CN115689233A - Wind, light, water, fire and storage system complementary coordination optimization scheduling method considering peak regulation initiative - Google Patents
Wind, light, water, fire and storage system complementary coordination optimization scheduling method considering peak regulation initiative Download PDFInfo
- Publication number
- CN115689233A CN115689233A CN202211407305.5A CN202211407305A CN115689233A CN 115689233 A CN115689233 A CN 115689233A CN 202211407305 A CN202211407305 A CN 202211407305A CN 115689233 A CN115689233 A CN 115689233A
- Authority
- CN
- China
- Prior art keywords
- power
- wind
- generating unit
- peak regulation
- storage system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
Abstract
The invention discloses a wind, light, water and fire storage system complementary coordination optimization scheduling method considering peak regulation initiative, which comprises a unit peak regulation compensation and allocation model taking thermal power, hydropower, wind power, photovoltaic and stored energy into consideration to participate in power grid scheduling by taking the total benefit maximization of a system as a target; establishing a wind-light-water-fire-storage multi-energy system complementary coordination optimization scheduling model taking minimum net load fluctuation, minimum system operation cost and minimum renewable energy power abandonment as optimization targets; establishing a layered optimization scheduling scheme to simplify the calculation complexity of multi-energy complementary coordination scheduling, and utilizing the peak clipping and valley filling characteristics of an energy storage system and exerting the deep peak shaving capacity of a thermal power generating unit; based on a decomposition coordination idea, through coordination of upper and lower layer problems and alternate iterative solution of the lower layer problems, the charging and discharging power of the energy storage system is decided and a peak regulation strategy is optimized; the technical problems that the existing regulating capacity is difficult to meet the requirements of renewable energy consumption and peak regulation and the like are solved.
Description
Technical Field
The invention belongs to the technical field of multi-energy complementary coordination scheduling, and particularly relates to a wind, light, water, fire and storage system complementary coordination optimization scheduling method considering peak regulation initiative.
Background
In recent years, with the rapid development of power systems, the proportion of renewable energy sources such as wind power and photovoltaic is increasing. However, due to the inverse peak regulation of wind power and uncertain characteristics of the inverse peak regulation, the peak-to-valley difference of the load is increased, and the uncertain pressure of the source load increases the peak regulation load of the power system. In order to deal with the phenomenon that the thermal power generating unit is started and stopped frequently due to fluctuation of wind power output, for example, when the wind power is large, measures of stopping the thermal power generating unit are taken to fully accept the wind power; if the wind power output is limited, a large amount of abandoned wind power is generated, so that precious renewable resources are wasted. The existing regulation capability of the power system is simply relied on, and the requirements of renewable energy consumption and peak regulation are difficult to meet.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the wind, light, water and fire storage system complementary coordination optimization scheduling method considering the peak regulation initiative is provided to establish wind, light, water and fire storage multi-energy system complementary coordination optimization scheduling considering the peak regulation initiative of a thermal power generating unit, and solve the technical problems that the renewable energy consumption and peak regulation requirements are difficult to meet only by means of the existing regulation capacity of a power system and the like.
The technical scheme of the invention is as follows:
a wind, light, water and fire storage system complementary coordination optimization scheduling method considering peak regulation initiative comprises the following steps:
s1, considering a peak regulation compensation and allocation model of a power generation unit, a hydropower station, a wind power unit, a photovoltaic unit and an energy storage unit which participate in power grid dispatching, with the aim of maximizing the total system benefit;
s2, establishing a wind-light-water-fire-storage multi-energy system complementary coordination optimization scheduling model taking minimum net load fluctuation, minimum system operation cost and minimum renewable energy power waste amount as optimization targets;
s3, establishing a hierarchical optimization scheduling scheme to simplify the calculation complexity of multi-energy complementary coordination scheduling, and utilizing the peak clipping and valley filling characteristics of the energy storage system and exerting the deep peak-shaving capacity of the thermal power generating unit;
and S4, based on the decomposition coordination idea, deciding the charging and discharging power of the energy storage system and optimizing a peak regulation strategy through coordination of the upper layer problem and the lower layer problem and alternate iterative solution of the lower layer problem.
The method for the peak regulation compensation and allocation model of the unit taking thermal power, hydropower, wind power, photovoltaic and energy storage into consideration to participate in power grid dispatching by taking the total benefit of the system as the maximum target in the step 1 comprises the following steps:
s101, establishing a compensation model based on deep peak shaving of a thermal power generating unit;
s102, establishing a peak regulation cost-based allocation model;
s103, considering the peak regulation initiative constraint of the thermal power generating unit, and whether each unit in the system participates in deep peak regulation or not is that whether benefit can be obtained from peak regulation service or not.
S101, the method for establishing the compensation model based on the thermal power generating unit deep peak regulation comprises the following steps: at a certain scheduling moment, if the unit output is reducedForce measures are taken to carry out peak shaving, so that the cost loss of the part is compensated to a certain extent; I.C. A g 、I h 、I wind 、I pv And I s The method comprises the following steps of respectively representing that thermal power, hydropower, wind power, photovoltaic units and an energy storage system participate in power grid dispatching, supposing that only the units participate in peak shaving in the whole system, and the units participating in peak shaving obtain compensation according to the electric energy of the peak shaving, wherein the expression is as follows:
in the formula:
the peak regulation compensation cost is added for the conventional thermal power generating unit;
compensating the price for the conventional thermal power generating unit by peak regulation;
adjusting peak power for a conventional thermal power generating unit; thus, the total peaking compensation cost at time t is:
s102, the method for establishing the peak regulation cost-based allocation model comprises the following steps:
in the deep peak regulation process, peak regulation compensation cost is jointly born by a thermal power generating unit, a wind power generating unit and a photovoltaic unit, cost expense of the wind power generating unit and the photovoltaic unit is shared according to the total power generation electric energy proportion in a deep peak regulation day, the shared part of the thermal power generating unit is shared according to the electric energy proportion of an access power grid, and the expression is as follows:
wherein the content of the first and second substances,
respectively allocating the peak shaving cost of each thermal power generating unit, the wind power plant and the photovoltaic power station at the time t;
the power of the thermal power generating unit i at the moment t is connected to the grid;
the grid connection power at the t moment of the wind power plant;
and the power is the power of the photovoltaic power station at the moment t.
