CN117439090A - Flexible resource allocation or scheduling method taking flexible adjustment coefficient as index - Google Patents
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Abstract
The invention discloses a flexible resource allocation or scheduling method taking a flexible adjustment coefficient as an index, which relates to the technical field of flexible adjustment of power systems, and is characterized in that the larger the value is, the more abundant the flexibility is by defining the flexible adjustment coefficient as the ratio of the flexible allowance of a node or a power grid to the net load fluctuation amplitude; establishing a flexible resource optimization configuration model, solving, and increasing or reducing flexible resources at each node according to the solving result; and establishing a power grid flexible adjustment dynamic programming model, solving, and determining the injection power increased or reduced by each node in one adjustment period according to the solving result. The invention can evaluate the flexibility of the whole network, guide the configuration of flexible resources at the planning level and guide the flexible operation of the system at the scheduling level.
Description
Technical Field
The invention relates to the technical field of flexible adjustment of an electric power system in the field of new energy, in particular to a flexible resource allocation or scheduling method taking a flexible adjustment coefficient as an index.
Background
In recent years, as the power generation rate of renewable energy source is gradually increased, the power system is facing challenges of increasing flexibility requirements and insufficient flexible adjustment capability. The flexibility of the power system reflects the capability of maintaining the balance of supply and demand of the system when the supply and demand relation is changed, and a plurality of researches related to the power system are conducted in the current academy. The method for quantitatively evaluating the flexibility is a key for researching flexible resource allocation and optimizing operation of the power system. The existing flexibility evaluation methods can be divided into two main types, namely a static evaluation method and a dynamic evaluation method, and the dynamic evaluation method can be divided into two types, namely a probability evaluation method and a section evaluation method.
The static evaluation method adopts a method similar to expert evaluation to evaluate the supply and demand of the system flexibility. Most typically, the scoring method is proposed by the international energy agency (International Energy Agency, IEA) and is based on factors such as grid area coverage, grid strength, adjustable flexible unit share, and the like, and scores are made for four types of flexible resources of a conventional power plant, energy storage equipment, loads capable of participating in demand side response, and a grid. In addition, some students define unit flexibility indexes and system flexibility indexes according to flexibility parameters of the unit, and set up a unit combination model considering unit extension with the aim of minimum total cost. Other students construct a flexibility assessment index system which is 'considerable, measurable, adjustable and controllable' aiming at adjustable loads, and give scoring standards for the flexibility of the loads in different market scenes. The assessment of flexibility in the above documents is mostly limited to the flexibility resource level, even if the whole network is involved, the whole assessment of the flexibility of the power system is not performed, and all power sources, loads and energy storage of the whole network are checked one by one, so that the implementation difficulty is high, and the feasibility is low.
The dynamic evaluation method sets a plurality of scenes to carry out production simulation, and judges the flexibility of the system according to whether load loss exists or not or the renewable energy power limit. The probability evaluation method is based on the historical power probability distribution of flexible resources, fluctuating power supplies and loads, and evaluates the overall flexibility level of the system. Some scholars consider the probability that the system cannot meet the load change situation as an index for measuring the flexibility of the power system, namely the insufficient climbing resource desire (insufficient ramping resource expectation, IRRE). Other students calculate the system flexibility allowance probability distribution, and then calculate the probability and expectation when the system flexibility is insufficient, and the allowance expectation and probability when the flexibility is sufficient, and use the probability as the system flexibility evaluation index. The probability evaluation method is suitable for a system planning layer, but can not directly tell operators the flexible adjustment capability available at each moment of the system.
The interval evaluation method gives out an adjustable interval of the system through real-time calculation, and judges the flexibility level of the system in operation. Some scholars consider the interval prediction of the net load prediction, establish a system maximum adjustment capability assessment model based on robust optimization and cost constraint, and the envelope relation between the optimal interval and the uncertainty demand interval reflects whether the adjustment flexibility on the system (lower) is sufficient or not. Other scholars define the maximum allowable range of wind power node output, namely a wind power node output 'not exceeding' (DNE) interval index, which is used for guiding the operation scheduling of each wind power node. The interval evaluation method is suitable for guiding the system to flexibly operate in real time, but cannot make overall evaluation on the flexibility level of the system at all moments.
In summary, all the existing flexibility evaluation methods have defects, wherein the static evaluation method is difficult to evaluate the flexibility of the whole network, the probability evaluation method is difficult to guide the system scheduling, and the interval evaluation method is not suitable for the power grid planning.
Disclosure of Invention
The invention aims at solving the problem that the existing flexibility evaluation method cannot comprehensively consider the whole network scheduling and the whole network planning, and provides a flexibility resource allocation or scheduling method taking a flexible adjustment coefficient as an index, which is used for evaluating the flexibility of the whole network, guiding the allocation of flexibility resources at the planning level and guiding the flexible operation of a system at the scheduling level.
