CN116885772A - Optimal scheduling method for wind power-photovoltaic-pumped storage-thermal power combined operation system - Google Patents
Optimal scheduling method for wind power-photovoltaic-pumped storage-thermal power combined operation system Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
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Abstract
The invention relates to an optimization scheduling method of a wind power-photovoltaic-pumped storage-thermal power combined operation system, wherein an optimization scheduler establishes an optimization scheduling model of a power distribution network with minimum running network loss and minimum abandoned wind and abandoned light quantity of the combined operation system as optimization targets based on the combined operation system of wind power, photovoltaic, pumped storage and thermal power station access to the power distribution network; in addition, the method fully utilizes the advantages of wind power, photovoltaic and pumped storage new energy sources, reasonably connects the new energy sources to a power distribution network, fully plays the role of a pumping and storage unit, greatly reduces the waste wind and waste light quantity and improves the utilization rate of the new energy sources; in addition, the method improves the resource utilization rate, and simultaneously compares and considers whether the power distribution network topology is dynamically reconstructed, thereby reducing network loss and resource waste to the greatest extent.
Description
Technical Field
The invention relates to the technical field of power system optimization, in particular to an optimization scheduling method of a wind power-photovoltaic-pumped storage-thermal power combined operation system.
Background
The new energy sources such as wind, light, water and the like are novel energy power generation systems with high cost performance, and are important material bases necessary for national economic development and people's life. The national energy agency calls that new energy sources such as wind energy, solar energy and the like become important components of a new energy system by 2050. As renewable new energy sources, wind power, photovoltaic power and the like have the advantages of sustainability, environmental protection and the like, but the renewable new energy sources also have certain defects, the intermittence and the anti-peak regulation characteristic of wind power generation, the randomness and the uncertainty of photovoltaic power generation, and a large number of access to a power grid can bring great challenges to safe and reliable operation of the power grid, and also can cause a large number of waste problems of resources such as wind abandoning, light abandoning and the like. Therefore, the rational utilization of energy storage technology is the key to solve this problem.
In order to solve the problem of impact of wind power and photovoltaic new energy power generation on a power grid, researchers consider using an energy storage technology to solve the problem. The Chinese patent CN 115600858A provides an economic optimization scheduling method of the wind-solar-energy-storage hydrogen production system punishment of wind and light abandonment, and adopts a simulated annealing particle swarm algorithm to perform optimization solution on an optimization scheduling model, wherein the adopted hydrogen production energy storage mode has small investment and high efficiency, but hydrogen production equipment can only be performed when power is excessive, and has no regularity in time. Among the energy storage technologies, the pumped storage power station is the most economical energy storage method at present, and the reasonable utilization of the pumped storage power station can bring better effects to the system. The integration of the pumped storage power station can adjust the wind-solar new energy power station and the power grid system, namely when the load in the combined system is used in a valley, the pumped storage power station converts the redundant generated energy into gravitational potential energy and stores the gravitational potential energy; when the load is in a peak period, the pumped storage power station regenerates electricity to supply the needed electricity. The adjusting capability of the pumped storage power station makes up for the fluctuation of wind-light power generation, reduces the waste of wind-light power generation capacity and improves the operation benefit of the combined system.
In addition, the traditional mathematical model of wind power, photovoltaic and pumped storage combined optimization adopts more methods, such as a traditional algorithm and a modern intelligent optimization algorithm mainly comprising a genetic algorithm and a particle swarm optimization algorithm, for example, a hybrid particle swarm algorithm is adopted in Chinese patent No. 108879793B to optimize an off-grid hybrid system complementary to a wind power, photovoltaic and storage hydropower station. The existing research has compared the software algorithm with the modern intelligent optimization algorithm, and proves that the method for solving the joint optimization operation problem by means of the mature commercial software has better effect than the intelligent optimization algorithm.