S103, considering the peak regulation initiative constraint of the thermal power generating unit, the key of whether each unit in the system participates in deep peak regulation or not is whether the method can benefit from peak regulation service or not, and the method comprises the following steps: the peak regulation initiative promotes the thermal power generating unit to actively participate in peak regulation through scheduling compensation; for the wind turbine generator and the photovoltaic power station, the deep peak shaving effectively increases the share of the wind turbine generator and the photovoltaic power station in the electric energy market, and compared with cost sharing, the wind turbine generator and the photovoltaic power station actively participate in the deep peak shaving as long as the cost of the sharing is lower than the benefit brought by self-increased electric energy.
S2, establishing a wind-light-water-fire-storage multi-energy system complementary coordination optimization scheduling model taking minimum net load fluctuation, minimum system operation cost and minimum renewable energy power consumption as optimization targets, and comprising the following steps of:
s201, establishing a wind, light, water, fire and energy storage multi-energy system complementary coordination optimization scheduling model with the optimization objectives of minimization of net load fluctuation, maximization of energy storage system operation benefit, minimization of system operation cost and minimization of renewable energy power consumption.
S202, power balance constraint, thermal power unit constraint, wind power unit output constraint, hydroelectric power unit output constraint, photovoltaic power station output constraint, energy storage constraint and line transmission capacity constraint are used as constraint conditions of the optimization model.
S201, the method for establishing the wind, light, water, fire and energy storage multi-energy system complementary coordination optimization scheduling model comprises the following steps:
establishing a net load fluctuation minimum objective function:
in the formula: p glt Is the net load value at time t;
actual grid-connected power of a wind power plant l at the moment t;
actual grid-connected power of hydropower h at time t;
the actual grid-connected power of the photovoltaic m at the time t is obtained; p St The discharge power of the energy storage device at the moment t is the net load average value in a scheduling period; n is a radical of W The total number of the wind power plants; n is a radical of S The total number of the photovoltaic power stations; n is a radical of H The total number of the hydropower stations;
and (3) considering the operating power generation yield of the energy storage system, establishing an energy storage system operation and environment yield model:
wherein p is price The price of electricity is the power grid; eta c And η d Respectively representing the charging efficiency and the discharging efficiency of the energy storage system;
and
respectively the charging and discharging power of the energy storage system at the moment t; m is the total number of types of pollutants generated by a superior power grid; p is a radical of price,k Is the unit discharge cost of the pollutants; xi grid,k Discharging the kth pollutant density for the electric energy of a superior power grid production unit;
considering the charge-discharge cost of the energy storage system, establishing an operation cost model of the energy storage system:
wherein, c sc The cost coefficient of the charge and discharge power of the energy storage system;
and
respectively the charging and discharging power of the energy storage system at the moment t; the maximum objective function of the operating yield of the energy storage system is as follows:
max f sy ＝f ss -f sc
the coal consumption cost and the start-stop cost of the thermal power generating unit are as follows:
wherein f is 2 The method is the conventional peak shaving operation cost of the thermal power generating unit; f. of mh And f qt Respectively representing the coal consumption cost and the start-stop cost of the thermal power generating unit; a is i 、b i 、c i Respectively representing consumption coefficients of the thermal power generating unit i; p i,t Outputting power for the thermal power generating unit;
and (3) calculating the unit loss cost:
f i,l ＝βS unit,i /(2N f (P))
beta is the influence coefficient of the operation of the thermal power generating unit; s. the unit,i Purchasing cost for the ith thermal power generating unit; n is a radical of f (P) representing the number of rotor cracking cycle cycles determined from the rotor low cycle fatigue curve;
in the stage of oil feeding depth peak regulation, additional peak regulation cost for oil feeding combustion supporting is generated, and the cost is as follows:
f y,i,t ＝Q oil,i,t p oil
Q oil,i,t for the oil input at the deep peak regulation stage of the ith unit at the time t oil Is the current season oil price;
the thermal power generating unit can express the operation cost by sections according to different operation states:
wherein, P i,min And P i,max Respectively the minimum output and the maximum output of the thermal power generating unit; p b The stable combustion limit load value is the stable combustion limit load value when the unit is subjected to deep peak regulation (oil injection); p a The stable combustion load value is the stable combustion load value when the unit is subjected to deep peak shaving (oil is not injected); the consumption capacity of the renewable energy source is represented by the sum of the wind curtailment electricity quantity, the water curtailment electricity quantity and the light curtailment electricity quantity in the scheduling period.
Step S202, the constraint condition includes:
and power balance constraint:
wherein the content of the first and second substances,
is the load of j node at time t;
and (3) constraint of the thermal power generating unit:
u it P i,min ＜P i,t ≤u it P i,max
u it P ib ＜P i,t ≤u it P i,max
-r i,down ≤P i,t -P i,t-1 ≤r i,up
wherein r is i,up And r i,down The maximum upward and downward climbing rates of the thermal power generating unit are respectively. (ii) a
Output restraint of the wind turbine generator:
output restraint of the hydroelectric generating set:
photovoltaic power station output restraint:
and (4) restraining the stored energy:
energy storage charge-discharge restraint:
line transmission capacity constraint:
wherein G is l,m A transfer distribution factor for node m to line l;
active injection power is provided for the node m to the scene s in the t period; m is the number of nodes.
