CN116914748A - Cross-regional power grid flexibility resource optimization scheduling method considering flexibility mutual aid - Google Patents
Cross-regional power grid flexibility resource optimization scheduling method considering flexibility mutual aid Download PDFInfo
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Abstract
The invention relates to the technical field of power system optimization scheduling, and provides a cross-regional power grid flexibility resource optimization scheduling method considering flexibility mutual economy, which comprises the steps of constructing a flexible evaluation model of a regional power grid, and calculating flexibility parameters corresponding to a conventional unit, energy storage and load reduction; constructing an optimized scheduling model by taking the comprehensive operation cost of the regional power grid as an objective function; constructing constraint conditions of the regional power grid in an optimal scheduling model; introducing fuzzy parameter description payload flexibility requirements aiming at random fluctuation of net load of a power grid; and solving the optimal scheduling model according to the constraint condition to obtain a scheduling optimization result. The invention adopts fuzzy opportunity constraint analysis of the influence of the net load uncertainty on the power balance and the flexible supply and demand balance, and improves the wind and light consumption of the system by setting the risk level of the confidence level control system; the multi-region decentralized scheduling can better promote the cross-region flow of flexible resources and improve the flexibility level of the whole power grid.
Description
Technical Field
The invention relates to the technical field of power system optimal scheduling, in particular to a cross-regional power grid flexible resource optimal scheduling method considering flexibility mutual aid.
Background
The output of new energy sources such as wind and light has larger fluctuation and intermittence, and large-scale access brings serious challenges to the operation of the system. The system multi-type flexible resource is deeply excavated and utilized, so that the operation flexibility of the power system can be improved, and the new energy consumption capability of the system is improved.
For a single-area power grid, the existing research is used for researching a scheduling mode of power system flexible supply-demand balance from the viewpoint of operation flexibility. The scheduling strategy considering the operation flexibility is mostly developed from the supply and demand characteristics of the flexibility, and the scheduling strategy is optimized by quantitatively evaluating the flexibility demand and supply capacity in the network. The above research is mainly aimed at the optimal scheduling of flexible resources in a single system, and the influence of mutual economy of flexible resources under the condition of regional interconnection is not considered.
One of the development directions of the future power system is a regional power system, and the regional power resource can be flexibly complemented and optimally allocated by the regional power system in a partitioning mode, so that the overall economy of the interconnected region is optimal. In the aspect of research on multi-region power grid collaborative scheduling, the existing research is developed aiming at the scheduling of a distributed architecture multi-region interconnection system, has positive significance for improving the operation flexibility of a new energy high-duty ratio interconnection power grid, does not consider the influence of inter-region flexibility mutual aid on new energy consumption and load limitation rejection, does not deeply excavate the flexibility mutual ability of flexible loads and energy storage resources in the multi-region power grid, and has insufficient operation form adaptability for multi-flexibility resource collaborative interaction of a future power grid.
Disclosure of Invention
The invention provides a cross-regional power grid flexible resource optimization scheduling method considering flexibility mutual economy, which solves the technical problems of low flexibility and poor economic benefit of power resources in each region caused by insufficient adaptation of development trend of coordinated operation of multi-type flexible resources of a power grid due to the fact that the influence of the flexibility resource mutual economy in a regional interconnected power system is not considered in the existing research.
In order to solve the technical problems, the invention provides a cross-regional power grid flexibility resource optimization scheduling method considering flexibility mutual aid, which comprises the following steps:
s1, constructing a flexible evaluation model of a regional power grid, and calculating flexible parameters corresponding to a conventional unit, energy storage and load reduction;
s2, constructing an optimized dispatching model by taking the comprehensive operation cost of the regional power grid as an objective function;
s3, constructing constraint conditions of the regional power grid in the optimal scheduling model;
s4, introducing fuzzy parameter description payload flexibility requirements aiming at random fluctuation of net load of the power grid;
and S5, solving the optimal scheduling model according to the constraint condition to obtain a scheduling optimization result.
In a further embodiment, the step S4 specifically includes:
s41, aiming at random fluctuation of net load of a power grid, wind power, photovoltaic and load output are used as uncertainty variables, and net load fuzzy parameters and fuzzy opportunity constraint methods are introduced to correct prediction data;
s42, solving fuzzy opportunity constraint planning, processing opportunity constraint, separating fuzzy parameters and decision variables in constraint conditions, and converting into clear equivalent class processing;
specifically, the optimization problem of the objective function and constraint condition containing the fuzzy parameters can be expressed as follows:
in the method, in the process of the invention,is a confidence level; f (x, ζ) is an objective function; g (x, ζ) is a constraint; x is an n-dimensional decision vector; ζ is a fuzzy parameter vector;
clear equivalence classes of opportunity constraints are;
in the method, in the process of the invention,、/>for the assumed two functions +.>Is a function->Part of (2)>~/>(k=1, 2, …, t, t e R) is a membership parameter.
In a further embodiment, the conversion of the blurring parameter is specifically: when a trapezoidal membership function is adopted in the aspect of membership of the fuzzy parameter, the fuzzy parameter can be expressed as a quadruple:
the first membership parameter to the fourth membership parameter corresponding to the trapezoid membership function are respectively +.>For the payload prediction value, +.>Is a proportionality coefficient.
In a further embodiment, the conversion of the decision variable comprises:
the flexibility probability balance clear equivalence classes after conversion are:
、/>the system is capable of adjusting flexibility up and flexibility down at t time.
