation understanding can be realized by calculating the operation data of the power grid through a three-term load flow calculation technology and a three-phase state estimation technology containing a distributed power supply, the operation of the power distribution network is monitored, and measures are taken to improve the electric energy quality of the power distribution network. Degree of network voltage offset B6Is the average of the voltage offsets of the nodes in the network.
2.7 State estimation accuracy B7
The three-phase state estimation is a generalized three-phase power flow calculation with higher precision and more measurement types, and the main function of the generalized three-phase power flow calculation is to provide an accurate data base for other analysis or execution functions of a system, and is a key technology for situation understanding. State estimation accuracy B7May be represented by the average relative error of the estimated value from the actual value.
2.8 number of voltage sags B8
Voltage sag is a key problem affecting power quality, a distribution network system with high flexibility requires load demand change and power output change caused by any reason, and a power system can guarantee sufficient power supply and good power quality. Number of voltage sags B8Is defined as the voltage temporary of a certain distribution network area within a certain timeThe number of drops.
2.9 degree of Current distortion B9
The distortion of the current will influence the stable work of equipment, may cause unusual phenomena such as generate heat, vibration, thereby reduce the power supply quality and influence situation understanding effect. Degree of current distortion B9As reflected by the maximum value of the harmonic current content of each node.
2.10 degree of voltage distortion B10
The voltage distortion has an effect not inferior to the current distortion, and the voltage distortion degree B10Can be reflected by the maximum value of the distortion rate of each node voltage.
2.11 connectivity loss B11
The connectivity loss is a performance index based on the network topology of the power distribution network, the level of the connectivity between the transformer substation and the load of the power distribution network is quantized, and the flexibility of the system is affected by the loss of the connectivity.
2.12 loss of blackout energy B12
The reasonable situation understanding can timely discover the hidden power supply troubles and weak links in the power distribution network, prevent the faults of the power distribution network from occurring, and improve the power supply reliability of the power distribution network, thereby reducing the loss of users. Loss of power supply interruption energy B12The power loss of a user caused by system power failure is described, and the situation understanding effect is directly reflected.
2.13 failure self-healing rate B13
The fault self-healing technology is an important direction for intelligent development of the power distribution network, power supply capacity, flexibility and viability of the power distribution network can be improved through good situation understanding, the topological structure of the power distribution network is optimized, and the power distribution network can recover power supply in time when a fault occurs. Self-healing rate of failure B13The ratio of the number of users realizing fault self-healing to the total number of users with faults in a statistical period (such as a quarter) is assigned to the power grid, and the good fault self-healing can improve the power supply reliability of the distribution network and indirectly reflect the situation understanding effect.
2.14 distributed Power Source absorption capability B14
Power system flexibility is defined in terms of economic constraints andunder the operation constraint, within a certain time scale, the power system quickly and effectively optimizes the capabilities of allocating the existing resources, quickly responding to the power change of the power grid and controlling the key operation parameters of the power grid. A large number of distributed power sources such as wind power and photovoltaic power generation in a power distribution network are connected to form a unique power source structure, the output of the power generation side has the characteristics of low controllability, strong randomness and uncertainty, and a flexible power system can timely process the uncertainty and the randomness of load demand change and power output change caused by the distributed power sources. The ability of the power distribution system to absorb distributed power indirectly reflects the flexibility of the power distribution system, and the absorption ability B of the distributed power14Representing the ratio of the output power of the distribution network actually absorbing the distributed power supply to the total load peak value.
3. Situation prediction effect index:
3.1 Power distribution network investment profitability C1
The distributed power supply access and the novel load can reduce the electricity selling amount and the electricity charge income; meanwhile, the intelligent power distribution network reduces the demand on the capacity of the power grid, slows down the construction and upgrading speed of the power grid and reduces the cost of power grid companies. The construction and the arrangement of a distribution network can be optimized through the load, the distributed power supply and the electric automobile output prediction in the situation prediction core technology, the economic operation of the distribution network is realized, and the power failure cost is reduced to the lowest through the distribution network safety risk analysis and early warning technology. Operating distribution network investment yield C1The economical efficiency of the operation of the distribution network is reflected, and the realization effect of the situation prediction key technology is indirectly reflected.
3.2 Power grid asset utilization C2
The accurate situation prediction is the key to realizing the electric power market and optimizing the dispatching, and the utilization rate C of the power grid assets2The distribution network dispatching effect and the electric power market effect can be checked, the optimal operation level of the distribution network is reflected, and the situation prediction effect is indirectly reflected.