S3, the method for establishing the hierarchical optimization scheduling scheme to simplify the calculation complexity of the multi-energy complementary coordination scheduling, utilizing the peak clipping and valley filling characteristics of the energy storage system and exerting the deep peak regulation capability of the thermal power generating unit comprises the following steps:
s301, a layered optimization scheduling scheme is provided, an upper-layer scheduling model optimizes a wind-light-water storage combined system output model by using the throughput capacity of an energy storage system to follow the fluctuation of wind power, photovoltaic and load and taking minimization of net load fluctuation and maximization of operation income of the energy storage system as targets so as to reduce peak clipping and valley filling pressure of a thermal power unit on load; the upper layer model adopts a particle swarm optimization algorithm to carry out model solution, and a wind, light, water and storage combined system optimal output model with minimized net load fluctuation and maximized energy storage system operation income is obtained;
and S302, the lower layer model calls a CPLEX tool to solve through an MATLAB platform by taking minimization of the operation cost of the thermal power unit and minimization of wind abandon, light abandon and water abandon as targets through an equivalent load curve of the upper layer model and combining penalty cost of the wind abandon and the light abandon, peak load regulation cost and operation characteristics of each unit.
The invention has the beneficial effects that:
the invention discloses a method for deciding charge and discharge power of an energy storage system and optimizing a peak regulation strategy by coordination of upper and lower layer problems and alternate iterative solution of the lower layer problems based on a decomposition coordination idea. The method comprises the following steps: s1, providing a unit peak regulation compensation and allocation model taking thermal power, hydropower, wind power, photovoltaic and energy storage into consideration to participate in power grid dispatching, wherein the aim is to maximize the total benefit of the system; s2, establishing a wind-light-water-fire-storage multi-energy system complementary coordination optimization scheduling model taking minimum net load fluctuation, minimum system operation cost and minimum renewable energy power waste as optimization targets; s3, a layered optimization scheduling scheme is provided to simplify the calculation complexity of multi-energy complementary coordination scheduling, fully utilize the peak clipping and valley filling characteristics of an energy storage system and exert the deep peak shaving capacity of the thermal power generating unit; and S4, based on a decomposition coordination idea, determining the charge and discharge power of the energy storage system and optimizing a peak regulation strategy through coordination of the upper layer problem and the lower layer problem and alternative iterative solution of the lower layer problem.
According to the invention, a multi-source complementary coordination mechanism is established to fully exploit the flexible regulation capability of the power system, and the renewable energy consumption is improved while the peak regulation requirement is met. Because the power supply structure of the existing power system still mainly comprises a thermal power generating unit, on the background, the deep peak regulation capacity of the thermal power generating unit is fully exerted in the wind, light, water and fire storage combined system, and meanwhile, the peak clipping and valley filling capacity of the energy storage device is utilized to improve the flexible regulation capacity and renewable energy consumption of the power system, and the wind, light, water and fire storage multi-energy system complementary coordination optimization scheduling method considering the peak regulation activity of the thermal power generating unit is established by combining the wind, light, water and fire storage combined system and the energy storage combined system. The technical problems that the existing adjusting capacity of an electric power system is not easy to meet the requirements of renewable energy consumption and peak regulation and the like are solved.
Drawings
FIG. 1 is a flow chart of a wind, light, water, fire and energy storage multi-energy system complementary coordination optimization scheduling method considering peak regulation initiative, which is disclosed by the invention;
FIG. 2 is a schematic diagram of a solution strategy of a hierarchical scheduling model of a federated system;
FIG. 3 is a schematic diagram of wind power, photovoltaic predicted power and load prediction curves according to an embodiment;
fig. 4-5 are schematic diagrams of optimal scheduling results of each unit before and after the peak shaving initiative is considered in the scene 3 in the specific embodiment;
fig. 6 to 7 are schematic diagrams of optimal scheduling results of each unit before and after the peak shaving initiative is considered in the scene 4 in the specific embodiment.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the invention discloses a wind, light, water, fire and storage system complementary coordination optimization scheduling method considering peak regulation initiative, which mainly comprises the following steps:
s1, providing a unit peak regulation compensation and allocation model taking thermal power, hydropower, wind power, photovoltaic and energy storage into consideration to participate in power grid dispatching, wherein the aim is to maximize the total benefit of the system;
s2, establishing a wind-light-water-fire-storage multi-energy system complementary coordination optimization scheduling model taking minimum net load fluctuation, minimum system operation cost and minimum renewable energy power consumption as optimization targets;
s3, a layered optimization scheduling scheme is provided to simplify the calculation complexity of multi-energy complementary coordination scheduling, fully utilize the peak clipping and valley filling characteristics of an energy storage system and exert the deep peak shaving capacity of the thermal power generating unit;
and S4, based on the decomposition coordination idea, deciding the charging and discharging power of the energy storage system and optimizing a peak regulation strategy through coordination of the upper layer problem and the lower layer problem and alternate iterative solution of the lower layer problem.
The invention firstly provides a unit peak regulation compensation and allocation model which takes the thermal power, hydroelectric power, wind power, photovoltaic and energy storage systems into consideration to participate in power grid dispatching and aims at maximizing the total benefits of the system. And a wind, light, water, fire and energy storage multi-energy system complementary coordination optimization scheduling model is established by taking minimization of net load fluctuation, minimization of system operation cost and minimization of renewable energy power consumption as optimization targets. And then, providing a hierarchical optimization scheduling scheme, and deciding the charge and discharge power of the energy storage system and an optimization peak regulation strategy through coordination of the upper layer problem and the lower layer problem and alternate iterative solution based on a decomposition coordination idea. Finally, the effectiveness and the applicability of the proposed model are verified through a simulation example.
In specific implementation, the step S1 includes the following steps:
s101, establishing a compensation model based on the thermal power generating unit deep peak shaving.