Relaxing power balance constraints to a certain confidence levelBalance of power constraint under the condition that the possibility of establishment of the balance constraint condition is not less than +.>An uncertainty factor set is constructed in this way as follows:
the power balance clear equivalence class after conversion is:
in the method, in the process of the invention,for the actual output of the conventional unit g at time t, < >>For the actual power of the energy store e at time t, < >>In order to be able to cut down the amount of load d cut down at time t, < >>And the power value on the interconnecting line between the m and n areas at the moment t.
In a further embodiment, the step S1 includes the steps of:
s11, respectively calculating the flexible supply capacity of a conventional unit, energy storage and load reduction, and integrating the flexible supply capacity of the system of the regional power grid;
s12, calculating the system flexibility requirement of the regional power grid according to the fluctuation of the net load and the prediction error of the net load;
s13, calculating flexibility probability balance characteristics according to the system flexibility requirements and the system flexibility supply capacity;
s14, calculating the flexibility mutual service cost among the areas according to the flexibility supply capacity of the system;
s15, creating a flexible mutual-aid capacity constraint model of the interconnecting lines according to the power adjustment rate, transmission power and maximum transmission capacity of the interconnecting lines between the areas;
the regional power grid flexibility parameters comprise system flexibility supply capacity, system flexibility requirements, flexibility probability balance characteristics, flexibility mutual-aid service cost and a flexibility mutual-aid capacity constraint model.
In a further embodiment, in said step S15, the constraint conditions of the flexible mutual capacity constraint model include:
the sum of the flexibility mutual-aid values in different directions on any connecting line at the same moment is 0;
the up-regulation flexibility mutual-aid value between the two regional power grids is not greater than the maximum up-regulation speed and the residual adjustable capacity of the transmission power of the connecting line;
the down-regulation flexibility mutual-aid value between the two regional power grids should not be larger than the maximum down-regulation rate and the residual adjustable capacity of the transmission power of the connecting line.
In a further embodiment, the flexible supply capacity of the conventional unit is,
the flexibility of the stored energy supply capacity is,
the load-reducible flexible supply capability is,
the system flexibility provisioning capability may then represent,
in the method, in the process of the invention,//>、/>//>、/>//>the up-regulation/down-regulation flexibility is provided for the conventional unit g, the energy storage e and the load d at the time t; />、/>Maximum upward and downward climbing capacity of the conventional unit g;、/>the maximum and minimum technical output of the conventional unit g are respectively; />The actual output of the conventional unit g at the time t is obtained;、/>、/>the rated power, the maximum capacity and the minimum capacity of the energy storage e are respectively; />、/>The actual power and capacity of the energy storage e at the time t are respectively; />、/>Charging and discharging power of the energy storage e respectively; />The maximum reduction amount for reducing the load d; />The reduction amount of the load d at the time t can be reduced; />、/>The system is up-regulating flexibility and down-regulating flexibility at the time of the system t.
In a further embodiment, the system flexibility requirement is expressed as,
the flexible probability balance characteristic is expressed as,
in the method, in the process of the invention,、/>upward and downward for system t momentAn activity requirement; />、/>The net load power predicted value and the predicted error at the moment t;
the flexibility mutual service cost is expressed as,
in the method, in the process of the invention,、/>the up-regulation and down-regulation flexibility mutual aid level obtained or sent by the region m through the connecting line at the moment t is positive when the region m obtains flexibility support of other regions, and is negative otherwise; />、/>The service cost coefficient is mutually matched for the up-regulation and the down-regulation flexibility of the region m; when region m gets other region flexibility support, < > it>The method is characterized by flexibility and mutual service cost; when region m provides flexibility support for other regions, < >>Manifesting as flexible mutual aid service benefits.
In a further embodiment, in the step S2, the objective function is:
wherein T is the total scheduling period;the method is characterized in that the method is respectively the power generation and start-stop cost, the energy storage scheduling cost, the load scheduling cost, the wind-discarding, light-discarding and load-shedding punishment cost, the flexibility mutual service cost and the carbon emission cost of a conventional unit in the t-moment region m.
In a further embodiment, in said step S3; the constraint conditions comprise thermal power generating unit operation constraint conditions, energy storage operation constraint conditions, load reduction operation constraint conditions, power balance constraint conditions, line power flow safety constraint conditions and regional flexibility constraint conditions;
wherein the zone flexibility constraint condition is that the zone maximum flexibility outgoing capability should not be greater than the flexibility level of itself;
the power balance constraint is expressed as,
in the method, in the process of the invention,for the actual output of the conventional unit g at time t, < >>For the actual power of the energy store e at time t, < >>In order to be able to cut down the amount of load d cut down at time t, < >>And the power value on the interconnecting line between the m and n areas at the moment t.
The beneficial effects of the invention are as follows:
1) And the influence of the uncertainty of the net load on the power balance and the flexible supply and demand balance is analyzed by adopting fuzzy opportunity constraint, and the wind-solar energy consumption of the system is improved by setting the risk level of the confidence level control system.
2) The influence of the ladder-type carbon transaction cost and the flexibility mutual-aid cost on the dispatching operation of the interconnected regional power grid is comprehensively considered, and the operation result shows that the introduced flexibility mutual-aid dispatching can effectively reduce the wind-abandoning light-abandoning load punishment cost of the system and improve the comprehensive benefit of the system operation.
3) With the increase of coordination scheduling areas and flexible resources, multi-area decentralized scheduling can better promote the cross-area flow of flexible resources and improve the flexibility level of the whole power grid.
Drawings
FIG. 1 is a workflow diagram of a cross-regional power grid flexible resource optimization scheduling method considering flexibility mutual aid provided by an embodiment of the invention;
FIG. 2 is a wind-solar load prediction value of a regional power grid provided by an embodiment of the invention;
FIG. 3 is a graph of zone A flexibility capacity versus mutual aid level for scheme 1 provided by an embodiment of the present invention;
FIG. 4 is a graph of zone A flexibility capacity versus mutual aid level for scheme 2 provided by an embodiment of the present invention;
fig. 5 shows the flexibility capacity and the mutual aid level of the area a in the scheme 3 according to the embodiment of the present invention.