However, the research on the combined optimization operation of the pumped storage peak shaving by the high-proportion renewable energy grid connection is less at present, and the influence of network reconstruction, voltage, current and the like in a power distribution network system is rarely considered, so that the method is not completely suitable for the actual situation and future requirements of a Chinese power system. Pumped storage is an energy storage facility with large capacity, mature technology and low cost in an electric power system. Only rely on the flexibility of thermal power generating unit to adjust, can lead to thermal power generating unit to open frequently to stop, cause threat to electric wire netting operational safety and economic nature. Therefore, the energy storage technology is combined with a modern software algorithm to be an effective means for solving the power grid dispatching problem, ensuring the stability of the power grid and reducing the grid-connected wind abandoning rate of new energy, and the method has an important effect on improving the flexibility of a power system.
Disclosure of Invention
The invention aims to provide an optimal scheduling method of a wind power-photovoltaic-pumped storage-thermal power combined operation system, and aims to solve the technical problems that the safety operation of a power grid is affected when a large amount of wind power generation and photovoltaic power generation are connected into the power grid, so that the wind power and the optical power cannot be effectively utilized, and the wind power and the optical power are largely abandoned.
In order to achieve the purpose, the invention provides an optimal scheduling method of a wind power-photovoltaic-pumped storage-thermal power combined operation system, which comprises the following steps:
s1: based on a combined operation system of wind power, photovoltaic, pumped storage and a thermal power station connected to a power distribution network, establishing a power distribution network optimization scheduling model with minimum operation network loss and minimum waste wind and waste light quantity of the combined operation system as optimization targets;
s2: based on the optimization target, constructing constraint conditions of corresponding wind power, photovoltaic, pumped storage, thermal power stations and static var compensators; the constraint conditions of wind power, photovoltaic, pumped storage, thermal power stations and static reactive compensators can ensure that different types of power supplies and equipment are connected and operated without adverse effect on the operation stability of the power distribution network, and meanwhile, the constraint conditions can ensure that various power supplies and equipment have certain flexibility and schedulability so as to adapt to the optimization of the operation of the power distribution network;
s3: establishing topological structure constraint conditions, power grid power balance constraint and branch power flow constraint conditions of the operation of the power distribution network; the topological structure constraint condition is used for avoiding forming a rotary island, ensuring the safety, stability and economy of a network and improving the running reliability and running efficiency of the power distribution network; the power balance constraint condition of the power grid is used for controlling the equal input and output power of each node in the network topology, and the power balance constraint condition is satisfied by load flow calculation; the branch power flow constraint condition is used for controlling the node voltage and the branch current amplitude to fluctuate within an allowable range, and controlling the active power transmission of the circuit to fluctuate within the allowable range;
s4: converting the power distribution network optimization scheduling model and constraint conditions into a mixed second-order cone planning model by using a second-order cone relaxation method; wherein the transformation model aims at three aspects: firstly, the mixed second-order cone planning model has stronger expression capability, and can be used for better describing and representing the complexity of the problem; secondly, the power distribution network optimization scheduling model is converted into a mixed second-order cone planning model, and the existing solving algorithm and tool can be utilized, so that the power distribution network optimization scheduling model is more efficient; finally, the various constraint conditions given in S2 and S3 can better process inequality constraint and nonlinear constraint by converting the constraint conditions into a mixed second-order cone planning model so as to ensure the feasibility and rationality of the problem;
s5: and testing the mixed second-order cone planning model, carrying out optimization solution on the power distribution network optimization scheduling model, and comparing the optimization scheduling result under the condition of dynamic reconstruction of the power distribution network.
Further, in step S1, the expression of the power distribution network optimization scheduling model is:
wherein, formula 1 is the objective function with minimum network loss, formula 2 is the objective function with minimum waste wind and waste light quantity, I ij,t Representing the branch current, τ ij Indicating the state of the branch switch, r ij Representing the impedance of the branch, Ω line Omega is a set of all line components in a power distribution network topology w Node set for accessing wind turbine generator in power distribution network topology, omega p A node set of a photovoltaic unit is accessed into the topology of the power distribution network; p'. w and Pi p,max The wind and light discarding amount at the time t are respectively; p (P) i w,max and Pi p,max The maximum value of the predicted power generation amount of the wind power unit and the photovoltaic unit is respectively;for the generation of the wind power at time t, < >>And the generated energy of the photovoltaic at the moment t.
Further, introducing a penalty factor to convert the objective function with the minimum network loss and the objective function with the minimum waste wind and waste light quantity into a single objective optimization function, and obtaining a median value:
wherein ,is a penalty factor.