And S102, establishing a peak regulation cost-based allocation model.
S103, considering the peak regulation initiative constraint of the thermal power generating unit, and whether each unit in the system participates in deep peak regulation or not is that whether benefit can be obtained from peak regulation service or not.
In specific implementation, S101 includes the following steps:
at a certain scheduling moment, if a measure for reducing the output of the unit is taken to perform peak shaving, the cost loss of the part needs to be compensated to a certain extent. I is g 、I h 、I wind 、I pv And I s The method comprises the following steps of respectively representing that thermal power, hydropower, wind power, a photovoltaic unit and an energy storage system participate in power grid dispatching, assuming that only the units participate in peak shaving in the whole system, and compensating the units participating in peak shaving according to the electric energy of the peak shaving, wherein the expression is as follows:
wherein the content of the first and second substances,
the peak regulation compensation cost is added for the conventional thermal power generating unit;
compensating the price for the conventional thermal power generating unit by peak regulation;
the peak power is regulated for the conventional thermal power generating unit. Thus, the total peak shaver compensation payout at time t is:
in specific implementation, S102 includes the following steps:
in the deep peak regulation process, peak regulation compensation cost is jointly born by a thermal power generating unit, a wind power generating unit and a photovoltaic unit, the cost of the wind power generating unit and the cost of the photovoltaic unit are shared according to the total power generation electric energy proportion in a deep peak regulation day, the shared part of the thermal power generating unit is shared according to the electric energy proportion of an access power grid, and the expression of the peak regulation compensation cost is as follows:
wherein, the first and the second end of the pipe are connected with each other,
respectively allocating the peak shaving cost of each thermal power generating unit, the wind power plant and the photovoltaic power station at the time t;
the power of the thermal power generating unit i at the moment t is connected to the grid;
the power of the wind power plant at the time t is the power of the grid connection;
and the power is the power of the photovoltaic power station at the moment t.
In specific implementation, S103 includes the following steps:
the peak regulation initiative is mainly to promote the thermal power generating unit to actively participate in peak regulation through reasonable scheduling compensation. For the wind turbine generator and the photovoltaic power station, the deep peak shaving can effectively increase the share of the wind turbine generator and the photovoltaic power station in the electric energy market, and compared with cost sharing, the wind turbine generator and the photovoltaic power station actively participate in the deep peak shaving as long as the cost borne by the wind turbine generator and the photovoltaic power station is lower than the benefit brought by self-increased electric energy.
In specific implementation, the step S2 includes the following steps:
s201, establishing a wind, light, water, fire and energy storage multi-energy system complementary coordination optimization scheduling model with the optimization objectives of minimization of net load fluctuation, maximization of energy storage system operation benefit, minimization of system operation cost and minimization of renewable energy power consumption.
S202, power balance constraint, thermal power unit constraint, wind power unit output constraint, hydroelectric power unit output constraint, photovoltaic power station output constraint, energy storage constraint, line transmission capacity constraint and other series of constraints are constraint conditions of the optimization model.
In specific implementation, S201 includes the following steps:
the net load refers to the residual load actually required to be borne by the thermal power generating unit after the wind, light and water storage combined output is removed from the system. In order to fully utilize the fluctuation of the energy storage system to compensate the wind and light renewable energy sources, the net load fluctuation born by the thermal power generating unit is minimum, the thermal power generating unit is prevented from being adjusted frequently and greatly, and a target function with the minimum net load fluctuation is established:
wherein, P glt Is the net load value at time t;
actual grid-connected power of a wind power plant l at the moment t;
actual grid-connected power of hydropower h at the moment t;
the actual grid-connected power of the photovoltaic m at the time t is obtained; p St The discharge power of the energy storage device at the moment t is the net load average value in a scheduling period; n is a radical of W The total number of the wind power plants; n is a radical of S The total number of the photovoltaic power stations; n is a radical of H The total number of hydropower stations.
In the operation process of the energy storage system, the operation and the environmental benefit of the energy storage system are main factors influencing the operation benefit of the energy storage system, and an energy storage system operation and environmental benefit model is established by considering the operation generated energy benefit of the energy storage system:
wherein p is price The price of electricity is the power grid; eta c And η d Respectively representing the charging efficiency and the discharging efficiency of the energy storage system;
and
respectively the charging and discharging power of the energy storage system at the moment t; m is the total number of pollutant types generated by the superior power grid; p is a radical of formula price,k Is the unit discharge cost of the pollutants; xi shape grid,k And discharging the kth pollutant density for the electric energy of the superior power grid production unit.
Considering the charge-discharge cost of the energy storage system, establishing an operation cost model of the energy storage system:
wherein, c sc The cost coefficient of the charge and discharge power of the energy storage system;
and
respectively the charging power and the discharging power of the energy storage system at the moment t. The maximum objective function of the operating yield of the energy storage system is as follows:
maxf sy ＝f ss -f sc
the coal consumption cost and the start-stop cost of the thermal power generating unit are as follows:
wherein f is 2 The method is the conventional peak shaving operation cost of the thermal power generating unit; f. of mh And f qt Respectively representing the coal consumption cost and the start-stop cost of the thermal power generating unit; a is i 、b i 、c i Respectively representing consumption coefficients of the thermal power generating unit i; p i,t The power is output for the thermal power generating unit.
When the thermal power generating unit carries out deep peak shaving, the running state of the thermal power generating unit deviates from the design value greatly, the generating efficiency is obviously reduced, and extra unit loss and oil feeding cost are generated. Roughly calculating the loss cost of the unit by referring to a Manson-coffee formula, wherein the loss cost of the unit is as follows:
f i,l ＝βS unit,i /(2N f (P))
beta is the influence coefficient of the operation of the thermal power generating unit; s. the unit,i Purchasing cost for the ith thermal power generating unit; n is a radical of hydrogen f (P) represents the number of rotor cracking cycle cycles determined from the rotor low cycle fatigue curve.