Detailed Description
The following examples are given for the purpose of illustration only and are not to be construed as limiting the invention, including the drawings for reference and description only, and are not to be construed as limiting the scope of the invention as many variations thereof are possible without departing from the spirit and scope of the invention.
The method for optimizing and scheduling the flexibility resources of the inter-regional power grid, which is provided by the embodiment of the invention and considers flexibility, as shown in fig. 1, comprises the following steps S1-S5:
s1, constructing a flexible evaluation model of a regional power grid, and calculating flexible parameters corresponding to a conventional unit, energy storage and load reduction, wherein the flexible evaluation model comprises the following steps of S11-S15:
s11, respectively calculating the flexible supply capacity of a conventional unit, energy storage and load reduction, and integrating the flexible supply capacity of the system of the regional power grid;
in this embodiment, flexibility supply capability is divided into up-flexibility and down-flexibility according to directions, and is classified according to different flexibility resource characteristics, and the flexibility supply capability of the conventional unit is that,
the flexibility of the stored energy supply capacity is,
the load-reducible flexible supply capability is,
the system flexibility provisioning capability may then represent,
in the method, in the process of the invention,//>、/>//>、/>//>respectively a conventional unit g, an energy storage e and a load d which can be reduced at the moment tThe provided up/down flexibility; />、/>Maximum upward and downward climbing capacity of the conventional unit g;、/>the maximum and minimum technical output of the conventional unit g are respectively; />The actual output of the conventional unit g at the time t is obtained;、/>、/>the rated power, the maximum capacity and the minimum capacity of the energy storage e are respectively; />、/>The actual power and capacity of the energy storage e at the time t are respectively; />、/>Charging and discharging power of the energy storage e respectively; />The maximum reduction amount for reducing the load d; />The reduction amount of the load d at the time t can be reduced; />、/>The system is up-regulating flexibility and down-regulating flexibility at the time of the system t.
S12, calculating the system flexibility requirement of the regional power grid according to the fluctuation of the net load and the prediction error of the net load;
in this embodiment, the system flexibility requirement is generally preset based on fluctuations in the payload and its prediction error, and is expressed as,
s13, calculating flexibility probability balance characteristics according to the system flexibility requirements and the system flexibility supply capacity;
in this embodiment, the system flexibility has a probabilistic nature, expressed as a flexibility probabilistic balance,
in the method, in the process of the invention,、/>the upward and downward flexibility requirement is met for the system t moment; />、/>The predicted value and the predicted error of the net load power at the time t are obtained.
S14, calculating the flexibility mutual service cost among the areas according to the flexibility supply capacity of the system;
in this embodiment, in view of the difference in interest subjects of each regional power grid, when obtaining/providing flexible assistance, each regional power grid should bear corresponding costs or obtain corresponding benefits, which are specifically expressed as: if the region m obtains the flexibility assistance of the region n, the comprehensive benefit of the region m is expressed in that the flexibility level of the regional power grid is improved, and the region m bears the cost of the flexibility mutual-aid service according to the mutual-aid level; at the same time, region n receives the benefit of this flexible mutual aid service. The inter-regional interconnecting line is utilized to carry out flexible coordination and mutual adjustment, and is divided into an up-regulation flexibility mutual adjustment and a down-regulation flexibility mutual adjustment according to the regulation direction, and the service cost of the regional m flexibility mutual adjustment at the moment t is expressed as,
in the method, in the process of the invention,、/>the up-regulation and down-regulation flexibility mutual aid level obtained or sent by the region m through the connecting line at the moment t is positive when the region m obtains flexibility support of other regions, and is negative otherwise; />、/>Flexible up-and down-regulation for region mAnd the sex mutually benefits the service cost coefficient.
When region m obtains additional region flexibility support,the method is characterized by flexibility and mutual service cost; when region m provides flexibility support for other regions, < >>Manifesting as flexible mutual aid service benefits.
S15, creating a flexible mutual-aid capacity constraint model of the interconnecting lines according to the power adjustment rate, transmission power and maximum transmission capacity of the interconnecting lines between the areas;
the tie line is a carrier for regional power grid flexibility resource mutual aid, and the running state and the self characteristic point of the tie line limit the flexibility mutual aid level between regions. At the same time, the sum of the flexibility mutual aid values in different directions on any connecting line is 0. The mutual aid capability of the flexibility between the areas is limited by the adjustment rate of the power of the connecting line, the transmission power and the maximum transmission capability, and the mutual aid value of the up-adjustment flexibility and the down-adjustment flexibility between the two areas is not greater than the maximum up-adjustment rate and the down-adjustment rate of the transmission power of the connecting line and the residual adjustable capacity.
The constraint conditions of the flexible mutual-aid capacity constraint model comprise:
the sum of the flexibility mutual-aid values in different directions on any connecting line at the same moment is 0;
the up-regulation flexibility mutual-aid value between the two regional power grids is not greater than the maximum up-regulation speed and the residual adjustable capacity of the transmission power of the connecting line;
the down-regulation flexibility mutual-aid value between the two regional power grids should not be larger than the maximum down-regulation rate and the residual adjustable capacity of the transmission power of the connecting line.
In this embodiment, the flexibility parameters of the regional power grid include a system flexibility supply capability, a system flexibility requirement, a flexibility probability balance characteristic, a flexibility mutual-aid service cost, and a flexibility mutual-aid capability constraint model.