Further, in step S2,
the expression for constructing the constraint condition of wind power is as follows:
wherein ,for the minimum output power of the fan at the moment t, +.>The maximum predicted force of the fan is the moment t;
the expression for constructing the photovoltaic power generation power constraint condition is as follows:
wherein ,for the minimum value of the output of the photovoltaic power station at time t,/->The predicted maximum value of the power of the photovoltaic power station at the moment t;
the pumping and the power generation of the pumped storage power station cannot be simultaneously carried out, at most one working state is only allowed at the same time, and one Bernoulli variable description is introduced, so that the expression of the mutual exclusion constraint conditions of pumping power of a water pump, generating power of a water turbine and pumping and discharging states is as follows:
wherein ,pumping power of the water pump at time t +.>The maximum value of the pumping power of the water pump is obtained,for generating power of the water turbine, < >>The maximum power of the water turbine is; /> and />For two boolean variables, +.>When the value of (1) is 1, the unit output is in the power generation state, < >>When the value is 1, the water pump is in a pumping state, and />1 cannot be taken at the same time;
the expression of the reservoir energy balance constraint condition of the pumped storage power station is as follows:
wherein ,Ei,t and Ei,t+1 The energy stored in the energy storage power station is pumped at the time t and the time t+1 respectively, and />The water pump pumping efficiency and the water turbine generating efficiency are respectively +.> and />Respectively the minimum value and the maximum value of reservoir energy storage energy of the pumped storage unit;
the expression of the constraint condition of the thermal generator set is as follows:
wherein ,the power generation active power of the thermal power generating unit at the time t is P i h,min Is the lower limit of the active power of the thermal power unit, P i h,max The upper limit of active power of the thermal power generating unit; />Reactive power for generating electricity of thermal power generating unit at t moment, < >>Andrespectively the upper limit and the lower limit of reactive power of the thermal power generating unit, omega h Accessing a node set of a thermal power unit into a power distribution network topology;
the expression of the constraint condition of the climbing power of the thermal power unit is as follows:
wherein ,for the maximum downward slope climbing rate of the thermal power generating unit, < > for>The maximum upward slope climbing rate of the thermal power generating unit is set;
since wind power and photovoltaic have substantially no capability to provide reactive power to the grid, or only very little reactive power, SVG is considered as a reactive compensation device to provide the required reactive power to the grid, SVG regulatory constraints are:
wherein ,reactive power provided for SVG, +.>Maximum value of reactive power allowed for SVG, Ω SVG And (5) accessing the node set of the SVG in the topology of the power distribution network.
Further, in step S3, establishing a topology constraint condition for operation of the power distribution network includes the following steps:
s31: consider that the topology of the distribution network is a radial structure, the operation structure of the distribution network is a radial expression:
g m ∈G (14)
wherein ,gm A certain radial network topology structure after reconstruction; g is a set of all feasible radial network topology structures, and the network topology is required to be radial after the power distribution network is reconstructed, so that island and loop situations cannot occur;
s32, considering the reconstruction of the power distribution network, the switch cannot be frequently opened, and the expression satisfying the constraint condition is as follows:
wherein ,τij,t-1 and τij,t The switching state of the branch circuit at the time t-1 and the switching state after the reconstruction at the time t are n smax The maximum number of allowed switching actions is reconstructed.
Further, in step S3, establishing the branch power flow constraint condition includes the following steps:
s33: considering branch power flow constraint, the expression of node power balance is:
wherein , and />The method comprises the steps of respectively obtaining the wind turbine on-line active power of a node j at a moment t, the photovoltaic turbine on-line active power, the thermal power generating power of a thermal power generating unit, the generating power of a pumped storage unit, the pumping power of the pumped storage unit, the node load active power and the active injection power; /> and />The method comprises the steps of respectively providing network surfing reactive power of a thermal power generating unit at t moment by a node j, reactive power provided by SVG, reactive power of a node load and reactive power injection, and omega BUS The method comprises the steps that a set is formed by all nodes in a power distribution network topology;
the expression of the tide model is:
wherein ,Ωu and Ωv Respectively defining the parent node and the child node of the node j after the power reference direction, and not changing along with the process of network reconstruction, thereby being universal for the process of network dynamic change; u (U) i,t and Uj,t The voltage at the time t is the voltage of the nodes i and j; p (P) ij,t and Qij,t Active power and reactive power transmitted between nodes i and j, respectively; r is (r) ij and xij Resistance and reactance between nodes i and j; and />Active power and reactive power injected by the node distributed source and SVG at the moment t respectively;
node voltage and branch current satisfy:
wherein , and />Respectively square upper and lower limits of the voltage of the node i; /> and />The upper and lower limits of the branch current are respectively.
Further, in step S4, for the non-convexity constraint of the system model and the constraint condition established in steps S1-S3, a second order cone relaxation (Second Order Cone Relaxation, SOCR) technique is adopted to relax the non-convexity constraint into a second order cone constraint, and the model constraint constructed in the previous step is converted into a second order cone planning function, wherein the specific conversion steps are as follows:
s41: performing dimension reduction on formulas (17) - (19) in step S33, introducing variables and the following inequality:
wherein ,M1 ,M 2 and M3 The formula defines that when the branch is disconnected, the current, the active power and the reactive power of the branch are all 0;
s42: the formulas (17) to (19) in the rewriting step S33 are:
for node i, defining that the virtual voltage of the node on the branch ij connected thereto satisfies:
wherein ,M4 Is a positive number that is sufficiently large to be useful,
s43: the rewrite (25) is a linear constraint:
s44: relaxing the apparent power quadratic constraint of equation (26) to a cone constraint:
s45: the above can be written as a standard second order cone form through equivalent conversion, namely:
wherein ,is a two-norm.
Further, the step S5 includes:
s51: and (3) obtaining a second order cone standard form model through the steps S1-S4:
objective function: min (F) 1 +F 2 )
S52: the second order cone planning function is solved efficiently by means of Yalmip, gurobi business algorithm packages and the like, and further the optimal scheduling result of the combined operation system is verified by comparing simulation results of the power distribution network under the conditions of dynamic reconstruction, access of the power extraction and storage station and the like.
Compared with the prior art, the invention has the following beneficial effects:
1. the comprehensiveness is strong: according to the invention, wind power, photovoltaic, pumped storage, reactive power compensation units and traditional thermal power units are comprehensively considered, the power is taken as a main element, an optimal scheduling model of the power distribution network is established with an optimal target for reducing resource waste and loss simultaneously, and the optimal scheduling model of the power distribution network is optimally solved, so that more economical and practical resource scheduling can be realized compared with conventional single-target optimal scheduling;
2. the resource utilization rate is improved: the invention fully utilizes the advantages of wind power, photovoltaic and pumped storage new energy sources, reasonably connects the energy sources to the power distribution network, fully plays the role of the pumping and storage unit, and compares and analyzes whether the pumping and storage energy storage technology is utilized or not;
3. loss reduction: and the dynamic reconstruction of the power distribution network topology is compared and considered while the resource utilization rate is improved, so that the network loss and the resource waste are reduced to the greatest extent.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a wind power-photovoltaic-pumped storage-thermal power-containing combined power generation system according to the invention;
FIG. 2 is a diagram of an improved IEEE33 node network topology and various group access locations in accordance with the present invention;
FIG. 3 is a graph of predicted maximum and actual output of a wind power and photovoltaic unit according to the invention;
FIG. 4 is a graph of thermal power plant output according to the present invention;
FIG. 5 is a pump storage unit output curve according to the present invention;
FIG. 6 is a graph showing the change in net loss of a pumped-storage unit according to the present invention;
FIG. 7 is a graph of the change in waste wind and waste light for a pumped storage unit according to the present invention;
fig. 8 is a diagram of network topology reconstruction at time t= 6,10,15,24 according to the present invention;
FIG. 9 is a graph showing the change in network loss of a pumped storage unit with or without the reconstruction of the network topology according to the present invention;
fig. 10 is a graph showing a change of wind and light rejection of a pumped storage unit under the network topology reconstruction according to the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, if a directional indication (such as up, down, left, right, front, and rear … …) is involved in the embodiment of the present invention, the directional indication is merely used to explain the relative positional relationship, movement condition, etc. between the components in a specific posture, and if the specific posture is changed, the directional indication is correspondingly changed.
In addition, if there is a description of "first", "second", etc. in the embodiments of the present invention, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, if "and/or" and/or "are used throughout, the meaning includes three parallel schemes, for example," a and/or B "including a scheme, or B scheme, or a scheme where a and B are satisfied simultaneously. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
The second order cone programming function is efficiently solved by means of a Yalmip, gurobi business algorithm package and the like. Taking an IEEE33 node model as an example, a wind generating set, a photovoltaic power station, a pumped storage power station, a thermal power unit, an SVG device and the like are added on the basis of the network structure, and a specific network topology structure diagram is shown in FIG. 2, wherein 3 stations of the thermal power unit are respectively connected with node positions 1, 11 and 28, 3 stations of the wind generating set are respectively connected with node positions 14, 25 and 32, 2 stations of the photovoltaic power station are respectively connected with node positions 21 and 32, 2 stations of the pumped storage power station are respectively connected with node positions 6 and 13, and 2 stations of the SVG device are respectively connected with node positions 3 and 6. The maximum output of the thermal power generating unit is 10kW, and the climbing rates are 60.
And (5) analyzing simulation results of power distribution network reconstruction. The power loss and the wind and light discarding factors of the power distribution network are as follows: line transmission constraints, wind power, photovoltaic, thermal power, pumped storage unit constraints, and the like. Taking the constraint of the pumped storage unit as an example, the influence on the system is considered through the access condition of the pumped storage unit.
Under the condition of being connected into a pumped storage unit, the power generation efficiency of the pumped storage power station is 0.8, the pumping efficiency is 0.9, the maximum predicted output and the actual output of the wind-solar unit are shown in fig. 3, and the output curves of the thermal power unit and the pumped storage unit are shown in fig. 4 and 5 respectively. The result shows that the wind-solar unit basically outputs with the maximum predicted value, and the generated energy of the thermal power unit is larger and the pumped storage unit generates power when the wind-solar output is smaller; with the increase of wind and light output, a main force function is played in a power grid, the output of a thermal power unit becomes smaller, and a pumped storage unit starts to pump water and store energy; and when the wind-solar power output is reduced again, the thermal power unit and the pumped storage power generation output are gradually increased. The time-varying curve of the network loss is shown in fig. 6, and the network loss is less after the pumped storage is accessed; fig. 7 shows the change of the waste wind and the waste light without being connected into the pumped storage unit and the pumped storage unit under the same condition, and obviously, the utilization rate of wind and light is greatly improved after being connected into the pumped storage unit.
And (5) considering the result analysis of the reconstruction of the power distribution network. The step is considered to be dynamic reconstruction of the distribution network, wherein the reconstruction structure at the time t=6, 10, 16, 24 is shown in fig. 8 when the pumped storage unit is connected. Under the condition that other parameters are not changed basically, the network loss and the waste wind and waste light change conditions under the condition that the pumped storage unit is not connected are respectively considered, the network loss changes under the two conditions are shown in fig. 9, and the waste wind and waste light changes are shown in fig. 10. Compared with the network loss changes of fig. 6 and 9, the dynamic reconstruction is more beneficial to reducing the system loss and saving resources; through fig. 7 and 10, not only is the wind and light utilization rate improved by the connection of the pumped storage unit, but also the amount of abandoned wind and abandoned light is greatly reduced by the dynamic reconstruction of the power distribution network, and the optimal scheduling effect of the combined operation system is realized.
The foregoing description is only of the optional embodiments of the present invention, and is not intended to limit the scope of the invention, and all the equivalent structural changes made by the description of the present invention and the accompanying drawings or the direct/indirect application in other related technical fields are included in the scope of the invention.
Claims (10)
1. The wind power-photovoltaic-pumped storage-thermal power combined operation system optimization scheduling method is characterized by comprising the following steps of:
s1: based on a combined operation system of wind power, photovoltaic, pumped storage and a thermal power station connected to a power distribution network, establishing a power distribution network optimization scheduling model with minimum operation network loss and minimum waste wind and waste light quantity of the combined operation system as optimization targets;
s2: based on the optimization target, constructing constraint conditions of corresponding wind power, photovoltaic, pumped storage, thermal power stations and static var compensators;
s3: establishing topological structure constraint conditions, power grid power balance constraint and branch power flow constraint conditions of the operation of the power distribution network;
s4: converting the power distribution network optimization scheduling model and constraint conditions into a mixed second-order cone planning model by using a second-order cone relaxation method;
s5: and testing the mixed second-order cone planning model, carrying out optimization solution on the power distribution network optimization scheduling model, and comparing the optimization scheduling result under the condition of dynamic reconstruction of the power distribution network.