In the stage of oil feeding depth peak regulation, additional peak regulation cost for oil feeding combustion supporting is generated, and the cost is as follows:
f y,i,t ＝Q oil,i,t p oil
Q oil,i,t the oil input amount is the oil input amount of the ith unit at the deep peak regulation stage at the time t; p is a radical of oil Is the current season oil price.
The thermal power generating unit can express the operation cost by sections according to different operation states:
wherein, P i,min And P i,max Respectively the minimum output and the maximum output of the thermal power generating unit; p b For the stable combustion limit load value during deep peak regulation (oil injection) of the unit；P a The stable combustion load value is the stable combustion load value when the unit is subjected to deep peak regulation (oil is not injected).
The consumption capacity of the renewable energy is represented by the sum of the wind power abandonment, the water power abandonment and the light power abandonment in the scheduling period, and the more the wind power abandonment, the light abandonment and the water abandonment, the weaker the consumption capacity of the renewable energy is.
In specific implementation, S202 includes the following steps:
the optimization model in S201 should satisfy a series of constraints,
and (3) power balance constraint:
wherein, the first and the second end of the pipe are connected with each other,
is the load of the j node at time t.
And (3) constraint of the thermal power generating unit:
u it P i,min ＜P i,t ≤u it P i,max
u it P ib ＜P i,t ≤u it P i,max
-r i,down ≤P i,t -P i,t-1 ≤r i,up
wherein r is i,up And r i,down The maximum upward and downward climbing rates of the thermal power generating unit are respectively.
Output restraint of the wind turbine generator:
output restraint of the hydroelectric generating set:
photovoltaic power station output restraint:
and (4) restraining the stored energy:
energy storage charging and discharging restraint:
line transmission capacity constraint:
wherein G is l,m The transfer distribution factor for node m to line l.
The active power is injected into the scene s for the node m in the t period; m is the number of nodes.
In specific implementation, the step S3 includes the following steps:
s301, a hierarchical optimization scheduling scheme is provided. The upper-layer scheduling model optimizes the wind, light and water storage combined system output model by using the rapid throughput capacity of the energy storage system to follow the fluctuation of wind power, photovoltaic and load and taking the minimization of net load fluctuation and the maximization of the operation income of the energy storage system as targets so as to reduce the load shifting pressure of the thermal power generating unit. And the upper layer model adopts a particle swarm optimization algorithm to carry out model solution, so as to obtain the optimal output model of the wind, light, water and storage combined system, which minimizes net load fluctuation and maximizes the operating yield of the energy storage system.
And S302, the lower layer model calls a CPLEX tool to solve through an MATLAB platform by taking minimization of the operation cost of the thermal power unit and minimization of wind abandon, light abandon and water abandon as targets through an equivalent load curve of the upper layer model and combining penalty cost of the wind abandon and the light abandon, peak load regulation cost and operation characteristics of each unit.
When the concrete implementation, still include:
and S4, based on a decomposition coordination idea, determining the charge and discharge power of the energy storage system and optimizing a peak regulation strategy through coordination of the upper layer problem and the lower layer problem and alternative iterative solution of the lower layer problem.
The following are specific examples of evaluations carried out using the method of the present invention:
in order to verify the effectiveness of the model, an improved IEEE30 node algorithm is adopted for simulation, and the system comprises 5 thermal power generating units, 1 hydroelectric generating unit of 100MW, 1 wind generating unit of 300MW total capacity and 1 photovoltaic power station of 50MW total capacity. And the capacity of the energy storage system is configured by adopting a lithium iron phosphate battery in a modular assembly mode. Spinning reserve was 10% of load. The power price of the thermal power grid is 375 yuan/(MW & h), the power price of the wind power grid pole is 570 yuan/(MW & h), the power price of the thermal power depth peak regulation compensation is 500 yuan/(MW & h), and the wind abandonment penalty coefficient is 500 yuan/(MW & h). The wind power and load curves are shown in fig. 3.
In order to verify the effectiveness of the model provided by the invention, 4 scheduling scenes are used for comparison, and the system operation economy and the renewable energy consumption level under different operation modes are analyzed.
Scene 1: wind, light and water are jointly operated, energy storage is not considered, and the peak shaving is conventionally performed on the thermal power generating unit.
Scene 2: wind, light, water and storage are operated in a combined mode, and the peak shaving is carried out on the thermal power generating unit conventionally.
Scene 3: wind, light, water and storage are operated in a combined mode, and the peak shaving of the thermal power generating unit is performed deeply.
Scene 4: wind, light and water are jointly operated, energy storage is not considered, and the thermal power generating unit carries out deep peak shaving.
Analyzing simulation results of the scenes 1 and 2, and when wind power, photovoltaic power and hydropower are integrated into a power grid, the equivalent load fluctuation variance is large, and the equivalent load peak-valley difference is large; when the wind, light and water storage combined operation is carried out after the energy storage access, the equivalent load fluctuation variance is small, the equivalent load peak-valley difference is small, the equivalent load fluctuation variance and the load peak-valley difference after the energy storage access are both relatively reduced, the peak clipping and valley filling capacity is limited due to the capacity of an energy storage system, the wind, light and water storage combined system cannot completely inhibit the fluctuation of the load, and the system still faces large peak-valley difference and strong load fluctuation.
The simulation results of the scenes 1 and 3 are analyzed, the access of an energy storage system and the deep peak regulation operation condition of the thermal power generating unit are considered, when the thermal power generating unit carries out deep peak regulation, on the premise that the total operation cost of the system is the lowest, the wind abandoning rate is relatively reduced, and the promotion effect of the deep peak regulation on wind power is more obvious. From the perspective of peak regulation benefit, the energy storage reduces the air volume abandoned by the system while improving the load peak-valley difference, and the comprehensive benefit is better.