The aim of the invention is realized by the following technical scheme:
a flexible resource allocation or scheduling method taking flexible adjustment coefficients as indexes comprises the following steps:
acquiring parameters of the regional power grid and each node, wherein the parameters comprise net load time sequence of the regional power grid, each lower power grid and each node, injection power of each node in normal operation, upward climbing slope, downward climbing slope and branch data of the regional power grid of a power supply contained in each node; calculating flexible adjustment coefficients of the current adjustment period of the regional power grid and each node, wherein the flexible adjustment coefficients comprise flexible up-adjustment coefficients and flexible down-adjustment coefficients, and the flexible up-adjustment coefficients are the ratio of the flexible up-allowance of the node or the power grid to the upward fluctuation amplitude of the net load; the flexible down-regulating coefficient is the ratio of the flexible down-regulating margin of the node or the power grid to the downward fluctuation amplitude of the net load;
taking the minimum total capacity of newly added resources as an objective function, taking the configured flexible adjustment coefficient of the regional power grid as a constraint condition that the newly added resource capacity of each node is larger than a set value, and taking the newly added resource capacity of each node as a constraint condition, establishing a flexible resource optimization configuration model under any adjustment period, solving, and adding or reducing flexible resources at each node according to a solving result;
and/or acquiring the flexibility requirement of the regional power grid in real time, sequentially participating in flexible adjustment by each node of the regional power grid according to the size of a flexible adjustment coefficient, setting the flexibility requirement of the regional power grid in the stage k as a stage k in the process of participating in adjustment by the node k as a stage k state variable, setting the increased power of the node k as a decision variable and an index function of the stage k, setting the maximum value of the index function as an optimal index function, establishing a dynamic planning model for flexible adjustment of the power grid, solving the decision variable value meeting the optimal index function value to obtain a decision result of each stage, and scheduling the increased or decreased injection power of each node in the current adjustment period according to the decision result.
Further, the net load upward fluctuation amplitude of the node or the power grid is represented by the maximum value of a net load fluctuation sequence, and the net load downward fluctuation amplitude of the node or the power grid is represented by the absolute value of the minimum value of the net load fluctuation sequence; the payload fluctuation sequence is obtained by performing first-order differential operation on the obtained payload time sequence.
Further, the flexible up-scaling factor and flexible down-scaling factor are calculated by:
wherein:flexible up-regulation factor for the current regulation period T,/->A flexible down-regulation coefficient for the current regulation period T; />An upward ramp slope of the nth power source for the power network or node within the current regulation period T +.>The downward climbing slope of the nth power supply of the power grid or the node in the current regulation period T; />For the flexibility of the node or the network to be increased by a margin during a control period, < >>A downward margin for flexibility of a node or a power grid in one adjustment period;for the net load up-fluctuation amplitude of the node or the network in the current regulation period T,/v>The net load of the node or grid fluctuates down in magnitude for the current regulation period T.
Further, the flexible resource optimization configuration model is as follows:
wherein: subscript i denotes the ith node, subscript j denotes the jth lower grid,flexible up-regulation factor for the current regulation period T of the ith node, < >>The magnitude of the upward fluctuation of the net load for the ith node in a conditioning period T, +.>The magnitude of the upward fluctuation of the net load for the j-th lower network in a control period T,/->Flexible down-regulation factor for the current regulation period T of the ith node, +.>For the i-th node the magnitude of the payload down-surge in a regulation period T, +.>For the magnitude of the net load down-surge of the j-th lower grid in one regulation period T, f is the total cost of flexible resource construction,C fx fixed cost required for flexible resource construction; />Up-regulating total capacity of resources for local regional power grid>Construction cost for up-regulating resource capacity for node i unit,/->Newly increasing up-regulated resource capacity for node i in current regulation period T,/>Flexibly up-regulating the set value of the coefficient for the local regional power grid, < >>Allowing the newly added up-regulation resource maximum capacity for the node i in the current regulation period T; />Newly increasing down-regulating total capacity of resources for local regional power grid,/->Construction cost for downregulating resource capacity for node i unit,/->Newly increasing down-regulated resource capacity for node i in current regulation period T,/>Flexibly down-regulating the set value of the coefficient for the local regional power grid, < >>A new added down-regulated resource maximum capacity is allowed for node i in the current regulation period T,for the whole positive real number set, +.>To fluctuate the magnitude of the net load of the local area network upwards during a regulation period T,for the net load of the local area network to fluctuate downwards by an amplitude within a regulation period T.
Further, the flexible power grid adjustment dynamic programming model is as follows:
wherein: f (f) k S is the optimal index function of the regional power grid in the stage k k D is a state variable of phase k k As a set of allowed decisions for stage k,increasing power for node k; />And->Flexible down-scaling factor and flexible up-scaling factor for node k, respectively, < >>And->The net load up-fluctuation amplitude and the net load down-fluctuation amplitude of the node k are respectively, P i For the injection power of node i in normal operation, T li Is the element of the jth row and the ith column of the direct current power flow distribution factor matrix, F j,max Is the maximum allowable value of the flow of power through line j.
Further, the state transition equation of the power grid flexible adjustment dynamic programming model is as follows:
wherein: subscripts k and k+1 denote phase numbers, s k As a state variable for phase k,the power to be delivered for node k is increased,is a flexible requirement of the regional power grid.