2. The optimization scheduling method of a wind power-photovoltaic-pumped storage-thermal power combined operation system according to claim 1, wherein in step S1, the expression of the optimization scheduling model of the power distribution network is:
wherein, formula 1 is the objective function with minimum network loss, formula 2 is the objective function with minimum waste wind and waste light quantity, I ij,t Representing the branch current, I ij,t Representing the branch current, τ ij Indicating the state of the branch switch, r ij Representing the impedance of the branch, Ω line Omega is a set of all line components in a power distribution network topology w Node set for accessing wind turbine generator in power distribution network topology, omega p A node set of a photovoltaic unit is accessed into the topology of the power distribution network; p'. w and Pi p,max The wind and light discarding amount at the time t are respectively; p (P) i w,max and Pi p,max The maximum value of the predicted power generation amount of the wind power unit and the photovoltaic unit is respectively;for the generation of the wind power at time t, < >>And the generated energy of the photovoltaic at the moment t.
3. The optimization scheduling method of the wind power-photovoltaic-pumped storage-thermal power combined operation system according to claim 2, wherein a penalty factor is introduced to convert an objective function with minimum network loss and an objective function with minimum waste wind and waste light quantity into a single objective optimization function, and a median value is obtained:
wherein θ is a penalty factor.
4. The method for optimizing and scheduling a wind power-photovoltaic-pumped storage-thermal power combined operation system according to claim 3, wherein in step S2,
the expression for constructing the constraint condition of wind power is as follows:
wherein ,for the minimum output power of the fan at the moment t, +.>The maximum predicted force of the fan is the moment t;
the expression for constructing the photovoltaic power generation power constraint condition is as follows:
wherein ,for the minimum value of the output of the photovoltaic power station at time t,/->And the predicted maximum value of the power of the photovoltaic power station at the moment t.
5. The method for optimizing and scheduling a wind power-photovoltaic-pumped storage-thermal power combined operation system according to claim 4, wherein in step S2,
the Bernoulli variable is introduced, and the expression provided with mutual exclusion constraint conditions of pumping power of a water pump, generating power of a water turbine and pumping and discharging states is as follows:
wherein ,pumping power of the water pump at time t +.>Maximum value of pumping power for water pump, +.>For generating power of the water turbine, < >>The maximum power of the water turbine is; /> and />For two boolean variables,when the value of (1) is 1, the unit output is in the power generation state, < >>When the value is 1, the water pump is in a pumping state, and the valve is in a +.> and />1 cannot be taken at the same time;
the expression of the reservoir energy balance constraint condition of the pumped storage power station is as follows:
wherein ,Ei,t and Ei,t+1 The energy stored in the energy storage power station is pumped at the time t and the time t+1 respectively, and />The water pump pumping efficiency and the water turbine generating efficiency are respectively +.> and />Respectively the minimum value and the maximum value of reservoir energy storage energy of the pumped storage unit.
6. The method for optimizing and scheduling a wind power-photovoltaic-pumped storage-thermal power combined operation system according to claim 5, wherein in step S2,
the expression of the constraint condition of the thermal generator set is as follows:
wherein ,the power generation active power of the thermal power generating unit at the time t is P i h,min Is the lower limit of the active power of the thermal power unit, P i h ,max Is fire ofThe upper limit of the active power of the motor group; />Reactive power for generating electricity of thermal power generating unit at t moment, < >> and />Respectively the upper limit and the lower limit of reactive power of the thermal power generating unit, omega h Accessing a node set of a thermal power unit into a power distribution network topology;
the expression of the constraint condition of the climbing power of the thermal power unit is as follows:
wherein ,for the maximum downward slope climbing rate of the thermal power generating unit, < > for>The maximum upward slope climbing rate of the thermal power generating unit is set;
since wind power and photovoltaic have substantially no capability to provide reactive power to the grid, or only very little reactive power, SVG is considered as a reactive compensation device to provide the required reactive power to the grid, SVG regulatory constraints are:
wherein ,reactive power provided for SVG, +.>Maximum value of reactive power allowed for SVG, Ω SVG And (5) accessing the node set of the SVG in the topology of the power distribution network.
7. The method for optimizing and scheduling a wind power-photovoltaic-pumped storage-thermal power combined operation system according to claim 6, wherein in step S3, establishing a topological structure constraint condition of the operation of the power distribution network comprises the following steps:
s31: the radial expression of the operation structure of the power distribution network is as follows:
g m ∈G (14)
wherein ,gm G is a set of all feasible radial network topologies;
step S32, an expression satisfying the topological structure constraint condition of the operation of the power distribution network is:
wherein ,τij,t-1 and τij,t The switching state of the branch circuit at the time t-1 and the switching state after the reconstruction at the time t are n smax The maximum number of allowed switching actions is reconstructed.
8. The optimal scheduling method for a wind power-photovoltaic-pumped storage-thermal power combined operation system according to claim 7, wherein in step S3, establishing branch tidal current constraint conditions comprises the steps of:
s33: considering branch power flow constraint, the expression of node power balance is:
wherein , and />The method comprises the steps of respectively obtaining the wind turbine on-line active power of a node j at a moment t, the photovoltaic turbine on-line active power, the thermal power generating power of a thermal power generating unit, the generating power of a pumped storage unit, the pumping power of the pumped storage unit, the node load active power and the active injection power; /> and />The method comprises the steps of respectively providing network surfing reactive power of a thermal power generating unit at t moment by a node j, reactive power provided by SVG, reactive power of a node load and reactive power injection, and omega BUS The method comprises the steps that a set is formed by all nodes in a power distribution network topology;
the expression of the tide model is:
wherein ,Ωu and Ωv Respectively defining the parent node and the child node of the node j after the power reference direction, and not changing along with the process of network reconstruction, thereby being universal for the process of network dynamic change; u (U) i,t and Uj,t The voltage at the time t is the voltage of the nodes i and j; p (P) ij,t and Qij,t Active power and reactive power transmitted between nodes i and j, respectively; r is (r) ij and xij Resistance and reactance between nodes i and j; and />Active power and reactive power injected by the node distributed source and SVG at the moment t respectively;
node voltage and branch current satisfy:
wherein , and />Respectively square upper and lower limits of the voltage of the node i; /> and />The upper and lower limits of the branch current are respectively.
9. The optimization scheduling method of a wind power-photovoltaic-pumped storage-thermal power combined operation system according to claim 8, wherein in step S4, a second order cone relaxation (Second Order Cone Relaxation, SOCR) technique is adopted to relax the non-convex constraint in which the system model and constraint conditions are established in steps S1-S3 into a second order cone constraint, and the model constraint is converted into a second order cone planning function, and the converting step includes:
s41: performing dimension reduction on formulas (17) - (19) in step S33, introducing variables and the following inequality:
wherein ,M1 ,M 2 and M3 The formula defines that when the branch is disconnected, the current, the active power and the reactive power of the branch are all 0;
s42: the formulas (17) to (19) in the rewriting step S33 are:
for node i, defining that the virtual voltage of the node on the branch ij connected thereto satisfies:
wherein ,M4 Is a positive number that is sufficiently large to be useful,
s43: the rewrite (25) is a linear constraint:
s44: relaxing the apparent power quadratic constraint of equation (26) to a cone constraint:
s45: the above can be written as a standard second order cone form through equivalent conversion, namely:
wherein ,is a two-norm.
10. The optimal scheduling method for the wind power-photovoltaic-pumped storage-thermal power combined operation system according to claim 9, wherein in step S5, a second order cone standard form model is obtained according to formula 5-formula 31:
objective function: min (F) 1 +F 2 )
S52: the second order cone planning function is solved efficiently by means of Yalmip, gurobi business algorithm packages and the like, and further the optimal scheduling result of the combined operation system is verified by comparing simulation results of the power distribution network under the conditions of dynamic reconstruction, access of the power extraction and storage station and the like.
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