The simulation results of the scenes 1 and 4 are analyzed, from the perspective of the system peak regulation effect, the deep peak regulation improves the flexibility of the system, the starting and stopping cost of the system is reduced on the premise of ensuring the lowest total operation cost of the system, and the wind abandoning rate is reduced relative to the scene 1.
For further analysis of the effectiveness of the model provided herein, taking scenario 3 and scenario 4 as an example, the operation costs and profits of the thermal power generating units before and after peak shaving initiative and the change situation of the profit of the wind farm are analyzed and considered, as shown in fig. 4-7.
Compared with the system optimization scheduling results before and after the peak regulation initiative constraint is considered in the scene 3 in fig. 4-5, it can be seen that before the peak regulation initiative constraint is considered, the thermal power unit 1 operates in the deep peak regulation state in the following time periods of 2-00-4 and 21-00-24, the thermal power unit 3, 4 operates in the deep peak regulation state in the time period of 22.
Comparing before and after the energy storage access of the scene 3 and the scene 4 in fig. 4-7, considering the system optimization scheduling result of the peak shaving initiative constraint, the scene 4 does not consider the energy storage system, the thermal power unit 1 operates in the deep peak shaving state at the time interval of 2-00-4 and 23-00.
Claims (9)
1. A wind, light, water and fire storage system complementary coordination optimization scheduling method considering peak regulation initiative comprises the following steps:
s1, aiming at maximizing the total benefits of a system, considering a peak regulation compensation and allocation model of a unit which participates in power grid dispatching by thermal power, hydropower, wind power, photovoltaic and energy storage;
s2, establishing a wind-light-water-fire-storage multi-energy system complementary coordination optimization scheduling model taking minimum net load fluctuation, minimum system operation cost and minimum renewable energy power consumption as optimization targets;
s3, establishing a hierarchical optimization scheduling scheme to simplify the calculation complexity of multi-energy complementary coordination scheduling, and utilizing the peak clipping and valley filling characteristics of the energy storage system and exerting the deep peak-shaving capacity of the thermal power generating unit;
and S4, based on a decomposition coordination idea, determining the charge and discharge power of the energy storage system and optimizing a peak regulation strategy through coordination of the upper layer problem and the lower layer problem and alternative iterative solution of the lower layer problem.
2. The wind, light, water, fire and storage system complementary coordination optimization scheduling method considering peak regulation initiative of claim 1, characterized in that: the method for the peak regulation compensation and allocation model of the unit taking thermal power, hydropower, wind power, photovoltaic and energy storage into consideration to participate in power grid dispatching by taking the total benefit of the system as the maximum target in the step 1 comprises the following steps:
s101, establishing a compensation model based on deep peak shaving of a thermal power generating unit;
s102, establishing a peak regulation cost-based allocation model;
and S103, considering the peak regulation initiative constraint of the thermal power generating unit, wherein the key of whether each unit in the system participates in deep peak regulation is whether the unit can benefit from peak regulation service.
3. A method as claimed in claim 2The wind, light, water, fire and storage system complementary coordination optimization scheduling method considering peak regulation initiative is characterized by comprising the following steps of: s101, the method for establishing the compensation model based on the thermal power generating unit deep peak shaving comprises the following steps: at a certain scheduling moment, if a measure for reducing the output of the unit is taken to carry out peak shaving, the cost loss of the part obtains certain compensation; i is g 、I h 、I wind 、I pv And I s The method comprises the following steps of respectively representing that thermal power, hydropower, wind power, photovoltaic units and an energy storage system participate in power grid dispatching, assuming that only the units of the whole system participate in peak shaving, and compensating the units participating in peak shaving according to the electric energy of the peak shaving, wherein the expression is as follows:
in the formula:
the peak regulation compensation cost is added for the conventional thermal power generating unit;
compensating the price for the conventional thermal power generating unit by peak regulation;
regulating peak power for a conventional thermal power generating unit; thus, the total peak shaver compensation payout at time t is:
4. the wind, light, water, fire and storage system complementary coordination optimization scheduling method considering peak shaving initiative of claim 3, wherein: s102, in the deep peak regulation process, peak regulation compensation cost is shared by a thermal power generating unit, a wind power generating unit and a photovoltaic unit, the cost of the wind power generating unit and the cost of the photovoltaic unit are shared according to the proportion of the total generated electric energy in a deep peak regulation day, the shared part of the thermal power generating unit is shared according to the proportion of the electric energy accessed to a power grid, and the expression is as follows:
wherein the content of the first and second substances,
respectively allocating the peak shaving cost of each thermal power generating unit, the wind power plant and the photovoltaic power station at the time t;
the power of the thermal power generating unit i at the moment t is connected to the grid;
the power of the wind power plant at the time t is the power of the grid connection;
the power of the photovoltaic power station at the moment t is the power of the photovoltaic power station connected with the network.
5. The wind, light, water, fire and storage system complementary coordination optimization scheduling method considering peak regulation initiative of claim 4, characterized in that: s103, considering the peak regulation initiative constraint of the thermal power generating unit, the key of whether each unit in the system participates in deep peak regulation or not is whether the method can benefit from peak regulation service or not, and the method comprises the following steps: the peak regulation initiative promotes the thermal power generating unit to actively participate in peak regulation through scheduling compensation; for the wind turbine generator and the photovoltaic power station, the deep peak shaving effectively increases the share of the wind turbine generator and the photovoltaic power station in the electric energy market, and compared with cost sharing, the wind turbine generator and the photovoltaic power station actively participate in the deep peak shaving as long as the cost of the sharing is lower than the benefit brought by self-increased electric energy.