That is, the flexible mutual ability constraint model of the tie line is as follows:
wherein:the power value on the interconnecting line between the region m and the region n at the moment t is positive when the power direction is that the region m flows to the region n, and is negative otherwise; />、/>The maximum adjustment speed and the maximum power value of the transmission power on the regional interconnecting line are obtained;、/>and (5) up-regulating the flexibility mutual aid value between the regions m and n at the moment t and down-regulating the flexibility mutual aid value.
S2, constructing an optimized dispatching model by taking the comprehensive operation cost of the regional power grid as an objective function;
consider three cross-region interconnected power grids containing flexible resources such as conventional units, energy storage, load reduction and the like under high-proportion new energy access. The three regional power grids are interconnected through a connecting line, and the regional A is assumed to contain a large amount of renewable energy resources such as wind power, photovoltaic and the like, and only contains a conventional unit as a flexible resource, so that the flexible requirement is greater than supply; the areas B and C contain flexible resources such as energy storage, load reduction and the like, large-scale renewable energy resources are not needed, and flexible supply is larger than the demand. In this mode, power and flexibility transfer is achieved by the cross-zone interconnect, maintaining power balance and flexibility balance for each system.
In order to ensure information security and data privacy of the power systems in all regions in the interconnected power system, benefits of different operation subjects and high autonomy are considered, and a regional dispersion coordination mechanism is adopted to analyze the flexible coupling relation among regions. And each regional power grid is used as a dispatching main body and is responsible for scheduling flexible resources in the regional, and the flexibility mutual-aid level among the regions is optimized by coordinating the unit combination mode of each region and the dispatching strategy of energy storage and load reduction. And an ADMM algorithm is adopted to decompose the optimization target with the minimum running cost of the large power grid in the traditional scheduling model into the minimum running cost of the power grid of each region, so that the decentralized coordinated scheduling of multi-region and multi-type flexible resources is realized. Taking the region m as an example, the optimization objective of the local sub-problem is that the sum of the generating and starting and stopping costs, the energy storage and load dispatching cost which can be reduced, the wind discarding, light discarding and load shedding penalty cost, the flexibility mutual-aid service cost and the carbon emission cost of the unit in the region m is minimum, and the formula (16) is an objective function of the local sub-problem:
in this embodiment, the objective function is:
wherein T is the total scheduling period;respectively at time tThe conventional unit in the region m has the advantages of generating and starting and stopping cost, energy storage scheduling cost, load scheduling cost reduction, wind and light discarding load shedding penalty cost, flexibility mutual service cost and carbon emission cost.
1) Conventional unit power generation and start-stop costs
The conventional unit power generation and start-stop costs can be expressed as:
wherein:the number of the conventional units is the area m; />The running state of the conventional unit g at the moment t in the region m is 1 when the unit runs, and otherwise, the running state is 0; />、/>、/>The fuel cost coefficient of the unit; />The power of the conventional unit g at the time t is the power of the region m; />、/>The cost coefficient is the starting and stopping cost coefficient of the unit; />、/>For the starting and stopping state of the conventional unit g in the region mAnd the variable is 1 when the unit is started, and otherwise, the variable is 0.
2) Energy storage scheduling cost
The scheduling cost of stored energy can be expressed as:
wherein:is the total energy storage amount; />The one-time purchase cost for energy storage; />The service life of energy storage is prolonged; />Is the rated capacity of the stored energy.
3) Load scheduling cost is reduced
The reducible load scheduling cost can be expressed as:
wherein:the total load can be reduced; />The scheduling cost of the load can be reduced per unit capacity.
4) Wind-discarding, light-discarding and load-shedding penalty
Insufficient upward flexibility can lead to load loss risk, insufficient downward flexibility can lead to wind abandon light risk, wind abandon light punishment coefficient and load shedding punishment coefficient are introduced, and objective function is brought into in the form of wind abandon light punishment cost and load shedding punishment cost:
wherein:penalty cost for wind and light discarding and load shedding; />、/>The unit wind and light abandoning punishment coefficient and the load shedding punishment coefficient are respectively adopted; />、/>、/>The system is respectively used for discarding wind, light and load.
5) Carbon emission cost
The carbon trade market promotes energy conservation and emission reduction by a market mechanism through trading greenhouse gas emission rights, and controls and reduces greenhouse gas emission pollution. Calculating the carbon emission quota of the system by adopting a datum line data method:
wherein:total carbon emission allowance for zone m; />Is the unit electricity discharge quota; />And (3) generating power for all the generator sets in the region m at the time t.
Wind power and photovoltaic are used as clean energy sources, carbon dioxide is not generated in the power generation process, so that the carbon emission of the region m mainly comes from a conventional unit, and the calculation formula of the carbon emission of the region m is as follows:
wherein:total carbon emissions for zone m; />Is the carbon emission intensity of the conventional unit g.
To further limit carbon emissions, a stepped carbon trade mechanism is employed. The stepped carbon trade mechanism prescribes the unit carbon trade price of the trade interval and different trade intervals of the system participating in the carbon trade. As carbon emissions increase, price per carbon tradeStep-by-step rising with mu as the amplitude. When->When (I)>Negative values, indicating that the actual carbon emissions of the system is less than the quota of the system, may trade the excess carbon emissions quota in the carbon trade market at the initial carbon emission price, resulting in a carbon return for the system.
The stepped carbon emission cost is as follows:
wherein: lambda is the carbon trade price; τ is the carbon emission interval length; mu is the increment of the price of carbon trade per 1 step of the rise of the carbon emission.
S3, constructing constraint conditions of the regional power grid in the optimal scheduling model;
in this embodiment; the constraint conditions comprise thermal power generating unit operation constraint conditions, energy storage operation constraint conditions, load reduction operation constraint conditions, power balance constraint conditions, line power flow safety constraint conditions and regional flexibility constraint conditions;
1) Thermal power generating unit operation constraint
Wherein:a state variable in the running or off-time of the unit g in the region m, wherein k is the running or off-time; />、/>And (5) the minimum starting and stopping time of the unit g in the region m.
2) Energy storage operation constraint
Wherein:、/>the charging and discharging power of the energy storage e in the t moment region m; />//>The charging/discharging state of the energy storage e in the t moment region m is 1 when the energy storage is charged/discharged, otherwise, the charging/discharging state is 0.
3) Load shedding operation constraint
The load-reducible operating constraints are:
/>
4) Power balance constraint
In order to ensure the safety of the system operation, the power balance constraint needs to be satisfied:
the power balance constraint is expressed as,
in the method, in the process of the invention,for the actual output of the conventional unit g at time t, < >>For the actual power of the energy store e at time t, < >>In order to be able to cut down the amount of load d cut down at time t, < >>And the power value on the interconnecting line between the m and n areas at the moment t.
5) Line tide safety constraint
Wherein:、/>、/>、/>、/>、/>、/>the power flow transfer factors of the unit g, the energy storage e, the wind power w, the photovoltaic s, the connecting line n, the load l and the load d in the region m are respectively reduced; />Is the power transfer limit for line b for region m.
6) Regional flexibility constraints
The maximum flexibility delivery capability of the area should not be greater than the flexibility level of the area itself, and the area flexibility constraint is:
wherein:、/>the up-regulation flexible capacity and the down-regulation flexible capacity of the region m are respectively; />、And (3) taking the up-regulation flexibility capacity and the down-regulation flexibility capacity of the link flexibility mutual-aid level into account for the region m.
S4, introducing fuzzy parameter description payload flexibility requirements aiming at random fluctuation of net loads of a power grid, wherein the steps comprise S41-S42:
s41, aiming at random fluctuation of net load of a power grid, wind power, photovoltaic and load output are used as uncertainty variables, and net load fuzzy parameters and fuzzy opportunity constraint methods are introduced to correct prediction data;
s42, solving fuzzy opportunity constraint planning, processing opportunity constraint, separating fuzzy parameters and decision variables in constraint conditions, and converting into clear equivalent class processing;
specifically, the optimization problem of the objective function and constraint condition containing the fuzzy parameters can be expressed as follows:
in the method, in the process of the invention,is a confidence level; f (x, ζ) is an objective function; g (x, ζ) is a constraint; x is an n-dimensional decision vector; ζ is a fuzzy parameter vector;
clear equivalence classes of opportunity constraints are;
in the method, in the process of the invention,、/>for the assumed two functions +.>Is a function->Part of (2)>~/>(k=1, 2, …, t, t e R) is a membership parameter.
In a further embodiment, the conversion of the blurring parameter is specifically: when a trapezoidal membership function is adopted in the aspect of membership of the fuzzy parameter, the fuzzy parameter can be expressed as a quadruple:
the first membership parameter to the fourth membership parameter corresponding to the trapezoid membership function are respectively +.>For the payload prediction value, +.>Is a proportionality coefficient.
In a further embodiment, the conversion of the decision variable comprises:
the flexibility probability balance clear equivalence classes after conversion are:
、/>flexible up-regulation for system at t momentSex, flexibility of down-regulation.
Relaxing power balance constraints to a certain confidence levelBalance of power constraint under the condition that the possibility of establishment of the balance constraint condition is not less than +.>An uncertainty factor set is constructed in this way as follows:
the power balance clear equivalence class after conversion is:
in the method, in the process of the invention,for the actual output of the conventional unit g at time t, < >>For the actual power of the energy store e at time t, < >>In order to be able to cut down the amount of load d cut down at time t, < >>And the power value on the interconnecting line between the m and n areas at the moment t.
And S5, solving the optimal scheduling model according to the constraint condition to obtain a scheduling optimization result.
In this embodiment, an example is established using three interconnected IEEE-39 node systems, which are implemented as follows:
defined as region a, region B and region C, respectively. Each area consists of 10 conventional units and 46 power transmission lines, the reactance of the lines is set to be 0.3pu, and the maximum allowable power flow of each line is 200MW. The region A is connected with a 2150MW conventional unit, a 29 node is connected with 800MW maximum wind power, and a 30 node is connected with 500MW photovoltaic; the region B is connected with a 2150MW conventional unit, and 28 nodes are connected with energy storage with maximum charge and discharge power of 100 MW; region C has access to 2150MW of conventional crew and a total of 200MW of load shedding at 21, 23, 24, 25, 26, 27 nodes. The three-region wind power, photovoltaic and load predicted values are shown in figure 2. The converter stations of the area tie are at node 7 of area a, and at nodes 27 of area B and area C, respectively, and the tie operating parameters are shown in table 1. Setting a carbon trade price lambda=50 yuan/t; carbon emission interval length τ=100deg.C; the increase in carbon trade price is μ=25%.
Table 1 (tie line operating parameters)
To analyze the effectiveness of the multi-region flexible resource coordination mutual-aid scheduling mode, five scheduling schemes are set: the scheme 1 is a scheduling scheme provided by the invention, and the power exchange and the flexibility coordination and mutual aid are carried out among the three areas; in the scheme 2, only the flexibility coordination of the area A and the area B is considered, and the other area C only participates in power exchange and does not participate in the system flexibility coordination; in the scheme 3, only the flexibility coordination of the area A and the area C is considered, and the other area B only participates in power exchange and does not participate in the system flexibility coordination; scheme 4 is a traditional day-ahead scheduling mode, only the power exchange among areas is considered, and the flexibility mutual compensation among areas is not considered; scheme 5 is a scheduling scheme that reserves a portion of the rotational spare capacity in the conventional day-ahead scheduling mode.
First, table 2 shows the results of the system wind and light rejection costs, load shedding costs, carbon emission costs, and total running costs under five scheduling schemes, as follows:
table 2 (comparison of costs for five scheduling schemes)
By comparison, when scheduling is performed by adopting the scheme 4, a large risk of wind and light abandoning and load shedding is generated. After introducing the inter-area flexibility, comparing the scheme 3 with the scheme 4, the wind-discarding and light-discarding cost of the scheme 3 is reduced by 61.8%, and the load-shedding cost is reduced by 44.0%. Compared with the scheme 2 and the scheme 4, the wind and light discarding cost of the scheme 2 is reduced by 62.3%, and the load shedding cost is reduced by 46.4%. When multiple areas participate in flexibility mutually, compared with the scheme 1 and the scheme 2, the areas capable of coordinating scheduling flexibility are increased, the risk cost of insufficient flexibility is reduced by 59.0% compared with the scheme 2, and the total cost is reduced by 2.23 ten thousand yuan. Therefore, after the flexible mutual-aid of the areas is introduced, compared with the traditional scheduling mode (scheme 4), the system can promote the absorption of new energy sources such as wind and light, reduce the load shedding of the system, improve the running reliability of the system, reduce the running cost of the system and improve the running economy of the system.
Comparing schemes 1 and 5, the cost of wind and light curtailment load penalty, carbon emission cost and total cost are all increased when reserving rotational reserve capacity in the system to reduce the impact of payload volatility. Wherein, the unit operation cost is increased by 7.6%, the carbon emission cost is increased by 16.5%, and the total cost is increased by 3%. When the rotation reserve capacity is reserved, the net load fluctuation is preferentially satisfied by the rotation reserve of the conventional unit, the flexible resource call of energy storage, load reduction and the like is reduced, and the generated carbon dioxide emission is increased. Therefore, the scheduling method with mutual economy of flexibility is adopted, compared with the method for reserving the rotary spare capacity, the system operation cost can be effectively reduced, the carbon emission is reduced, and the development concepts of energy conservation, emission reduction, low carbon and environmental protection in the electric power industry in China are met.
Second, table 3 shows the results of unit operation cost, waste wind, waste light, waste load cost, carbon emission cost, flexibility mutual service cost and total cost of the area a under the four scheduling schemes, as follows:
table 3 (comparison of costs for the four scheduling schemes for region A)
As can be seen from table 3, the ratio of the flexible mutual service costs to the total cost in the schemes 1 to 4 is 39.61%, 19.53%, 19.21%, 0, respectively, and the flexible mutual service costs of the area a increase with the increase of the interconnection area, but the total operation cost decreases. Compared with the scheme 1 and the scheme 2, the flexibility mutual-aid service cost of the scheme 1 is increased by 98.77%, the unit operation cost is reduced by 20.79%, the waste wind waste light cutting load cost is reduced by 59.01%, the carbon emission income is increased by 96.33%, and the total cost is reduced by 1.99%, because after the flexibility mutual-aid area is increased, the degree of unbalance of flexibility supply and demand of the area A is reduced, the waste wind waste light cutting load is reduced, the output of a conventional unit is reduced, and the total cost is reduced. In addition, because new energy is relatively high, zone a may benefit from trading excess carbon emission credits.
Third, fig. 3 to 5 show the flexibility capacity and flexibility mutual aid level of the area a under each scheme.
As shown in fig. 3, in the scheduling mode considering the regional flexibility mutual assistance, each period has higher flexibility adjustment capability, and in the periods of 10:00-15:00 and 19:00-21:00 where the up-adjustment flexibility is relatively short, the out-of-region up-adjustment flexibility support accounts for 72.77% and 74.20% of the up-adjustment flexibility capacity at this time, respectively. Tables 4 to 5 are the comparison of the special period zone a flexibility capacities under three scheduling schemes, as follows:
table 4 (Special period zone A up flexibility capacity comparison under three scheduling schemes)
TABLE 5 (Special period zone A Down flexibility Capacity comparison under three scheduling schemes)
According to the comparative analysis of fig. 2, 3, 4 and 5, when the area of the coordinated scheduling increases, the level of regional flexibility mutual aid increases, which indicates that the superiority of scheme 1 is gradually amplified as the area participating in mutual aid increases, and the adaptability to the future regional coordinated operation of the multi-region power grid is stronger.
The fuzzy opportunity constraint model adopted by the invention is not limited to absolute power equality relation, but a certain confidence coefficient is set, so that the system can meet the power balance and flexibility balance relation in the scheduling process under the confidence level, and the expectations of a scheduling decision maker are achieved. The confidence level has an important effect on the system safety and economy, taking scheme 1 as an example, when the system confidence level alpha is changed, the system scheduling result is shown in table 6, and the following table is shown:
table 6 (comparison of different confidence probability schedule results)
From Table 6, the higher the confidence level, the lower the wind curtailment rate, the greater the system flexibility requirement and the greater the overall cost. The flexibility requirement of the system is time sequence climbing power of the net load of the system, and the adverse effects of new energy and load fluctuation and uncertainty can be reduced by meeting the flexibility requirement of the system. The system flexibility demand estimation level is closely related to the confidence level, the lower the confidence level is, the smaller the system flexibility demand estimation is, the poor capability of coping with new energy fluctuation can be caused, the wind and light abandoning is increased, and the system faces higher operation risk. However, as confidence levels increase, the overall operating costs increase as the system safety increases and the system economics decrease. Therefore, in the scheduling process, the safety and the economy are comprehensively considered according to the actual requirement of the system, and the proper confidence level is selected.