A flexible resource allocation or scheduling system with flexible adjustment coefficients as indicators, comprising:
the flexible adjustment coefficient calculation module is used for obtaining parameters of the regional power grid and each node, including the net load time sequence of the regional power grid, each lower power grid and each node, injection power of each node in normal operation, upward climbing slope, downward climbing slope of a power supply contained in each node and branch data of the regional power grid, and calculating flexible adjustment coefficients of the current adjustment period of the regional power grid and each node, wherein the flexible adjustment coefficients comprise flexible up-regulation coefficients and flexible down-regulation coefficients, and the flexible up-regulation coefficients are the ratio of the flexible up-allowance of the node or the power grid to the upward fluctuation amplitude of the net load; the flexible down-regulating coefficient is the ratio of the flexible down-regulating margin of the node or the power grid to the downward fluctuation amplitude of the net load;
the resource allocation module is used for establishing a flexible resource optimization allocation model under any adjustment period and solving the flexible resource optimization allocation model by taking the minimum total capacity of newly-added resources as an objective function and taking the configured flexible adjustment coefficient of the regional power grid as a constraint condition that the newly-added resource capacity of each node is larger than a set value and the newly-added resource capacity of each node is smaller than the set value, and increasing or decreasing flexible resources at each node according to a solving result;
and/or a scheduling module, which is used for acquiring the flexibility requirement of the regional power grid in real time, each node of the regional power grid sequentially participates in flexible adjustment according to the size of the flexible adjustment coefficient, the process of participating in adjustment of the node k is set as a stage k, the flexibility requirement of the regional power grid in the stage k is set as a state variable of the stage k, the power increased by the node k is set as a decision variable and an index function of the stage k, the maximum value of the index function is set as an optimal index function, a dynamic planning model of the power grid flexible adjustment is established, the decision variable value meeting the optimal index function value is solved, the decision result of each stage is obtained, and the injection power increased or decreased by each node in the current adjustment period is scheduled according to the decision result.
The beneficial effects of the invention are as follows: 1. the method can simply and conveniently evaluate the flexibility of the whole network, and overcomes the defects that the static evaluation method is difficult to implement and only stays at the flexible resource level; 2. the method is suitable for flexible resource planning, and solves the problem that the interval evaluation method cannot make overall evaluation on the flexibility level at any moment; 3. the method can guide the flexible operation of the power system, and solves the problem that the probability evaluation method can not give a scheduling scheme in real time; 4. the analytic expression of the flexible resource optimization configuration model is given, iteration is not needed in the solving process, the calculating time is short, and the problems that the probability evaluation model is complex in calculation and difficult to give the analytic expression are solved; 5. the flexible regulation process is calculated by adopting the dynamic programming model, so that the flexible resource output of each node and the regulation power distribution of each line in the power grid can be coordinated, and compared with the traditional model, the flexibility of the power grid can be kept more abundant, so that the next flexible regulation can be dealt with.
Drawings
FIG. 1 is a topology of an IEEE 30 node system after preprocessing;
FIG. 2 is a diagram of the result of a configuration scheme when the flexible up-adjustment coefficient set value is 1 and the flexible down-adjustment coefficient set value is 1 to 1.1 respectively;
FIG. 3 is a diagram showing the result of a configuration scheme when the flexible up-adjustment coefficient set values are respectively 1-1.1 and the flexible down-adjustment coefficient set values are 1;
FIG. 4 is a diagram of the result of the configuration scheme when the flexible up-adjustment coefficient set value and the flexible down-adjustment coefficient set value are equal and 1 to 1.1 are taken;
fig. 5 is a power variation distribution diagram of the system under different flexibility demands, where (a) in fig. 5 is a power variation distribution diagram of the local area grid, and (b) in fig. 5 is a power variation distribution diagram of the lower level grid.
Detailed Description
The invention provides a flexible resource allocation or scheduling method taking a flexible adjustment coefficient as an index, which defines the flexible adjustment coefficient and gives out a calculation method thereof, and a flexible resource optimization allocation model and a flexible power grid adjustment dynamic planning model are established on the basis and are respectively used for guiding flexible planning and flexible scheduling of a system, in particular to the flexible resource allocation method taking the flexible adjustment coefficient as the index, which comprises the following steps:
step one: parameters of the regional power grid, each lower power grid and each node are obtained, the parameters comprise net load time sequence of the regional power grid, each lower power grid and each node, injection power of each node in normal operation, upward climbing slope, downward climbing slope and the like of a power supply contained in each node, and flexible adjustment coefficients of the current adjustment period of the regional power grid and each node are calculated; the flexible adjustment coefficient is defined as the ratio of the flexible allowance of the node or the power grid to the net load fluctuation amplitude; where the magnitude of the net load fluctuation refers to the maximum possible change in net load (i.e., total load minus the output of the fluctuating power source) of the node or grid over a conditioning cycle, and the margin of flexibility refers to the maximum power that the node or grid can condition over a conditioning cycle. The flexibility of the node or the power grid is evaluated by using the flexible adjustment coefficient, and the larger the flexible adjustment coefficient is, the more abundant the flexibility is.
Step two: and taking the minimum total capacity of newly added resources as an objective function, taking the configured flexible adjustment coefficient of the regional power grid as a constraint condition that the newly added resource capacity of each node is larger than a set value and smaller than the set value, establishing a flexible resource optimization configuration model under any adjustment period, solving, and adding or reducing flexible resources at each node according to a solving result. The flexible resource mainly refers to a generator set, and the newly increased resource capacity of each node refers to total adjustable power of the generator set newly increased by each node.
The invention relates to a scheduling method taking flexible adjustment coefficients as indexes, which comprises the following steps:
step one: parameters of the regional power grid, each lower power grid and each node are obtained, the parameters comprise net load time sequence of the regional power grid, each lower power grid and each node, injection power of each node in normal operation, upward climbing slope and downward climbing slope of a power supply contained by each node, branch data of the regional power grid and the like, and flexible adjustment coefficients of the current adjustment period of the regional power grid and each node are calculated; the flexible adjustment coefficient is defined as the ratio of the flexible allowance of the node or the power grid to the net load fluctuation amplitude; where the magnitude of the net load fluctuation refers to the maximum possible change in net load (i.e., total load minus the output of the fluctuating power source) of the node or grid over a conditioning cycle, and the margin of flexibility refers to the maximum power that the node or grid can condition over a conditioning cycle. The flexibility of the node or the power grid is evaluated by using the flexible adjustment coefficient, and the larger the flexible adjustment coefficient is, the more abundant the flexibility is.
Step two: the method comprises the steps of acquiring flexibility requirements of a regional power grid in real time, sequentially participating in flexible adjustment of nodes of the regional power grid from large to small according to flexible adjustment coefficients, setting the process of participating in adjustment of the nodes k as a stage k, setting the flexibility requirements of the regional power grid in the stage k as a state variable of the stage k, setting the power amplified by the nodes k as a decision variable and an index function of the stage k, setting the maximum value of the index function as an optimal index function, establishing a dynamic planning model for flexible adjustment of the power grid, solving decision variable values meeting the optimal index function values to obtain decision results of each stage, and scheduling the injection power increased or reduced by each node in the current adjustment period according to the decision results.
As an alternative embodiment, the flexible adjustment coefficient of step one is calculated by first calculating the magnitude of the payload fluctuation for the payload timing sequence P L Performing first-order differential operation, namely collecting one data every other regulating period T, and subtracting two adjacent data to obtain a net load fluctuation sequence; the upward fluctuation amplitude of the net load of the node or the power grid is represented by the maximum value of the net load fluctuation sequence, the downward fluctuation amplitude of the net load of the node or the power grid is represented by the absolute value of the minimum value of the net load fluctuation sequence, and the net load fluctuation amplitude of the power grid in the area is taken as an example, as shown in the formula (1):
（1）
wherein: for any positive integer m,the net load at the mT moment of the regional power grid; />For the upward fluctuation of the net load of the local power network in a regulation period T, +.>For a net load down-wave amplitude of the local regional power grid in a regulation period T;
then the ratio of the flexibility allowance to the net load fluctuation amplitude is defined as a flexible adjustment coefficient, and the calculation formula is as follows:
（2）
wherein:flexible up-regulation factor for the current regulation period T,/->A flexible down-regulation coefficient for the current regulation period T; />An upward ramp slope of the nth power source for the power network or node within the current regulation period T +.>The downward climbing slope of the nth power supply of the power grid or the node in the current regulation period T; />For the flexibility of the node or the network to be increased by a margin during a control period, < >>Is one ofThe flexibility of the node or the power grid in the adjustment period is limited downwards.
As an optional implementation manner, a scenario applicable to the flexible resource optimization configuration model in the flexible resource configuration method step two using the flexible adjustment coefficient as an index is: and configuring flexible resources with certain capacity at each node so as to improve the flexibility level of the regional power grid, namely, increasing the flexible adjustment coefficient of the power grid. The derivation process of the model is as follows:
firstly, under a time scale T, taking the minimum total capacity of newly added up-regulated resources as an objective function, taking the flexibly up-regulated coefficients of the regional power grid after configuration as constraint conditions that the flexibly up-regulated coefficients are larger than a set value and the newly added up-regulated resource capacities of all nodes are smaller than the set value, and obtaining an up-regulated resource optimal configuration model when the adjustment period is T:
（3）
wherein: subscript i denotes the ith node, subscript j denotes the jth lower grid,flexible up-regulation factor for the current regulation period T of the ith node, < >>The magnitude of the upward fluctuation of the net load for the ith node in a conditioning period T, +.>The magnitude of the upward fluctuation of the net load for the j-th lower network in a control period T,/->Up-regulating total capacity of resources for local regional power grid>Construction cost for up-regulating resource capacity for node i unit,/->Up-regulating resource capacity for node i>Flexibly up-regulating the set value of the coefficient for the local regional power grid, < >>And allowing the newly increased maximum capacity of the down-regulated resource for the node i, namely, newly increasing the set value of the up-regulated resource capacity for the node i.
Then, the same method is used for obtaining a down-regulation resource optimization configuration model when the regulation period is T:
（4）
wherein: subscript i denotes the ith node, subscript j denotes the jth lower grid,flexible down-regulation factor for the current regulation period T of the ith node, +.>For the i-th node the magnitude of the payload down-surge in a regulation period T, +.>For the j-th lower network, the magnitude of the downward fluctuation of the net load in a control period T,/->Newly increasing down-regulating total capacity of resources for local regional power grid,/->Construction cost for downregulating resource capacity for node i unit,/->Newly adding down-regulating resource capacity for node i, < >>Flexible for local regional power gridSetting value of down-regulating coefficient,/>The newly added down-regulated resource maximum capacity is allowed for node i.
And then calculating the total cost of flexible resource construction under the condition that any adjustment period is T:
（5）
wherein: f is the total cost of flexible resource construction, C fx For the fixed costs required for flexible resource construction,is a set of overall positive real numbers.
Equation (5) is the overall objective function of the flexible resource optimization configuration model. The solution to this objective is a min-max problem, where f U Determined by formula (3), f D Determined by equation (4). Finally, the combined type (3), 4 and 5) can obtain a flexible resource optimization configuration model, which is as follows:
as an optional implementation manner, a scenario applicable to the power grid flexible adjustment dynamic planning model in the scheduling method step two using the flexible adjustment coefficient as an index is as follows: in one regulation period, each node regulates the injection power of each node to meet the flexibility requirement of the regional power grid(i.e. all flexible resources need to be increased in total +.>The power of (c) in which the node with the large flexible adjustment coefficient participates in flexible adjustment preferentially. To adjust the injection power upwards (i.e +.>Greater than 0) for example, the model is derived as follows:
firstly renumbering each node according to the flexible up-regulation coefficient from big to small, and each node participates in flexible regulation in sequence according to the numbering sequence. According to the dynamic programming theory, the process of participating in adjustment of the node k is set as a stage k, the flexibility requirement of the regional power grid in the stage k is set as a state variable of the stage k, and the power amplified by any node k is set as a decision variable and an index function of the stage k. In order to make the node with large flexible adjustment coefficient participate in flexible adjustment preferentially, the node with small sequence number should increase the power as much as possible, so the maximum value of the index function is set as the optimal index function, and the decision variable value meeting the optimal index function value is the decision result of each stage, that is, the maximum value of the increased power of the node k is the decision result.
Initial conditions for dynamic programming are then calculated. Initial flexibility requirement of local regional power gridSet as state variable for phase 1, then the initial conditions are:
（7）
wherein: s is(s) 1 As a state variable of phase 1, f 1 As an optimal index function for phase 1,for the flexibility requirement of the local area network, +.>Power up for node 1, D 1 Is the allowed decision set for phase 1.
In equation (7), the decision set D is allowed 1 Also the constraint condition of the optimal index function value, namely: 1) The increased power of the node 1 is smaller than the flexibility requirement; 2) The increased power of the node 1 is within the range of the flexibility allowance of the node; 3) The line flow cannot exceed a limit value. Correspondingly, the number of decision results is calculatedThe study model is as follows:
（8）
wherein:and->Flexible down-regulation factor and flexible up-regulation factor of node 1, respectively, < >>And->The net load up-fluctuation amplitude and the net load down-fluctuation amplitude of the node 1 are respectively, T j1 Is the element of the jth row and the 1 st column of the direct current power flow distribution factor matrix, F j,max Is the maximum allowable value of the flow of power through line j.
And then calculating the decision result of any stage. In the process that the nodes 1 to k participate in regulation in turn, after the power is increased by each node, the flexibility requirement of the area is reduced. The reduction of the flexibility requirement of the k stage is equal to the power increased by the node k, so the state variable of the k stage minus the power increased by the node k is the state variable of the k+1 stage, and the following steps are:
（9）
wherein: subscripts k and k+1 denote phase numbers, s k As a state variable of stage k, f k As an optimal index function of stage k,power up for node k, D k+1 Is the allowed decision set for stage k+1.
In the formula (9), s k And s k+1 The recursive relation between the dynamic planning models is the state transition party of the flexible adjustment dynamic planning model of the power gridAnd (5) processing. Allowed decision set D k+1 Also the constraint condition of the optimal index function value, namely: 1) The increased power of the node k+1 is smaller than the flexibility requirement of the stage k+1; 2) The increased power of the node k+1 is within the range of the flexibility allowance of the node k+1; 3) The line flow cannot exceed a limit value. Accordingly, the mathematical model of the calculation decision result is:
（10）
wherein:and->Flexible down-regulation factor and flexible up-regulation factor of node k+1, respectively, +.>And->The net load up-fluctuation amplitude and the net load down-fluctuation amplitude of the node k+1 are respectively, T ji Is the element of the jth row and the ith column of the direct current power flow distribution factor matrix, F j,max Is the maximum allowable value of the flow of power through line j.
Finally, a basic equation of a flexible power grid adjustment dynamic programming model with more generality is obtained according to the formulas (7), (8), (9) and (10):
（11）
wherein: f (f) k S is the optimal index function of stage k k D is a state variable of phase k k As a set of allowed decisions for stage k,increasing power for node k; />And->The flexible down-scaling factor and flexible up-scaling factor of node k +1 respectively,and->The net load up-surge amplitude and net load down-surge amplitude of node k+1, P, respectively i For the injection power of node i in normal operation, T ji Is the element of the jth row and the ith column of the direct current power flow distribution factor matrix, F j,max Is the maximum allowable value of the flow of power through line j.
The state transfer equation of the model is:
（12）
wherein: subscripts k and k+1 denote phase numbers, s k As a state variable for phase k,the power to be delivered for node k is increased,is a flexible requirement of the regional power grid.
The invention provides a flexible resource optimal allocation model and a flexible adjustment dynamic planning model based on a flexible adjustment coefficient, wherein the flexible resource optimal allocation model realizes the site selection and volume fixing of flexible resources at a planning level, and the flexible resource dynamic planning model adjusts the output power of each flexible resource at a scheduling level. The invention is further described with reference to a specific example.
Taking a classical IEEE 30 node system as an example, preprocessing the system, dividing the whole system into an upper stage and a lower stage according to voltage levels, regarding an inter-stage interconnecting line as an equipotential point, and endowing the system with certain fluctuation according to power data of a power supply and a load in a certain northwest province of China. After the preprocessing, a preprocessed IEEE 30 node system as shown in fig. 1 is obtained, and a specific embodiment of the present invention will be described below in connection with the system.
The preprocessed system data are shown in table 1. The differences between the pretreated system and the classical system mainly include four aspects: (1) The IEEE 30 node system has two voltage levels, namely 132kV and 33kV, wherein the pretreated system regards the 132kV part of the system as a local area power grid, and the 33kV part of the system as a lower power grid; (2) The lines corresponding to the dashed lines in fig. 1 are isoelectric points, the impedances of which are all zero (the transformer impedance is all reduced to the high voltage side); (3) As can be seen from table 1, the power supply on the bus of the node 8 has reached the maximum output during normal operation, so the difference (5 MW) between the output and the load at the node 8 is equivalent to the power fixedly transmitted from the local regional power grid to the upper power grid; (4) The power supply and the load in the pretreated system have certain fluctuation, and have certain flexible adjustment capability, the adjustment coefficient and the fluctuation amplitude of each node are shown in table 1, and the data refer to the load fluctuation condition of a power grid of certain provincial level in northwest China.
Table 1 node data for one adjustment period (time scale: t=5 min)
Table 2 tributary data (sb=100 MVA)
Flexible resource allocation.
The economic cost and extension scale of setting the configuration flexibility resources of each node are shown in table 3. Substituting the data in the table 3 into the formula (6), solving based on the MATLAB software platform to obtain the flexible resource planning schemes of each node under the constraint of different flexible adjustment coefficients of the area, wherein the flexible resource planning schemes are shown in fig. 2, 3 and 4.
TABLE 3 economic cost and extension Scale of Flexible resources for node configuration
As can be seen from FIG. 2, in the flexible resource optimization configuration process, if takenIf=1, the up-regulation resource capacity needed to be newly built by the node 1 and the node 2 is fixed and is respectively 1.16MW and 10MW. Along with->The value of (2) increases from 1 to 1.1, and the capacity of the newly built down-regulated resource and the total cost of the newly built flexible resource in the regional power grid gradually increase.
As can be seen from FIG. 3, in the flexible resource optimization configuration process, if taken=1, then node 2 needs the newly built up-regulated resource capacity to be constant at 10MW, and node 2 needs the newly built down-regulated resource capacity to be constant at 1.03MW. Along with->The value of (1) increases from 1 to 1.1, and the capacity of the newly created up-regulated resource at node 1 gradually increases. In the whole optimization process, no new down-regulating resource is needed at the node 1. Furthermore, the->The larger the value of (c), the greater the total cost.
As can be seen from fig. 4, in the flexible resource optimization configuration process, followingAnd->With the exception that node 2 builds up the up-regulated resource to remain unchanged by 10MW, all new flexible resources are gradually increasing.
As can be seen from comparing fig. 2, 3 and 4, the newly created flexible resource capacity in fig. 4 is the union of the newly created flexible resource capacities of fig. 2 and 3. Therefore, if the optimal configuration results of the two parameters of the flexible up-regulation coefficient and the flexible down-regulation coefficient after the simultaneous change are to be calculated, the result of each parameter after the independent change can be calculated first, and then the or operation is carried out on the two results. This has guiding significance for actual planning.
In summary, the higher the requirement of the actual planning on the flexibility allowance of the regional power grid is, the higher the total cost of newly built flexible resources is, the requirement of the up-regulation or down-regulation capability of the regional power grid is properly relaxed, and the investment cost can be reduced.
And (II) power grid dispatching.
Suppose that the pre-processed IEEE 30 node system requiring normal operation of the upper power grid is externally increased within 5min through the node 8And power transmission, wherein a scheduling scheme of flexible resources is formulated according to the flexible up-regulation coefficient. For analyzing the optimization results under different working conditions, let +.>Take 5MW, 10MW, 15MW, 20MW and 25MW. The specific solving process is as follows:
step one: and respectively renumbering each node of the regional power grid and each node of the lower power grid (the last row of connecting nodes) according to the flexible up-regulation coefficients in the table 1 from large to small. The sequence numbers in brackets in fig. 1 are new node sequence numbers. Because the plurality of connecting lines between the upper and lower electric network of the pretreated system are isoelectric points, the original serial numbers of the lower electric network are 9, 10, 12, 27 andthe new sequence numbers corresponding to the nodes of (a) are all 18. The new sequence numbers are referred to below in reference to node sequence numbers.
Step two: substituting the data in table 2 into formula (8), solving by using a genetic algorithm (namely calling ga functions) based on a MATLAB software platform to obtain the power variation distribution of the local area power grid under different flexibility requirements, wherein the distribution is shown in (a) of fig. 5.
Step three: the area is covered byThe optimization result of the power grid is used as the boundary condition of the optimization of the lower power grid, namely the increased power of the node 7 in (a) in fig. 5That is, the system requires the power externally transmitted by the lower power grid +.>. Notably, the->The power change quantity of the regional power grid required by the upper power grid can be understood as the flexibility requirement of the regional power grid; likewise->The flexibility requirements of the lower level grid can also be understood.
Step four: and (3) repeating the step two, and obtaining the power variation distribution of the lower power grid as shown in (b) of fig. 5. The regional power grid flexibility requirements are working conditions of 25MW, 20MW, 15MW, 10MW and 5MW, and the corresponding lower-level power grid flexibility requirements are 11.48MW, 6.48 MW, 1.48MW, 0 and 0.
As can be seen from fig. 5 (a), as the flexibility requirement of the local regional power grid gradually increases, the nodes 9, 8, and 7 participate in flexible regulation in turn, and the node 7 does not participate in regulation until the output power variation amounts of the nodes 8 and 9 reach the maximum allowable value (i.e., the upward RC). When the flexibility requirement is 5MW and 10MW, the node 8 and the node 9 of the regional power grid participate in adjustment, so that the flexibility requirement can be met; when the flexibility requirement is 15MW, 20MW and 25MW, the nodes 7, 8 and 9 of the regional power grid need to participate in regulation simultaneously to meet the flexibility requirement.
As can be seen from fig. 5 b, when the flexibility requirement of the local area power grid is 5MW and 10MW, the generalized node (node 7) of the local area power grid does not participate in the flexible adjustment, so that the lower power grid has no flexibility requirement, and all nodes do not participate in the flexible adjustment. When the flexibility requirement of the regional power grid is increased from 15MW to 25MW, the power variation of the generalized node output of the regional power grid is gradually increased, so that the flexibility requirement of the lower power grid is gradually increased from 1.48MW to 11.48MW. At this time, the nodes 17 and 16 of the lower-level power grid participate in flexible regulation in turn, and the nodes 16 do not participate in regulation until the output power variation of the nodes 17 reaches the maximum allowable value.
In summary, along with the increase of flexibility requirements, nodes in the same power grid participate in flexible adjustment in sequence from large to small according to sequence numbers, and before the power variation of the nodes with high priority reaches the maximum allowable value, the nodes with low priority do not output the power variation.
The invention also provides an embodiment of the flexible resource allocation or scheduling system taking the flexible adjustment coefficient as an index, corresponding to the embodiment of the flexible resource allocation or scheduling method taking the flexible adjustment coefficient as an index.
A flexible resource allocation or scheduling system with flexible adjustment coefficients as indicators, comprising:
the flexible adjustment coefficient calculation module is used for obtaining parameters of the regional power grid and each node, including the net load time sequence of the regional power grid, each lower power grid and each node, injection power of each node in normal operation, upward climbing slope, downward climbing slope of a power supply contained in each node and branch data of the regional power grid, and calculating flexible adjustment coefficients of the current adjustment period of the regional power grid and each node, wherein the flexible adjustment coefficients comprise flexible up-regulation coefficients and flexible down-regulation coefficients, and the flexible up-regulation coefficients are the ratio of the flexible up-allowance of the node or the power grid to the upward fluctuation amplitude of the net load; the flexible down-regulating coefficient is the ratio of the flexible down-regulating margin of the node or the power grid to the downward fluctuation amplitude of the net load;
the resource allocation module is used for establishing a flexible resource optimization allocation model under any adjustment period and solving the flexible resource optimization allocation model by taking the minimum total capacity of newly-added resources as an objective function and taking the configured flexible adjustment coefficient of the regional power grid as a constraint condition that the newly-added resource capacity of each node is larger than a set value and the newly-added resource capacity of each node is smaller than the set value, and increasing or decreasing flexible resources at each node according to a solving result;
and/or a scheduling module, which is used for acquiring the flexibility requirement of the regional power grid in real time, each node of the regional power grid sequentially participates in flexible adjustment according to the size of the flexible adjustment coefficient, the process of participating in adjustment of the node k is set as a stage k, the flexibility requirement of the regional power grid in the stage k is set as a state variable of the stage k, the power increased by the node k is set as a decision variable and an index function of the stage k, the maximum value of the index function is set as an optimal index function, a dynamic planning model of the power grid flexible adjustment is established, the decision variable value meeting the optimal index function value is solved, the decision result of each stage is obtained, and the injection power increased or decreased by each node in the current adjustment period is scheduled according to the decision result.
The implementation process of the functions and roles of each unit in the above device is specifically shown in the implementation process of the corresponding steps in the above method, and will not be described herein again.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary or exhaustive of all embodiments. And obvious variations or modifications thereof are contemplated as falling within the scope of the present invention.
Claims (7)
1. A flexible resource allocation or scheduling method using a flexible adjustment coefficient as an index, comprising:
acquiring parameters of the regional power grid and each node, wherein the parameters comprise net load time sequence of the regional power grid, each lower power grid and each node, injection power of each node in normal operation, upward climbing slope, downward climbing slope and branch data of the regional power grid of a power supply contained in each node; calculating flexible adjustment coefficients of the current adjustment period of the regional power grid and each node, wherein the flexible adjustment coefficients comprise flexible up-adjustment coefficients and flexible down-adjustment coefficients, and the flexible up-adjustment coefficients are the ratio of the flexible up-allowance of the node or the power grid to the upward fluctuation amplitude of the net load; the flexible down-regulating coefficient is the ratio of the flexible down-regulating margin of the node or the power grid to the downward fluctuation amplitude of the net load;
taking the minimum total capacity of newly added resources as an objective function, taking the configured flexible adjustment coefficient of the regional power grid as a constraint condition that the newly added resource capacity of each node is larger than a set value, and taking the newly added resource capacity of each node as a constraint condition, establishing a flexible resource optimization configuration model under any adjustment period, solving, and adding or reducing flexible resources at each node according to a solving result;
and/or acquiring the flexibility requirement of the regional power grid in real time, sequentially participating in flexible adjustment by each node of the regional power grid according to the size of a flexible adjustment coefficient, setting the flexibility requirement of the regional power grid in the stage k as a stage k in the process of participating in adjustment by the node k as a stage k state variable, setting the increased power of the node k as a decision variable and an index function of the stage k, setting the maximum value of the index function as an optimal index function, establishing a dynamic planning model for flexible adjustment of the power grid, solving the decision variable value meeting the optimal index function value to obtain a decision result of each stage, and scheduling the increased or decreased injection power of each node in the current adjustment period according to the decision result.
2. The method of claim 1, wherein the net load up-surge amplitude of the node or grid is represented by a maximum value of a net load surge sequence and the net load down-surge amplitude of the node or grid is represented by an absolute value of a minimum value of the net load surge sequence; the payload fluctuation sequence is obtained by performing first-order differential operation on the obtained payload time sequence.
3. The method of claim 1, wherein the flexible up-scaling coefficients and flexible down-scaling coefficients are calculated by:
；
wherein:flexible up-regulation factor for the current regulation period T,/->A flexible down-regulation coefficient for the current regulation period T; />An upward ramp slope of the nth power source for the power network or node within the current regulation period T +.>The downward climbing slope of the nth power supply of the power grid or the node in the current regulation period T; />For the flexibility of the node or the network to be increased by a margin during a control period, < >>A downward margin for flexibility of a node or a power grid in one adjustment period; />For the net load up-fluctuation amplitude of the node or the network in the current regulation period T,/v>The net load of the node or grid fluctuates down in magnitude for the current regulation period T.
4. The method of claim 1, wherein the flexible resource optimization configuration model is:
；
wherein: subscript i denotes the ith node, subscript j denotes the jth lower grid,for the ith nodeFlexible up-regulation factor of current regulation period T, < >>The magnitude of the upward fluctuation of the net load for the ith node over a conditioning period T,the magnitude of the upward fluctuation of the net load for the j-th lower network in a control period T,/->Flexible down-regulation factor for the current regulation period T of the ith node, +.>For the i-th node the magnitude of the payload down-surge in a regulation period T, +.>For the net load downward fluctuation amplitude of the jth lower power grid in a regulating period T, f is the total cost of flexible resource construction, C fx Fixed cost required for flexible resource construction; />Up-regulating total capacity of resources for local regional power grid>Construction cost for up-regulating resource capacity for node i unit,/->Newly increasing up-regulated resource capacity for node i in current regulation period T,/>Flexibly up-regulating the set value of the coefficient for the local regional power grid, < >>Allowing the newly added up-regulation resource maximum capacity for the node i in the current regulation period T; />Newly increasing down-regulating total capacity of resources for local regional power grid,/->Construction cost for downregulating resource capacity for node i unit,/->Newly increasing down-regulated resource capacity for node i in current regulation period T,/>Flexibly down-regulating the set value of the coefficient for the local regional power grid, < >>Allowing a new added down-regulated resource maximum capacity for node i in the current regulation period T,/for node i>For the whole positive real number set, +.>To fluctuate the magnitude of the net load of the local area network upwards during a regulation period T,for the net load of the local area network to fluctuate downwards by an amplitude within a regulation period T.
5. The method according to claim 1, wherein the flexible adjustment dynamic planning model of the power grid is:
；
wherein: f (f) k S is the optimal index function of the regional power grid in the stage k k D is a state variable of phase k k As a set of allowed decisions for stage k,increasing power for node k; />And->Flexible down-scaling factor and flexible up-scaling factor for node k, respectively, < >>And->The net load up-fluctuation amplitude and the net load down-fluctuation amplitude of the node k are respectively, P i For the injection power of node i in normal operation, T li Is the element of the jth row and the ith column of the direct current power flow distribution factor matrix, F j,max Is the maximum allowable value of the flow of power through line j.
6. The method of claim 5, wherein the state transition equation of the grid flexible adjustment dynamic planning model is:
；
wherein: subscripts k and k+1 denote phase numbers, s k As a state variable for phase k,power up for node k, +.>For the flexibility of the regional power gridAnd (5) solving.
7. A flexible resource allocation or scheduling system using flexible adjustment coefficients as indicators, comprising:
the flexible adjustment coefficient calculation module is used for obtaining parameters of the regional power grid and each node, including the net load time sequence of the regional power grid, each lower power grid and each node, injection power of each node in normal operation, upward climbing slope, downward climbing slope of a power supply contained in each node and branch data of the regional power grid, and calculating flexible adjustment coefficients of the current adjustment period of the regional power grid and each node, wherein the flexible adjustment coefficients comprise flexible up-regulation coefficients and flexible down-regulation coefficients, and the flexible up-regulation coefficients are the ratio of the flexible up-allowance of the node or the power grid to the upward fluctuation amplitude of the net load; the flexible down-regulating coefficient is the ratio of the flexible down-regulating margin of the node or the power grid to the downward fluctuation amplitude of the net load;
the resource allocation module is used for establishing a flexible resource optimization allocation model under any adjustment period and solving the flexible resource optimization allocation model by taking the minimum total capacity of newly-added resources as an objective function and taking the configured flexible adjustment coefficient of the regional power grid as a constraint condition that the newly-added resource capacity of each node is larger than a set value and the newly-added resource capacity of each node is smaller than the set value, and increasing or decreasing flexible resources at each node according to a solving result;
and/or a scheduling module, which is used for acquiring the flexibility requirement of the regional power grid in real time, each node of the regional power grid sequentially participates in flexible adjustment according to the size of the flexible adjustment coefficient, the process of participating in adjustment of the node k is set as a stage k, the flexibility requirement of the regional power grid in the stage k is set as a state variable of the stage k, the power increased by the node k is set as a decision variable and an index function of the stage k, the maximum value of the index function is set as an optimal index function, a dynamic planning model of the power grid flexible adjustment is established, the decision variable value meeting the optimal index function value is solved, the decision result of each stage is obtained, and the injection power increased or decreased by each node in the current adjustment period is scheduled according to the decision result.
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