6. The wind, light, water, fire and storage system complementary coordination optimization scheduling method considering peak regulation initiative of claim 1, characterized in that: s2, establishing a wind-light-water-fire-storage multi-energy system complementary coordination optimization scheduling model taking minimum net load fluctuation, minimum system operation cost and minimum renewable energy power consumption as optimization targets, and comprising the following steps of:
s201, establishing a wind-light-water-fire-storage multi-energy system complementary coordination optimization scheduling model by taking minimization of net load fluctuation, maximization of energy storage system operation benefit, minimization of system operation cost and minimization of renewable energy power consumption as optimization targets;
s202, power balance constraint, thermal power unit constraint, wind power unit output constraint, hydroelectric power unit output constraint, photovoltaic power station output constraint, energy storage constraint and line transmission capacity constraint are used as constraint conditions of the optimization model.
7. The wind, light, water, fire and storage system complementary coordination optimization scheduling method considering peak regulation initiative of claim 6, characterized in that: s201, the method for establishing the wind, light, water, fire and energy storage multi-energy system complementary coordination optimization scheduling model comprises the following steps:
establishing a net load fluctuation minimum objective function:
in the formula: p glt Is the net load value at time t;
actual grid-connected power of the wind power plant l at the moment t;
actual grid-connected power of hydropower h at time t;
the actual grid-connected power of the photovoltaic m at the time t is obtained; p St The discharge power of the energy storage device at the moment t is the net load average value in a scheduling period; n is a radical of W The total number of the wind power plants; n is a radical of S The total number of the photovoltaic power stations; n is a radical of H The total number of the hydropower stations;
and (3) considering the operating power generation yield of the energy storage system, establishing an energy storage system operation and environment yield model:
wherein p is price The price of electricity is the power grid; eta c And η d Respectively representing the charging efficiency and the discharging efficiency of the energy storage system;
and
the charging power and the discharging power of the energy storage system at the moment t are respectively; m is the total number of pollutant types generated by the superior power grid; p is a radical of price,k Is the unit discharge cost of the pollutants; xi shape grid,k Discharging the kth pollutant density for the electric energy of a superior power grid production unit;
considering the charge and discharge cost of the energy storage system, establishing an operation cost model of the energy storage system:
wherein, c sc A charge-discharge power cost coefficient for the energy storage system;
and
the charging power and the discharging power of the energy storage system at the moment t are respectively; the maximum objective function of the operating yield of the energy storage system is as follows:
max f sy ＝f ss -f sc
the coal consumption cost and the start-stop cost of the thermal power generating unit are as follows:
wherein f is 2 The method is the conventional peak shaving operation cost of the thermal power generating unit; f. of mh And f qt Respectively representing the coal consumption cost and the start-stop cost of the thermal power generating unit; a is a i 、b i 、c i Respectively representing consumption coefficients of the thermal power generating unit i; p is i,t Outputting power for the thermal power generating unit;
and (3) calculating the unit loss cost:
f i，l ＝βS unit，i /(2N f (P))
beta is the influence coefficient of the operation of the thermal power generating unit; s unit,i Purchasing cost for the ith thermal power generating unit; n is a radical of f (P) representing the number of rotor cracking cycle cycles determined from the rotor low cycle fatigue curve;
in the stage of oil feeding depth peak regulation, additional peak regulation cost for oil feeding combustion supporting is generated, and the cost is as follows:
f y,i,t ＝Q oil,i,t p oil
Q oil,i,t for the oil input at the deep peak regulation stage of the ith unit at the time t oil Is the current season oil price;
the thermal power generating unit can express the operation cost by sections according to different operation states:
wherein, P i,min And P i,max Respectively the minimum output and the maximum output of the thermal power generating unit; p b The stable combustion limit load value is the stable combustion limit load value when the unit is subjected to deep peak regulation (oil injection); p a The stable combustion load value is the stable combustion load value when the unit is subjected to deep peak shaving (oil is not injected);
the consumption capacity of the renewable energy is represented by the sum of the wind curtailment electricity quantity, the water curtailment electricity quantity and the light curtailment electricity quantity in the scheduling period.
8. The wind, light, water, fire and storage system complementary coordination optimization scheduling method considering peak shaving initiative of claim 6, wherein: step S202, the constraint condition includes:
and power balance constraint:
wherein the content of the first and second substances,
is the load of the j node at time t;
and (3) constraint of the thermal power generating unit:
u it P i,min ＜P i,t ≤u it P i,max
u it P ib ＜P i,t ≤u it P i,max
-r i,down ≤P i,t -P i,t-1 ≤r i,up
wherein r is i,up And r i,down Respectively realizing the maximum upward and downward climbing rates of the thermal power generating unit;
output restraint of the wind turbine generator:
output restraint of the hydroelectric generating set:
photovoltaic power station output restraint:
and (4) restraining the stored energy:
energy storage charge-discharge restraint:
line transmission capacity constraint:
wherein, G l,m A transfer distribution factor for node m to line l;
active injection power is provided for the node m to the scene s in the t period; m is the number of nodes.