3.3 degree of load fluctuation C3
The distributed power supply grid connection can be used as power supplement of a power distribution network, and accurate distributed power supply output prediction can perform load peak clipping and valley fillingThe method has the advantages that reasonable distributed power supply grid-connected plan is made by combining load hierarchical and hierarchical prediction and electric vehicle distributed charging prediction, large fluctuation of loads in the distribution network area can be further reduced, and stable and economic operation of the distribution network is facilitated. Degree of fluctuation C of load4The method represents the change rate of the power of the distributed power supply on a unit time scale, and indirectly reflects the key technical operation effect of situation prediction.
3.4 mean relative error of load prediction C4
Hierarchical prediction of each node load is a key technology in situation prediction, and the load prediction fitting degree C5The average relative error of the load prediction in the situation awareness process is reflected.
3.5 System equalization degree C5
The access of the distributed power supply causes the change of the flow direction of the power distribution network, the balance of the power distribution system is influenced, the line loss of the system can be increased due to the extremely unbalanced distribution of the load in the network, and the power utilization quality and the power supply safety of users are influenced. The power supply path can be changed through power distribution network reconstruction, and the power load on the heavy load line is transferred to the line with lighter load, so that the purpose of load balancing is achieved. And the intelligent power distribution network is combined with the situation prediction result to redistribute the system and balance the power flow distribution of the power distribution network. Degree of system equalization C5The method reflects the equilibrium degree of the power flow distribution of the lines in the power distribution system, and indirectly reflects the influence of the situation prediction result on the power distribution network.
3.6 degree of control of System load C6
Degree of control of system load C6The proportion of interruptible loads in the system to all loads is represented, the friendship degree of the loads in the distribution network area is represented, the basis of system scheduling and optimization operation is provided, and the effect of decision scheduling according to situation prediction is influenced.
3.7 distribution network failure rate C7
The early warning technology in the situation prediction can effectively reduce the fault probability of the power distribution network, improve the power supply reliability and reduce the power failure loss. Distribution network failure rate C7Describing the failure of an area of an electric distribution networkProbability.
3.8 distribution network Accident Risk C8
Through the safety risk analysis of the power distribution network, the accident risk indexes in the running process of the power distribution network can be quantized, the power distribution network is prompted to take measures to reduce risks, the acceptable accident risk level is determined, and the social and environmental influences caused by power failure are reduced to the minimum. Distribution network accident status C8And comprehensively considering the probability and the severity of risk occurrence and evaluating the risk cost of the current power distribution network.
3.9 distribution network operational Risk C9
The system operation risk is a key index of the power distribution network safety risk analysis and early warning technology, and is beneficial to timely mastering the power distribution network operation situation and timely discovering abnormal power supply and potential faults, so that the management of the power distribution network is enhanced, and the safe and economic operation level is improved. Distribution network operational risk C9The system operation risk comprehensively considers node voltage out-of-limit risk values and branch load flow out-of-limit risks, and the power distribution network operation risk is evaluated.
(2) After an initial power distribution network situation perception effect evaluation index system is established, removing part of indexes which cannot be calculated due to incomplete data according to the data collected in the step 1, and screening out calculable power distribution network situation perception effect evaluation indexes;
step 3, quantifying evaluation indexes of the situation perception effect of the intelligent calculation power distribution network;
(1) constructing a calculation formula according to the power distribution network situation perception effect evaluation index system screened out in the step 2; the specific index calculation formula comprises:
1. situation awareness effect index:
1.1PMU configuration level A1
For a certain area distribution network, the following measurement equation of the distribution system is provided:
z＝h(xk)+v
wherein x is a node complex voltage state variable vector; z is a measurement vector; v is a measurement error vector; h (-) is a nonlinear measurement function, and the measurement comprises active and reactive measurement at the initial end of the branch, active and reactive measurement at the tail end of the branch, active and reactive measurement injected into the node, and measurement of the voltage amplitude and phase angle of the node.
G(xk)＝HT(xk)R-1HT(xk)
A1＝tr(G(xk)-1)
In the formula, G (x)k) The gain matrix, also called Fisher Information Matrix (FIM), contains the system information obtained by measurement, and its inverse matrix G (x)k)-1The covariance matrix of the state estimation error represents the estimation effect of the measurement system, and is an important index for evaluating the configuration quality of the measurement system.
In the estimation field, G (x) is usually selectedk)-1The certain scalar function is used as a state estimation precision evaluation index, is used for quantitative evaluation of different angles of a state estimation confidence ellipsoid, and G (x) is selected in the textk)-1To evaluate the configuration of the PMUs.
1.2 observability degree A2
For having NPThe number of the state phasors of a certain area power distribution network of each node is N.
Then for node i:
in the formula: i, j represents a node number; riIs the observable degree of node i; biIs a 0-1 variable that characterizes whether node i is equipped with a PMU, and when node i is equipped with a PMU, bi1, otherwise 0; a isj,iIs the (j, i) element of the system network incidence matrix, has
Meanwhile, the influence of the existing measurement equipment and the zero injection node should be considered. The initial node observable condition is represented by vector BobsAnd zero injection node vector BzeroA description is given. If some nodes in the distribution network are configured with nodes capable of realizing observability, BobsThe middle corresponding element is set to be one. If one and only one of all nodes connected with the zero injection node is not observable, the unobservable node can be observable according to the calculation of other observable nodes. In conclusion, the modified distribution network observability formula is as follows:
in the formula: c. Cj,iA variable of 0-1, c when node i can be observed based on the measured data of the zero injection node j and its phase connection point setj,iIs 1, otherwise is 0; z is a radical ofjIs also a variable from 0 to 1, z is zero injection node when node j is zero injection nodejIs 1, otherwise is 0.
1.3 measuring redundancy A3。
Measuring redundancy A3The ratio of the number of independent measurements of the power distribution network system in a certain area to the number of state variables of the system is referred to.
In the formula SindepRepresenting the independent measurement quantity of the distribution network in a certain area; sstatusRepresenting the number of state variables of the distribution network in a certain area.
1.4 degree of identifiability A4
Identifiability A of power distribution system4Representing the proportion of bad data in the monitoring data of the distribution network in a certain area.
ri＝zi-hi(x)
、Ci＝zi (k)-zi (k-1)
In the formula, riDetecting a residual error; ciTo detect mutations; in practical application, the bad data can be detected by a method of combining residual error and mutation detection.
In the formula, NerrorThe number of error data of a power distribution network in a certain area is calculated; n is a radical ofsumAll data detected for a distribution network in a certain area.
1.5 coverage rate A of intelligent ammeter5
Coverage rate A of intelligent electric meter5The ratio of the number of the intelligent electric meters to the number of the nodes of the distribution network in a certain area is indicated.
In the formula, NPRepresenting the number of nodes of a distribution network in a certain area; n is a radical ofsThe number of the smart meters in a certain area distribution network is represented.
1.6 undetectable depth A6
Depth A not observable6The number of branches of the shortest path from an unobservable node i of a distribution network in a certain area to other observable nodes is specified.
where if the node i is considerable, then ηi0, otherwise 1.
1.7 mean time delay of communication A7
Average delay of communication A7The difference value between the time of the distribution network automation system in a certain area receiving data and the time scale time of the data is referred to.
In the formula, TxIndicating the time when the value is updated in other large areas or systems; t isnRepresenting the moment at which data is perceptually collected by the system; n is a radical ofdaThe selected communication data quantity with the time mark is indicated.
2. Situation understanding effect indexes are as follows:
2.1 distribution network elasticity B1
Elasticity B of distribution network1The ratio of the integral of the actual power load curve to the integral of the desired performance curve may be quantified.
In the formula PT(t) represents the normal operating power, P, of the load of the distribution network in a certain area at time tRAnd (t) represents the fault operation power of the distribution network load in a certain area at the moment t.
2.2N-1 line specific gravity B2
N-1 line specific gravity B2The proportion of all lines in a power distribution network in a certain area is determined according to the N-1 principle.
Specific gravity B of 2.3N-1 transformer3
Specific gravity B of N-1 transformer3The proportion of all transformers is represented by the transformers meeting the N-1 principle in a distribution network in a certain area.
2.4 Low-voltage distribution network power supply capability B4
The invention adopts the partition calculation aiming at the power supply capacity of the power distribution network, and under the condition of not considering the mutual power supply conversion of loads among low-voltage lines, the power supply capacity of a low-voltage power distribution area can be expressed as follows:
in the formula, B4-iThe power supply capacity of a low-voltage power distribution area i; sTD-iDistributing and transforming capacity for a power distribution area i; slThe sustained limit delivery capacity for the low voltage line l; omegaLVL-iThe number of all outgoing lines of the power distribution area i is aggregated.
Thus, the power supply capacity of the low voltage distribution network can be expressed as:
in the formula: b is4For the power supply capacity of low-voltage distribution networks, omegaLDNAnd numbering sets for all the low-voltage distribution network areas.
2.5 power supply capacity B of medium-high voltage distribution network5
The method is used for calculating the power supply capacity of the medium-high voltage distribution network based on the N-1 principle. Its line powering capability can be expressed as:
S'L＝ηmax,LSL
in the formula SLIs the sustained limit delivery capacity of the medium-high voltage line L; s'Lis the maximum transport capacity of the line L ηmax,LThe maximum load factor is that a single radiation line is generally taken as 0, the N-1 principle is adopted for wiring, for example, an overhead single interconnection is 0.5, an overhead double interconnection is 0.667, an overhead triple interconnection and more are provided, a bus connection switch and a cable double-ring network are arranged in a switch station and are 0.75, any main line of a cable N for one standby is 1, and a standby line is 0.
The power supply capacity of the medium and high voltage distribution network region i can be expressed as:
in the formula: b is5-iThe power supply capacity of a low-voltage power distribution area i; omegaVL-iThe number of all outgoing lines of the power distribution area i is aggregated.
The power supply capacity of the medium-high voltage distribution network can be expressed as:
in the formula: b is5For the power supply capacity of medium and high voltage distribution network, omegaDNThe numbers of all the areas of the medium-high voltage distribution network are collected.
2.6 degree of network Voltage offset B6
Degree of network voltage offset B6The average value of the voltage deviation of each node in the power distribution network in a certain area is obtained.
In the formula of Ui-eIs the nominal voltage of node i, Ui-ldIs the actual operating voltage of node i, NPIs the set of all node numbers in the distribution network of a certain area.
2.7 State estimation accuracy B7
State estimation accuracy B7Can be represented by the average relative error of the state estimation value and the actual value of a certain area distribution network.
Calculating formula and load prediction average relative error C4Similarly, but the state estimates are more numerous and not all listed here.
2.8 number of voltage sags B8
Number of voltage sags B8Defined as the number of voltage sags occurring in a given distribution network area over a given period of time.
In the formula, epsiloniIs the voltage sag rate of node i, NPThe number of nodes in a certain distribution network area.
2.9 degree of Current distortion B9
Degree of current distortion B9The maximum value of the harmonic current content of each node of the distribution network in a certain area can be used for reflecting.
In the formula I1,iEffective value of fundamental current of I-th node, Ih,iThe effective value of the total harmonic current of the ith node.
2.10 degree of voltage distortion B10
Degree of voltage distortion B10The maximum value of the voltage distortion rate of each node of the power distribution network in a certain area can be used for reflection.
In the formula of U1,iEffective value of fundamental current of i-th node, Uh,iThe effective value of the total harmonic current of the ith node.
2.11 connectivity loss B11
Loss of connectivity B11The communication degree level between the distribution network transformer substation and the load in a certain area is quantized.
In the formula NLDRefers to the number of loads, N, in a given distribution network areaT-iFor the number of ith load connected substations after the fault, NTe-iAnd initially connecting the number of the transformer stations for the ith load.
2.12 loss of blackout energy B12
Loss of power supply interruption energy B12The method describes the electric energy loss of users in a certain area power distribution network caused by system power failure, and directly reflects the situation understanding effect.
In the formula, Tf-iIs the load power off time of load i, LiIs the average load connected at load point i.
2.13 failure self-healing rate B13
Self-healing rate of failure B13The ratio of the number of users who realize fault self-healing to the total number of users who have faults in a certain area distribution network in a statistical period (such as a quarter) is referred to.
In the formula, NhRepresenting the number of users realizing fault self-healing in a statistical time period; n is a radical ofgRepresenting the total number of users that failed within the statistical period.
2.14 distributed Power Source absorption capability B14
Distributed power supply absorption capacity B14The ratio of the output electric quantity of the distributed power supply actually absorbed by the distribution network in a certain area to the total load peak value is represented.
In the formula, PDGRepresenting the actual absorption of the output power, P, of the distributed power supply by the distribution network in a certain areamRepresenting the total load peak.
(3) Situation prediction effect index:
3.1 Power distribution network investment profitability C1
Distribution network investment profitability C1The economical efficiency of the operation of a distribution network in a certain area is reflected, and the realization effect of the situation prediction key technology is indirectly reflected.
In the formula, SR is the income of selling electricity; IC is the investment cost; OC is the operating cost; PC is the electricity purchasing cost; OU is the cost of power outage.
In the formula, ESi,tSupplying power generation amount (kw · h) for the ith load node in the T hour within the time T; cpThe average electricity selling price (yuan/kw.h) of the electricity quantity; t is a selected time period; n is a radical ofLDThe number of load nodes.
In the formula SPiTotal cost (dollars) for class i devices; m isiThe service life duration (h) of the ith type of equipment; n is a radical ofDEThe number of equipment categories.
Wherein PLOS represents a line loss amount (kW. h); ST (ST)iTotal failure time (h) for the ith type of equipment; riThe maintenance cost (yuan/h) of the ith equipment.
In the formula, E0,tIn the time length T, the actual electricity purchasing quantity (kW.h) of the system in the T hour; cbrThe average electricity purchase price (yuan/kW.h) is obtained.
In the formula, EENS is the annual expected power shortage (kW · h), and a is the ratio of the electricity price per unit power failure to the average electricity price.
3.2 Power grid asset utilization C2
Where PRO and CAP are total earnings and total assets, respectively, up to T hours; SRmAnd EBmThe electricity selling income and the environmental benefit of the mth hour are respectively.
3.3 degree of load fluctuation C3
Degree of fluctuation C of load4Representing the rate of change of power of the distributed power supply on a unit time scale.
In the formula, PDG(t) represents the output power of a distributed power supply connected to a certain distribution network area at time t, PDG(t +1) represents the output power of the distributed power supply connected to a certain distribution network area at the moment of t +1, PEV(t) represents charging power of electric vehicle connected to certain power distribution network area at time t，PEV(t) represents the charging power, P, of an electric vehicle connected to a certain distribution network area at time tEV(t +1) represents the charging power of the electric vehicle accessed to a certain distribution network area at the time of t +1, PLD2(t) represents the output power of the remaining loads connected to a certain distribution network area at time t, PLD2(T +1) represents the output power of the rest of the loads connected to a certain distribution network area at time T +1seIs a selected time period.
3.4 mean relative error of load prediction C4
Load prediction fitting degree C5The average relative error of load prediction in the process of sensing the situation of the distribution network in a certain area is reflected.
In the formula Pfor-iRepresenting the predicted load value, P, of the ith nodereal-iRepresenting the actual load value of the ith node.
3.5 System equalization degree C5
Degree of system equalization C5The method reflects the equilibrium degree of the power flow distribution of the circuits in the power distribution network in a certain area, and indirectly reflects the influence of the situation prediction result on the power distribution network.
In the formula SLiRepresenting the actual power of line i, SLNiRepresenting the real-time limit capacity, N, of the line ilRepresenting the number of branches of a distribution network in a certain area.
3.6 degree of control of System load C6
Degree of control of system load C6Representing the proportion of interruptible loads to all loads in a distribution network in a certain area.
LARepresenting interruptible loads in a certain distribution area, LBRepresenting an uninterruptible load within a certain distribution area.
3.10 distribution network failure rate C7
Distribution network failure rate C7The probability of a failure of a distribution network in a certain area is described.
In the formula NDERepresenting the number of devices in a power distribution network of a certain area, Pfault-iThe probability of failure, which represents device i, can be obtained from historical data.
3.11 distribution network Accident Risk C8
Distribution network accident status C8The probability and the severity of the risk occurrence of the power distribution network in a certain area are comprehensively considered, and the risk cost is described.
In the formula Lloss-iRepresenting the fault severity of the equipment, the economic loss of each equipment after different types of faults can be known according to historical data, and the fault severity of each equipment can be determined and replaced by an AHP method if data is missing.
3.12 distribution network operational Risk C9
Distribution network operational risk C9Node voltage out-of-limit risk values and branch load flow out-of-limit risks are comprehensively considered, and potential risks of operation of the power distribution network in a certain area are represented.
In the formula
Indicating the node voltage out-of-limit risk at the moment t;
representing the branch power flow out-of-limit risk at the moment t; mu.s1、μ2Respectively setting weights according to the voltage and current target requirements of the distribution network; t isseIs the selected time length; vi-tRepresenting the voltage of the node i at the time t; vi-minRepresents the lower limit voltage of the node i; vi-maxRepresents the lower limit voltage of the node i; n is a radical oflineThe number of branches of a power distribution network in a certain area is indicated; si-tIndicating the power flow of the branch i at the moment t; si-maxRefers to the upper bound flow of branch i.
(2) And (4) quantitatively calculating each index value of the situation perception effect evaluation of the intelligent power distribution network by combining the data collected in the step (1).
(3) And multiplying the calculated index result by the average weight, and summing to obtain the evaluation value of the situation perception effect of the intelligent power distribution network.
An effect evaluation device of a calculation method for evaluating situation awareness effects of an intelligent power distribution network comprises a gateway, a computer, a display module and a case;
the gateway is used for transmitting the power grid operation data from terminals such as an SCADA system, a distribution network automation system, a distribution network energy management system, a GIS system, a power grid information management system and the like to a computer through an optical fiber private network and a wireless network, and performing protocol conversion by means of the gateway so as to collect the data according to the data range in the step 1;
the computer is used for constructing and screening the power distribution network situation perception effect evaluation index system according to the step 2 and the power distribution network situation perception effect evaluation index calculation method according to the step 3, carrying out quantitative calculation work on the power distribution network situation perception effect evaluation index, and finally calculating a power distribution network situation perception effect evaluation result according to the comprehensive evaluation method of the step 4 to finish evaluation of the power distribution network situation perception effect;
the display module is used for completing the interaction between the equipment and the user and realizing the visualization of the situation perception effect of the power distribution network;
the case comprises a mouse, a keyboard and a machine body shell. The mouse and the keyboard are used for completing the interaction between a user and the equipment; the case shell is provided with protective facilities for the operating console and the machine body, so that the mouse and the keyboard are prevented from being damaged when not used, and meanwhile, people around can be prevented from peeping privacy input such as passwords in the operating process, and the using process is optimized.
It should be emphasized that the examples described herein are illustrative and not restrictive, and thus the present invention includes, but is not limited to, those examples described in this detailed description, as well as other embodiments that can be derived from the teachings of the present invention by those skilled in the art and that are within the scope of the present invention.
Claims (4)
1. A calculation method for evaluating situation awareness effects of an intelligent power distribution network is characterized by comprising the following steps: the method comprises the following steps:
step 1, collecting power grid operation data required by situation awareness effect evaluation of the intelligent power distribution network;
step 2, constructing an index system for evaluating the situation perception effect of the intelligent power distribution network; the index system for evaluating the situation perception effect of the power distribution network is established from three different angles of situation awareness, situation understanding and situation prediction based on the situation perception key technology of the intelligent power distribution network and the target planning of the operation of the power distribution network; obtaining situation perception effect evaluation indexes of the intelligent power distribution network through the technology in the step 1;
and step 3, quantitatively calculating indexes for evaluating the situation perception effect of the intelligent power distribution network.
2. The computing method for situation awareness effect assessment of the intelligent power distribution network according to claim 1, wherein: the specific steps of the step 3 comprise:
(1) constructing a calculation formula according to the situation perception effect evaluation index system of the intelligent power distribution network screened in the step 2;
(2) and (4) quantitatively calculating each index value of the situation perception effect evaluation of the intelligent power distribution network by combining the data collected in the step (1).
(3) And multiplying the calculated index result by the average weight, and summing to obtain the evaluation value of the situation perception effect of the intelligent power distribution network.
3. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method steps of claims 1-2.
4. The effect evaluation device of the calculation method for situation awareness effect evaluation of the intelligent power distribution network according to any one of claims 1 to 3, comprising a gateway, a computer medium, a display module and a case;
the gateway is used for transmitting information to a computer by adopting power grid operation data of a SCADA system, a distribution network automation system, a distribution network energy management system, a GIS system, a power grid information management system and other terminals through an optical fiber private network and a wireless network, and carrying out protocol conversion by virtue of the gateway so as to collect the information according to the data range in the step 1;
the computer medium is used for constructing and screening the intelligent distribution network situation perception effect evaluation index system in the step 1 and the intelligent distribution network situation perception effect evaluation index calculation method in the step 1, performing quantitative calculation work on the intelligent distribution network situation perception effect evaluation index, and finally calculating the intelligent distribution network situation perception effect evaluation result in the comprehensive evaluation method in the step 2 to finish evaluation of the intelligent distribution network situation perception effect;
the display module is used for completing interaction between equipment and a user and realizing visualization of situation perception effect of the intelligent power distribution network;
the case comprises a mouse, a keyboard and a machine body shell; the mouse and the keyboard are used for completing the interaction between a user and the equipment; the case shell is provided with protective facilities for the operating console and the machine body, so that the mouse and the keyboard are prevented from being damaged when not in use.
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