The beneficial effects of the invention are as follows:
1) And the influence of the uncertainty of the net load on the power balance and the flexible supply and demand balance is analyzed by adopting fuzzy opportunity constraint, and the wind-solar energy consumption of the system is improved by setting the risk level of the confidence level control system.
2) The influence of the ladder-type carbon transaction cost and the flexibility mutual-aid cost on the dispatching operation of the interconnected regional power grid is comprehensively considered, and the operation result shows that the introduced flexibility mutual-aid dispatching can effectively reduce the wind-abandoning light-abandoning load punishment cost of the system and improve the comprehensive benefit of the system operation.
3) With the increase of coordination scheduling areas and flexible resources, multi-area decentralized scheduling can better promote the cross-area flow of flexible resources and improve the flexibility level of the whole power grid.
The above examples are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above examples, and any other changes, modifications, substitutions, combinations, and simplifications that do not depart from the spirit and principle of the present invention should be made in the equivalent manner, and the embodiments are included in the protection scope of the present invention.
Claims (10)
1. A cross-regional power grid flexibility resource optimization scheduling method considering flexibility mutual aid is characterized by comprising the following steps:
s1, constructing a flexible evaluation model of a regional power grid, and calculating flexible parameters corresponding to a conventional unit, energy storage and load reduction;
s2, constructing an optimized dispatching model by taking the comprehensive operation cost of the regional power grid as an objective function;
s3, constructing constraint conditions of the regional power grid in the optimal scheduling model;
s4, introducing fuzzy parameter description payload flexibility requirements aiming at random fluctuation of net load of the power grid;
and S5, solving the optimal scheduling model according to the constraint condition to obtain a scheduling optimization result.
2. The method for optimizing and scheduling the flexible resources of the inter-regional power grid according to claim 1, wherein the step S4 is specifically:
s41, aiming at random fluctuation of net load of a power grid, wind power, photovoltaic and load output are used as uncertainty variables, and net load fuzzy parameters and fuzzy opportunity constraint methods are introduced to correct prediction data;
s42, solving fuzzy opportunity constraint planning, processing opportunity constraint, separating fuzzy parameters and decision variables in constraint conditions, and converting into clear equivalent class processing;
specifically, the optimization problem of the objective function and constraint condition containing the fuzzy parameters can be expressed as follows:
in the method, in the process of the invention,is a confidence level; f (x, ζ) is an objective function; g (x, ζ) is a constraint; x is an n-dimensional decision vector; ζ is a fuzzy parameter vector;
clear equivalence classes of opportunity constraints are;
in the method, in the process of the invention,、/>for the assumed two functions +.>Is a function->Part of (2)>~/>(k=1, 2, …, t, t e R) is a membership parameter.
3. The method for optimizing and scheduling the flexibility resources of the inter-flexible power grid in a cross-region manner according to claim 2, wherein the method is characterized in that: when a trapezoidal membership function is adopted in the aspect of membership of the fuzzy parameter, the fuzzy parameter can be expressed as a quadruple:
the first membership parameter to the fourth membership parameter corresponding to the trapezoid membership function are respectively +.>For the payload prediction value, +.>Is a proportionality coefficient.
4. A cross-regional power grid flexible resource optimization scheduling method considering flexibility mutual aid as claimed in claim 3, wherein the conversion of the decision variables comprises:
the flexibility probability balance clear equivalence classes after conversion are:
、/>the system is up-regulating flexibility and down-regulating flexibility at t time;
relaxing power balance constraint to a certain positionLevel of creditBalance of power constraint under the condition that the possibility of establishment of the balance constraint condition is not less than +.>An uncertainty factor set is constructed in this way as follows:
the power balance clear equivalence class after conversion is:
in the method, in the process of the invention,for the actual output of the conventional unit g at time t, < >>For the actual power of the energy store e at time t, < >>In order to be able to cut down the amount of load d cut down at time t, < >>And the power value on the interconnecting line between the m and n areas at the moment t.
5. The method for optimizing and scheduling the flexible resources of the inter-flexible power grid according to claim 4, wherein the step S1 comprises the steps of:
s11, respectively calculating the flexible supply capacity of a conventional unit, energy storage and load reduction, and integrating the flexible supply capacity of the system of the regional power grid;
s12, calculating the system flexibility requirement of the regional power grid according to the fluctuation of the net load and the prediction error of the net load;
s13, calculating flexibility probability balance characteristics according to the system flexibility requirements and the system flexibility supply capacity;
s14, calculating the flexibility mutual service cost among the areas according to the flexibility supply capacity of the system;
s15, creating a flexible mutual-aid capacity constraint model of the interconnecting lines according to the power adjustment rate, transmission power and maximum transmission capacity of the interconnecting lines between the areas;
the regional power grid flexibility parameters comprise system flexibility supply capacity, system flexibility requirements, flexibility probability balance characteristics, flexibility mutual-aid service cost and a flexibility mutual-aid capacity constraint model.
6. The method for optimizing and scheduling flexible resources of a cross-regional power grid in consideration of flexibility mutual aid according to claim 5, wherein in the step S15, constraint conditions of the flexible mutual aid capability constraint model include:
the sum of the flexibility mutual-aid values in different directions on any connecting line at the same moment is 0;
the up-regulation flexibility mutual-aid value between the two regional power grids is not greater than the maximum up-regulation speed and the residual adjustable capacity of the transmission power of the connecting line;
the down-regulation flexibility mutual-aid value between the two regional power grids should not be larger than the maximum down-regulation rate and the residual adjustable capacity of the transmission power of the connecting line.
7. The method for optimizing and scheduling the flexibility resources of the inter-flexible power grid in a cross-region manner according to claim 5, wherein the method is characterized in that:
the flexibility supply capacity of the conventional unit is that,
the flexibility of the stored energy supply capacity is,
the load-reducible flexible supply capability is,
the system flexibility provisioning capability may then represent,
in the method, in the process of the invention,//>、/>//>、/>//>the up-regulation/down-regulation flexibility is provided for the conventional unit g, the energy storage e and the load d at the time t; />、/>Respectively of conventional units gMaximum uphill and downhill climbing capability; />、The maximum and minimum technical output of the conventional unit g are respectively; />The actual output of the conventional unit g at the time t is obtained; />、、/>The rated power, the maximum capacity and the minimum capacity of the energy storage e are respectively; />、/>The actual power and capacity of the energy storage e at the time t are respectively; />、/>Charging and discharging power of the energy storage e respectively; />The maximum reduction amount for reducing the load d; />The reduction amount of the load d at the time t can be reduced; />、/>The system is up-regulating flexibility and down-regulating flexibility at the time of the system t.
8. The method for optimizing and scheduling the flexibility resources of the inter-flexible power grid in a cross-region manner according to claim 5, wherein the method is characterized in that:
the system flexibility requirement is expressed as a function of,
the flexible probability balance characteristic is expressed as,
in the method, in the process of the invention,、/>the upward and downward flexibility requirement is met for the system t moment; />、/>Is t time is cleanLoad power predictive value, predictive error;
the flexibility mutual service cost of the region m at time t is expressed as,
in the method, in the process of the invention,、/>the up-regulation and down-regulation flexibility mutual aid level obtained or sent by the region m through the connecting line at the moment t is positive when the region m obtains flexibility support of other regions, and is negative otherwise; />、/>The service cost coefficient is mutually matched for the up-regulation and the down-regulation flexibility of the region m; when region m gets other region flexibility support, < > it>The method is characterized by flexibility and mutual service cost; when region m provides flexibility support for other regions, < >>Manifesting as flexible mutual aid service benefits.
9. The method for optimizing and scheduling flexible resources of a cross-regional power grid in consideration of flexibility as set forth in claim 1, wherein in the step S2, the objective function is:
wherein T is the total scheduling period;the method is characterized in that the method is respectively the power generation and start-stop cost, the energy storage scheduling cost, the load scheduling cost, the wind-discarding, light-discarding and load-shedding punishment cost, the flexibility mutual service cost and the carbon emission cost of a conventional unit in the t-moment region m.
10. A cross-regional power grid flexible resource optimization scheduling method considering flexible mutual aid as claimed in claim 4, wherein in step S3; the constraint conditions comprise thermal power generating unit operation constraint conditions, energy storage operation constraint conditions, load reduction operation constraint conditions, power balance constraint conditions, line power flow safety constraint conditions and regional flexibility constraint conditions;
wherein the zone flexibility constraint condition is that the zone maximum flexibility outgoing capability should not be greater than the flexibility level of itself;
the power balance constraint is expressed as,
in the method, in the process of the invention,for the actual output of the conventional unit g at time t, < >>For the actual power of the energy store e at time t, < >>In order to be able to cut down the amount of load d cut down at time t, < >>And the power value on the interconnecting line between the m and n areas at the moment t.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117439090A (en) * | 2023-12-19 | 2024-01-23 | 浙江大学 | Flexible resource allocation or scheduling method taking flexible adjustment coefficient as index |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110061496A (en) * | 2019-04-25 | 2019-07-26 | 燕山大学 | The confidence level optimization method of source net lotus Multiobjective programming models |
| CN113298407A (en) * | 2021-06-08 | 2021-08-24 | 昆明理工大学 | Industrial park electricity-gas comprehensive energy system optimization scheduling model establishing method |
| CN115600388A (en) * | 2022-09-30 | 2023-01-13 | 中国矿业大学（Cn） | Fishing ground comprehensive energy system collaborative optimization operation method and device |
| CN116154863A (en) * | 2022-11-25 | 2023-05-23 | 国网山西省电力公司经济技术研究院 | Optimal configuration method considering cooperation and complementation of multi-type flexible resources |
-
2023
- 2023-09-08 CN CN202311156672.7A patent/CN116914748A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110061496A (en) * | 2019-04-25 | 2019-07-26 | 燕山大学 | The confidence level optimization method of source net lotus Multiobjective programming models |
| CN113298407A (en) * | 2021-06-08 | 2021-08-24 | 昆明理工大学 | Industrial park electricity-gas comprehensive energy system optimization scheduling model establishing method |
| CN115600388A (en) * | 2022-09-30 | 2023-01-13 | 中国矿业大学（Cn） | Fishing ground comprehensive energy system collaborative optimization operation method and device |
| CN116154863A (en) * | 2022-11-25 | 2023-05-23 | 国网山西省电力公司经济技术研究院 | Optimal configuration method considering cooperation and complementation of multi-type flexible resources |
Non-Patent Citations (1)
| Title |
|---|
| 林顺富 等: "考虑灵活性互济的跨区电网灵活性资源优化调度策略", 《电力建设》, vol. 44, no. 08, pages 71 - 81 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117439090A (en) * | 2023-12-19 | 2024-01-23 | 浙江大学 | Flexible resource allocation or scheduling method taking flexible adjustment coefficient as index |
| CN117439090B (en) * | 2023-12-19 | 2024-04-02 | 浙江大学 | Flexible resource allocation or scheduling method taking flexible adjustment coefficient as index |
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