9. The wind, light, water, fire and storage system complementary coordination optimization scheduling method considering peak regulation initiative of claim 1, characterized in that: s3, the method for establishing the hierarchical optimization scheduling scheme to simplify the calculation complexity of the multi-energy complementary coordination scheduling, utilizing the peak clipping and valley filling characteristics of the energy storage system and exerting the deep peak regulation capability of the thermal power generating unit comprises the following steps:
s301, a layered optimization scheduling scheme is provided, an upper-layer scheduling model utilizes the throughput capacity of an energy storage system to follow the fluctuation of wind power, photovoltaic and load, optimizing a wind, light, water and storage combined system output model by taking minimization of net load fluctuation and maximization of operation benefit of an energy storage system as targets so as to reduce load peak clipping and valley filling pressure of a thermal power generating unit; the upper layer model adopts a particle swarm optimization algorithm to carry out model solution, and a wind, light, water and storage combined system optimal output model with minimized net load fluctuation and maximized energy storage system operation income is obtained;
and S302, the lower layer model calls a CPLEX tool to solve through an MATLAB platform by taking minimization of the operation cost of the thermal power unit and minimization of wind abandon, light abandon and water abandon as targets through an equivalent load curve of the upper layer model and combining penalty cost of the wind abandon and the light abandon, peak load regulation cost and operation characteristics of each unit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211407305.5A CN115689233A (en) | 2022-11-10 | 2022-11-10 | Wind, light, water, fire and storage system complementary coordination optimization scheduling method considering peak regulation initiative |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211407305.5A CN115689233A (en) | 2022-11-10 | 2022-11-10 | Wind, light, water, fire and storage system complementary coordination optimization scheduling method considering peak regulation initiative |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115689233A true CN115689233A (en) | 2023-02-03 |
Family
ID=85052560
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211407305.5A Pending CN115689233A (en) | 2022-11-10 | 2022-11-10 | Wind, light, water, fire and storage system complementary coordination optimization scheduling method considering peak regulation initiative |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115689233A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116742724A (en) * | 2023-08-16 | 2023-09-12 | 杭州太阁未名科技有限公司 | Active power distribution network optimal scheduling method and device, computer equipment and storage medium |
| CN116823000A (en) * | 2023-08-31 | 2023-09-29 | 华能澜沧江水电股份有限公司 | Hydropower compensation peak regulation analysis and evaluation method and device thereof |
| CN116826867A (en) * | 2023-08-30 | 2023-09-29 | 国网湖北省电力有限公司经济技术研究院 | Optimized scheduling and cost compensation allocation method for improving source load multielement peak regulation initiative |
-
2022
- 2022-11-10 CN CN202211407305.5A patent/CN115689233A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116742724A (en) * | 2023-08-16 | 2023-09-12 | 杭州太阁未名科技有限公司 | Active power distribution network optimal scheduling method and device, computer equipment and storage medium |
| CN116742724B (en) * | 2023-08-16 | 2023-11-03 | 杭州太阁未名科技有限公司 | Active power distribution network optimal scheduling method and device, computer equipment and storage medium |
| CN116826867A (en) * | 2023-08-30 | 2023-09-29 | 国网湖北省电力有限公司经济技术研究院 | Optimized scheduling and cost compensation allocation method for improving source load multielement peak regulation initiative |
| CN116826867B (en) * | 2023-08-30 | 2023-12-15 | 国网湖北省电力有限公司经济技术研究院 | Optimized scheduling and cost compensation allocation method for improving source load multielement peak regulation initiative |
| CN116823000A (en) * | 2023-08-31 | 2023-09-29 | 华能澜沧江水电股份有限公司 | Hydropower compensation peak regulation analysis and evaluation method and device thereof |
| CN116823000B (en) * | 2023-08-31 | 2024-01-19 | 华能澜沧江水电股份有限公司 | Hydropower compensation peak regulation analysis and evaluation method and device thereof |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111738497B (en) | Virtual power plant double-layer optimal scheduling method considering demand side response | |
| CN115689233A (en) | Wind, light, water, fire and storage system complementary coordination optimization scheduling method considering peak regulation initiative | |
| CN105048516A (en) | Wind-light-water-fire multi-source complementary optimization scheduling method | |
| CN111181198B (en) | Heterogeneous energy complementary power generation scheduling method based on network source mutual feedback | |
| CN112395748B (en) | Power system rotation reserve capacity optimization method considering supply and demand double-side flexible resources | |
| CN114865631B (en) | Optimal distribution robust economic scheduling method for source-load cooperative carbon reduction integrated energy system | |
| CN109993345B (en) | Garden-oriented dynamic economic dispatching method for multi-energy complementary system for island operation | |
| CN112086960B (en) | Model prediction control-based flexible margin calculation method for electro-hydrogen coupling system | |
| CN113036750A (en) | Power grid peak regulation resource coordination optimization method containing energy storage power station | |
| CN116599148A (en) | Hydrogen-electricity hybrid energy storage two-stage collaborative planning method for new energy consumption | |
| CN116780646A (en) | Power system resource optimization scheduling method considering flexibility and terminal | |
| CN112653137A (en) | Photothermal power station and wind power system considering carbon transaction, and low-carbon scheduling method and system | |
| CN116979578A (en) | Electric and thermal triple generation optimal scheduling method and system for wind, light, water and fire storage | |
| CN112311017A (en) | Optimal collaborative scheduling method for virtual power plant and main network | |
| CN109873419B (en) | Water-light storage system operation optimization method considering similarity and economic benefits | |
| CN116805192A (en) | Comprehensive energy system double-layer planning optimization method considering optimal energy rejection rate and application thereof | |
| CN113078687B (en) | Energy optimization scheduling method for island multi-energy complementary electricity-gas coupling system | |
| CN115882460A (en) | Two-stage new energy micro-grid optimization scheduling method considering demand side management | |
| CN114465226A (en) | Method for establishing multi-level standby acquisition joint optimization model of power system | |
| CN112182915A (en) | Optimized scheduling method and system for cooperatively promoting wind power consumption | |
| CN111769602B (en) | Optimized scheduling method for multi-time-scale wind storage combined system | |
| CN115513941A (en) | Adjustable robust interval optimization method of electricity-heat comprehensive energy system considering hydrogen energy storage | |
| Rong et al. | Economic Analysis of Integrated Energy Systems Based on Synergistic Optimization of Flexible Loads and Energy Storage | |
| Gan et al. | Bi-level Iterative Planning of Integrated Energy System for Renewable Energy Consumption | |
| CN117277373A (en) | Variable-period source load storage optimal scheduling method considering adjustment resource demand difference |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |