CN113435659B - Scene analysis-based two-stage optimized operation method and system for comprehensive energy system - Google Patents
Scene analysis-based two-stage optimized operation method and system for comprehensive energy system Download PDFInfo
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Abstract
The invention discloses a scene analysis-based two-stage optimization operation method and a scene analysis-based two-stage optimization operation system for a comprehensive energy system, wherein the method comprises the following steps of: constructing a day-ahead stage optimization model by taking the lowest day-ahead operation cost as an objective function based on the prediction reference values of each energy and load; constructing an intra-day phase optimization model according to the objective function with the smallest increment expected value or the largest decrement expected value to the cost before day in an intra-day actual operation scene; obtaining a global optimal operation scheme through a day-ahead stage optimization model and a day-in stage optimization model; the actual operation scene is reduced by a multi-parameter fast forward selection method so as to improve the optimization speed. The invention has the advantages of high optimization speed, good economy, good inclusion and the like.
Description
Technical Field
The invention mainly relates to the technical field of energy optimization, in particular to a scene analysis-based two-stage optimization operation method and system for a comprehensive energy system.
Background
Due to the obvious difference of physical characteristics, market mechanisms, informatization and automation levels of all subsystems of the comprehensive energy system, the method adopts independent planning, construction, operation and management of different units for a long time, and the optimization of the whole energy system is not considered globally. The distributed energy becomes the core of the terminal integrated energy supply system with the advantage of close load, the dependence of the system on a main network is reduced, and therefore the expansion investment of a distribution network is reduced to a certain extent. However, the volatility, intermittency and unpredictability of the load of the distributed renewable energy sources pose a serious challenge to the reliability of the energy system and the system balance, and how to stabilize the negative effects by responding to fast energy sources such as distributed natural gas units, energy storage devices and the like becomes a key point.
At present, with continuous expansion of the scale of a power grid, the structure of the power grid is increasingly complex, and the problems that the peak shaving capacity of a power system is insufficient and the large-scale grid-connected consumption of clean energy is difficult to adapt exist. The problems of wind abandonment, water abandonment and light abandonment in partial areas are serious, and the development of distributed energy sources faces a bottleneck. In order to solve the problem, the key point is to make an economic dispatching plan of source load formulated through short-term distributed power supply and load prediction, how to reduce the adverse effect of distributed power supply output uncertainty on unit operation cost and limit the load loss influence caused by uncertainty, and the economic dispatching plan is always an important research problem of comprehensive energy optimization dispatching.
With the enhancement of uncertainty in the operation of the comprehensive energy system, if a certain deterministic unit combination mode is adopted, the effectiveness of the decision is difficult to guarantee, and various operation forms possibly presented by the system under an uncertain condition and different corresponding operation effects must be considered in the unit combination decision. Essentially, the prediction of distributed renewable energy output and compliance belongs to the random set combination problem that minimizes a certain quantile or its expectation of induced system cost distribution using probabilistic models of uncertain input factors (such as demand, equipment failure, and partially predictable renewable energy generation). Typically, such a probabilistic model is approximated by a set of scenarios describing reasonable realizations of these stochastic factors. In order for the random solution to be reliable, the number of scenes to be considered must be large, which may lead to problematic optimization problems.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the problems in the prior art, the invention provides a two-stage optimization operation method and a two-stage optimization operation system of a comprehensive energy system based on scene analysis, which have the advantages of high optimization speed, low operation cost and good containment.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
a comprehensive energy system two-stage optimization operation method based on scene analysis comprises the following steps:
constructing a day-ahead stage optimization model by taking the lowest day-ahead operation cost as an objective function based on the prediction reference values of each energy and load;
constructing an intra-day stage optimization model according to an objective function with the minimum increment expected value or the maximum decrement expected value of the cost before the day in an intra-day actual operation scene;
obtaining a global optimal operation scheme through a day-ahead stage optimization model and a day-in stage optimization model;
the actual operation scene is reduced by a multi-parameter quick forward selection method so as to improve the optimization speed.
As a further improvement of the above technical solution:
the multi-parameter rapid forward selection method comprises the following specific steps:
1) Acquiring an original scene, and establishing a three-dimensional data space by taking the electric load predicted value as an X axis, the heat load predicted value as a Y axis and the photovoltaic electric output predicted value as a Z axis;
2) Calculating the spatial distance between each scene point and any other scene point;
3) Calculating the Kantorovich distance of each scene point, and updating and reducing a scene set;
4) Calculating the distance between any two remaining scene points in the reduced scene set, calculating the Kantorovich distance of each scene point, and updating the reduced scene set until the reduced scene number reaches the preset number;
5) And updating the scenes, and calculating the probability of each reserved scene after aggregation to form a typical cut scene set.
In step 1), the predicted values of the electric load, the thermal load and the photovoltaic power output are obtained through corresponding predicted reference values and predicted error values, wherein the predicted reference values are determined through analysis of the sunshine condition, the weather condition, the historical load data and the load growth direction of the region.
The decision variables of the optimization model at the day-ahead stage comprise one or more of electric output of the backpressure unit, heat output of the backpressure unit, electric output of the extraction and coagulation unit, heat output of the extraction and coagulation unit, charge and discharge power of the energy storage power station, electric quantity of the energy storage power station and power exchange between the comprehensive energy system and the main grid.
The constraint conditions of the day-ahead stage optimization model comprise one or more of an integrated energy system and main network power exchange constraint condition, a back pressure unit thermal power range constraint condition, a constraint relation between electric power and thermal power of a back pressure unit under a thermal power control rule, a pumping condensing unit thermoelectric power range constraint condition, a two-gas unit thermoelectric power climbing rate limitation constraint condition, an energy storage power station charging and discharging depth limitation constraint condition, an energy storage power station charging and discharging power upper and lower limit constraint condition, an energy storage power station electric quantity and charging power relation constraint condition and a thermoelectric balance constraint condition in the integrated energy system.
The decision variables of the optimization model in the day phase comprise one or more of the electric output variable quantity of the backpressure unit, the thermal output variable quantity of the backpressure unit, the electric output variable quantity of the pumping and condensing unit, the thermal output variable quantity of the pumping and condensing unit, the charge and discharge power variable quantity of the energy storage power station, the real-time electric quantity of the power station in the day phase and the electric power exchange variable quantity between the comprehensive energy system and the main network.
And the constraint conditions of the day-interior phase optimization model are the constraint conditions which meet the day-ahead phase under different scenes.
The invention also discloses a comprehensive energy system two-stage optimization operation system based on scene analysis, which comprises the following steps:
the first module is used for constructing a day-ahead stage optimization model by taking the lowest day-ahead operation cost as an objective function based on the prediction reference values of all energy sources and loads;
the second module is used for constructing an intra-day stage optimization model according to the objective function with the minimum increment expected value or the maximum decrement expected value of the cost before the day in the actual operation scene in the day; the actual operation scene is reduced by a multi-parameter rapid forward selection method so as to improve the optimization speed;
and the third module is used for obtaining a global optimal operation scheme through the day-ahead phase optimization model and the day-in phase optimization model.
The invention further discloses a computer-readable storage medium on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for two-stage optimized operation of an integrated energy system based on scene analysis as described above.
The invention also discloses computer equipment which comprises a memory and a processor, wherein the memory is stored with a computer program, and the computer program executes the steps of the comprehensive energy system two-stage optimization operation method based on the scene analysis when being operated by the processor.
Compared with the prior art, the invention has the advantages that:
the invention adopts a day-ahead and day-inside two-stage optimization operation strategy, and mainly solves the problem of a comprehensive energy system comprising a 'one-pumping-one-back' distributed natural gas energy station, a distributed photovoltaic power station and an energy storage power station.
The invention adopts two-stage optimization to obtain the optimal scheduling scheme under an expected scene and the feasible scheduling scheme with the minimum system operation cost under any scene, and has stronger robustness due to the consideration of random output; according to the characteristics of thermoelectric loads at different stages and the respective operating characteristics of the backpressure pumping condensing unit, the two-stage optimization is adopted to dynamically adjust the output of the unit, so that the aim of fully utilizing the economic operation of the unit is fulfilled; the invention can realize higher primary energy conversion efficiency, reduce peak load regulation pressure of the external network and indirectly save the investment of capacity-increasing transformation of the power distribution network; according to the method, a large number of original random samples are obtained by adopting a Monte Carlo method, scenes with high occurrence frequency are reserved by adopting a multi-parameter rapid forward selection method, the scenes selected by a scene screening algorithm have the characteristics of small quantity but more typical characteristic, the model solving speed can be improved, and a practical optimization solution is provided.
Drawings
FIG. 1 is a flow chart of an embodiment of the method of the present invention.
Fig. 2 is a graph showing probability distribution and data probability distribution characteristics of a scene after probability reduction and an original scene.
FIG. 3 is a schematic diagram of various stages of operation of the devices of the present invention; wherein (a) is a refrigeration period gas unit operation strategy diagram; (b) is a heating period gas unit operation strategy diagram; (c) is a refrigeration period energy storage power station operation strategy diagram; and (d) is an operation strategy diagram of the energy storage power station in the heating period.
Detailed Description
The invention is further described below with reference to the figures and the specific embodiments of the description.
As shown in fig. 1, the two-stage optimization operation method of the comprehensive energy system based on the scene analysis of the embodiment includes the steps of:
based on the prediction reference values of all energy sources and loads, constructing a day-ahead stage optimization model by taking the lowest day-ahead operation cost as a target function;
constructing an intra-day stage optimization model according to an objective function with the minimum increment expected value or the maximum decrement expected value of the cost before the day in an intra-day actual operation scene;
obtaining a global optimal operation scheme through a day-ahead stage optimization model and a day-in stage optimization model;
the actual operation scene is reduced by a multi-parameter fast forward selection method so as to improve the optimization speed.
The invention adopts a day-ahead and day-inside two-stage optimization operation strategy, and mainly solves the problem of a comprehensive energy system comprising a 'one-pumping-one-back' distributed natural gas energy station, a distributed photovoltaic power station and an energy storage power station.
In a specific embodiment, the number of samples required for describing the random unit combination problem is large, in order to improve the calculation efficiency, the samples need to be reduced, and the sample precision can be fitted to the maximum extent at the same time, in the embodiment, a multi-parameter-fast forward selection method is innovatively adopted for scene reduction, and the specific steps are as follows:
step 1: generating an original scene set omega, each scene s E omega containing
Wherein
And
the electric load level, the thermal load level and the photovoltaic power output with prediction errors are respectively, and data are simulated by adopting a truncation distribution method, so that the data conform to truncation normal distribution, and the expressions are shown as follows:
wherein, the first and the second end of the pipe are connected with each other,
and
the method is characterized by comprising the following steps of (1) representing the prediction reference values of photovoltaic power output, electric load and heat load within 24 hours on a typical day; standard deviation of
Representative is a predicted error value; s is a scene of the prediction data, and the total scene number represents the sample capacity of the prediction data; t is a 24 hour period of the day. The prediction reference value is determined by analyzing the sunshine condition, the weather condition, the historical load data and the load increasing direction of the region, and accords with the characteristics of the region.
And generating a large number of pseudo random numbers according to the predicted reference value and the error value to simulate the possible situations of photovoltaic output, electrical load level and thermal load level in the actual operation of the system, and when the scene of the sample reaches a certain number, considering that the predicted data has high reliability and is close to various possible situations in the actual production.
And then establishing a three-dimensional data space by taking the predicted value of the electric load as an X axis, the predicted value of the heat load as a Y axis and the predicted value of the photovoltaic output as a Z axis.
Step 2: calculating the spatial distance between each scene point and any other scene point:
and 3, step 3: calculating the Kantorovich distance of each scene point s:
selection of s 1 ∈arg min s∈Ω KD s Updating and deleting scene set J ← omega \ s 1 }……
Step i: calculating the distance between any two remaining scene points in the deleted scene set J:
calculating the Kantorovich distance of each scene point:
selecting
Updating deleted scene set J i ←J i-1 \{s i }；
Step i +1: repeating the step i until the number of the deleted scenes reaches the design requirement, and updating the following sets:
J * ＝J i+1 S * ＝Ω\J * (6)
calculating the probability of each remaining scene after aggregation:
the initial scene set in step 1 has a large number of scenes N Ω, and the probability of each scene is the same 1/N Ω. After three parameters are coordinated, each scene corresponds to a unique point in a three-dimensional data space, the spatial distance between any two points can be calculated through the formula (2) in the step 2, then the Kantorovich distance of each point is calculated through the formula (3) in the step 3, after representative scenes with the target number (such as 100 scenes) are selected in a repeated iteration mode, the probabilities of the scenes represented by the representative scenes are added, and the probabilities are the new probabilities of the representative scenes
Forming a representative reduced scene set S * 。
In one embodiment, the operation of the integrated energy source is optimized to optimize energy efficiency by optimizing the output of the distributed primary energy source while satisfying the energy demand of the user. For this purpose, a two-stage optimization operation method is adopted, and in the first stage, the output of the unit, the charging and discharging characteristics of the energy storage power station and the power exchange between the comprehensive energy system and the main network are used as input; and the second stage considers the uncertainty of the light output and the load in the day operation stage based on the optimized data of the first stage.
In a particular embodiment, the day-ahead phase optimization model is based on a predicted baseline value for source-load with an optimization objective of day-ahead operating cost minimization.
Decision variables for the day-ahead optimization phase include: 1) Electric output of back pressure unit
Thermal output
2) Electric power output of pumping and condensing unit
Thermal output
3) Charging and discharging power of energy storage power station
Electric quantity of power station
4) Power exchange between integrated energy system and main network
The day-ahead optimization stage objective function is:
wherein
Respectively the cost of the unit electric output and the heat output of the back pressure unit,
the cost of unit electric output and thermal output of the extraction condensing unit are respectively,
the cost of the power interaction between the comprehensive energy system and the main network is reduced.
The following constraints are respectively followed: the comprehensive energy system and the main network power exchange constraint condition, the backpressure unit thermal power range constraint condition, the constraint relation between the electric power and the thermal power of the backpressure unit under the rule of heat power fixation, the extraction and condensation unit thermoelectric power range constraint condition, the two gas unit thermal and electric power climbing rate constraint conditions, the energy storage power station charging and discharging depth constraint condition, the energy storage power station charging and discharging power upper and lower limit constraint conditions, the energy storage power station electric quantity and charging power relation constraint condition and the thermoelectric balance constraint condition in the comprehensive energy system.
Wherein, the comprehensive energy system and the main network power exchange constraint conditions are as follows:
whereinP e GRID 、
Respectively, a lower limit and an upper limit of the switching power.
The constraint conditions of the thermal power range of the back pressure unit are as follows:
whereinP h BY 、
Respectively the lower limit and the upper limit of the thermal output of the back pressure unit.
The constraint relation of electric power and thermal power of the back pressure unit under the rule of 'fixing electricity with heat' is as follows:
wherein
Is the thermoelectric ratio of the backpressure unit.
The thermal power range constraint conditions of the pumping condensing unit are as follows:
whereinP h CN 、
Respectively the lower limit and the upper limit of the thermal output of the extraction condensing unit.
The electric power range constraint conditions of the extraction and coagulation unit are as follows:
whereinP e CN 、
Respectively the lower limit and the upper limit of the thermal output of the extraction condensing unit.
The thermal and electric power ramp rate limiting constraint conditions of the two gas turbine units are as follows:
wherein
Respectively is the lower limit and the upper limit of the thermal output ramp rate of the back pressure unit,
respectively is the lower limit and the upper limit of the thermal output climbing speed of the extraction condensing unit,
respectively is the lower limit and the upper limit of the electric output climbing speed of the extraction condensing unit.
The constraint conditions for limiting the charging and discharging depth of the energy storage power station are as follows:
wherein
Is the total capacity of the energy storage power station,
is the daily initial capacity, eta, of the energy storage power station chg 、η dis Respectively the charge-discharge efficiency of the energy storage power station.
The constraint conditions of the upper and lower limits of the charging and discharging power of the energy storage power station are as follows:
whereinP e BAT 、
Respectively the lower limit and the upper limit of the charging and discharging power of the energy storage power station.
The constraint conditions of the relation between the electric quantity and the charging power of the energy storage power station are as follows:
wherein
And the electric quantity of the energy storage power station at the moment t-1 is shown, and delta t represents unit time.
Thermoelectric balance constraints within the integrated energy system:
in a specific embodiment, the optimization goal of the intra-day phase optimization model is that the incremental expectation for the cost before day is minimal (or the decremental expectation is maximal) in the context of the actual operation of the system during the day.
Decision variables for the intraday optimization phase include: 1) Electric output variable quantity of back pressure unit
Variation of thermal output
2) Electric output variable quantity of pumping and condensing unit
Variation of thermal output
3) Energy storage power station charge and discharge power variation
Real-time electric quantity of power station in daytime
4) Electric power exchange variable quantity between comprehensive energy system and main network
The in-day optimization phase objective function is:
the following constraints are respectively followed:
the intra-day phase constraints (20) to (32) indicate that constraints such as the upper and lower limits, the change rate, and the system balance of each variable are still satisfied when each decision variable changes within a day. The two-stage optimization in the day-ahead and in-day is solved simultaneously, the obtained day-ahead scheduling strategy is two-stage global optimal, the value of source-load random prediction is fully exerted, and the inclusion of various working conditions possibly occurring in actual operation in the optimization design is reflected.
The invention adopts a scene reduction algorithm to reduce small probability scenes, reserves and combines the scenes closest to the original scene probability, avoids the problem of the reduction of the solving efficiency of random unit combination problems caused by the increase of the number of the scenes, realizes the simulation of a large number of uncertain scenes by a small number of scenes, and is shown in figure 2 as probability distribution and data probability distribution characteristic curves of the scenes and the original scenes after probability reduction.
As can be seen from fig. 2, a typical reduction scenario is uniformly distributed in the whole data space, and in a cumulative probability graph representing the data probability distribution characteristics, it can also be seen that the 100 reduced prediction data have a probability distribution almost identical to that of the original 1000 prediction samples.
When the heat pump type heat pump power generation system is applied specifically, based on the operation characteristics of the back pressure unit, the extraction condensing unit and the energy storage power station in different stages, the operation strategies of the units are provided on the basis of not reducing the operation efficiency of the units and ensuring the energy storage power station to exert the peak clipping and valley filling functions, the requirements of fixing the power by heat are fully met, the economic operation characteristics of the two units are met, meanwhile, the idle rate of the energy storage power station is reduced, and the service life requirements are met.
Fig. 3 (a) and (b) show the gas turbine unit operation strategy during the cooling and heating periods. In the refrigeration period, the heat load in the base is small, and at the moment, the operation efficiency of the back pressure unit is low and does not meet the economic requirement; the electric load is higher, so the adjustable characteristic of the electric output of the extraction condensing unit needs to be fully utilized. When the thermal load is lower than 1; when the heat load is higher than 11 to 00, the heat supply economical efficiency of the back pressure unit exceeds that of the extraction condensing unit, so the modes of supplying heat by the back pressure unit and supplying power by the extraction condensing unit under full load are adopted in the time period.
In the heating period, heat is supplied mainly by a backpressure unit due to high heat load, and a pumping condensing unit is used as an auxiliary. The shape of the electric output of the back pressure unit is similar to that of the thermal output curve, and the operation principle of 'fixing electricity by heat' is met; the average daily electric output of the extraction and condensation unit is about 50 percent, and the requirement of economic operation in the starting state is also met.
Fig. 3 (c) and (d) show the charging and discharging strategies of the energy storage power station in the integrated energy system, and the energy storage power station is in a charging state in a low electricity price period of 1; during the high-price time period of 11; and in the rest flat price time periods, the energy storage power station carries out flexible charging and discharging adjustment according to the supply and demand conditions of the system electric energy.
The simulation operation result of the energy storage power station reflects the peak clipping and valley filling effects of the energy storage device, the charging amount of the energy storage device in the low valley period of the refrigeration period is 168MWh, the flat charging electric quantity is 49MWh, and the discharging amount of the energy storage device in the high peak period is 173MWh; the charging amount at the low valley period of the heating period day is 200MWh, the flat discharging electric quantity is 126MWh, and the discharging amount at the high peak period is 34MWh.
Table 1 shows various system indexes calculated according to the power exchange power between the integrated energy system and the main grid, the photovoltaic output and the back pressure, and the electric heating output of the extraction and condensation unit before and after the multi-energy complementary integration optimization is performed:
TABLE 1
As can be seen from comparison of various indexes of the system before and after the multi-energy complementary integration optimization is developed in table 1, after two-stage optimization, power exchange between the system and a main network is reduced, the power self-sufficiency rate is increased, the power peak-valley difference is reduced, the peak load regulation pressure of the system is reduced, and the capacity increasing and modifying requirements of a power distribution network can be reduced; the energy supply ratio of clean energy is greatly improved, the accepting capability of a comprehensive energy system to clean renewable energy is enhanced, meanwhile, the fossil energy conversion efficiency is also greatly improved, and the environmental protection property is greatly improved while the system operation economy is met.
The invention adopts two-stage optimization to obtain the optimal scheduling scheme under an expected scene and the feasible scheduling scheme with the minimum system operation cost under any scene, and has stronger robustness due to the consideration of random output; according to the characteristics of thermoelectric loads at different stages and the respective operating characteristics of the backpressure pumping condensing unit, the two-stage optimization is adopted to dynamically adjust the output of the unit, so that the aim of fully utilizing the economic operation of the unit is fulfilled; the invention can realize higher primary energy conversion efficiency, reduce peak load regulation pressure of the external network and indirectly save the investment of capacity-increasing transformation of the power distribution network; according to the method, a large number of original random samples are obtained by adopting a Monte Carlo method, scenes with high occurrence frequency are reserved by adopting a multi-parameter rapid forward selection method, the scenes selected by a scene screening algorithm have the characteristics of small quantity but more typical characteristic, the model solving speed can be increased, and a practical optimization solution is provided.
The invention also discloses a comprehensive energy system two-stage optimization operation system based on scene analysis, which comprises the following steps:
the first module is used for constructing a day-ahead stage optimization model by taking the lowest day-ahead operation cost as an objective function based on the prediction reference values of each energy source and load;
the second module is used for constructing an intra-day stage optimization model according to the objective function with the minimum increment expected value or the maximum decrement expected value of the cost before the day in the actual operation scene in the day; the actual operation scene is reduced by a multi-parameter rapid forward selection method so as to improve the optimization speed;
and the third module is used for obtaining a global optimal operation scheme through the day-ahead stage optimization model and the day-in stage optimization model.
The operation system of the invention corresponds to the operation method and has the advantages of the operation method.
The invention further discloses a computer-readable storage medium on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for two-stage optimized operation of an integrated energy system based on scene analysis as described above. The invention also discloses computer equipment which comprises a memory and a processor, wherein the memory is stored with a computer program, and the computer program executes the steps of the comprehensive energy system two-stage optimization operation method based on the scene analysis when being operated by the processor. All or part of the flow of the method of the embodiments may be implemented by a computer program, which may be stored in a computer-readable storage medium and executed by a processor, to implement the steps of the embodiments of the methods. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. The memory may be used to store computer programs and/or modules, and the processor may perform various functions by executing or executing the computer programs and/or modules stored in the memory, as well as by invoking data stored in the memory. The memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (4)
1. A scene analysis-based two-stage optimized operation method for an integrated energy system, wherein the integrated energy system comprises a distributed natural gas energy station, a distributed photovoltaic power station and an energy storage power station, and is characterized by comprising the following steps:
constructing a day-ahead stage optimization model by taking the lowest day-ahead operation cost as an objective function based on the prediction reference values of each energy and load;
constructing an intra-day stage optimization model according to an objective function with the minimum increment expected value or the maximum decrement expected value of the cost before the day in an intra-day actual operation scene;
obtaining a global optimal operation scheme through a day-ahead stage optimization model and a day-in stage optimization model;
the actual operation scene is reduced by a multi-parameter rapid forward selection method so as to improve the optimization speed;
the multi-parameter rapid forward selection method comprises the following specific steps:
1) Acquiring an original scene, and establishing a three-dimensional data space by taking the electric load predicted value as an X axis, the heat load predicted value as a Y axis and the photovoltaic electric output predicted value as a Z axis;
2) Calculating the space distance between each scene point and any other scene point;
3) Calculating the Kantorovich distance of each scene point, and updating and reducing a scene set;
4) Calculating the distance between any two remaining scene points in the reduced scene set, calculating the Kantorovich distance of each scene point, and updating the reduced scene set until the reduced scene number reaches the preset number;
5) Updating scenes, and calculating the probability of each reserved scene after aggregation to form a typical reduction scene set;
in the step 1), the predicted values of the electric load, the heat load and the photovoltaic power output are obtained through corresponding predicted reference values and predicted error values, wherein the predicted reference values are determined through analysis on the sunshine condition, the weather condition, the historical load data and the load growth direction of the region;
the decision variables of the optimization model at the day-ahead stage comprise one or more of electric output of a back pressure unit, thermal output of the back pressure unit, electric output of a pumping and condensing unit, thermal output of the pumping and condensing unit, charge and discharge power of an energy storage power station, electric quantity of the energy storage power station and power exchange between an integrated energy system and a main network; the constraint conditions of the optimization model at the day-ahead stage comprise one or more of comprehensive energy system and main network power exchange constraint conditions, backpressure unit thermal power range constraint conditions, constraint relation between electric power and thermal power of a backpressure unit under a heat and power fixing rule, extraction and condensation unit thermoelectric power range constraint conditions, two gas unit thermal electric power ramp rate limitation constraint conditions, energy storage power station charging and discharging depth limitation constraint conditions, energy storage power station charging and discharging power upper and lower limit constraint conditions, energy storage power station electric quantity and charging power relation constraint conditions and thermoelectric balance constraint conditions in the comprehensive energy system;
the constraint conditions of the intra-day phase optimization model are the constraint conditions which meet the intra-day phase optimization model under different scenes;
the decision variables of the optimization model in the day phase comprise one or more of the variation of electric output of the backpressure unit, the variation of thermal output of the backpressure unit, the variation of electric output of the extraction and condensation unit, the variation of thermal output of the extraction and condensation unit, the variation of charge and discharge power of the energy storage power station, the real-time electric quantity of the power station in the day phase and the variation of electric power exchange between the comprehensive energy system and the main network;
the operation strategy of the gas turbine set in the refrigeration and heating periods comprises the following steps: when the heat load is low, the mode that the electric load and the heat load are all supplied by the extraction condensing unit is adopted; when the heat load is higher, the mode of supplying heat by a back pressure unit and supplying power by a pumping condensing unit under full load is adopted at the time; in the heating period, as the heat load is higher, the heat is mainly supplied by a back pressure unit, and a condensation pumping unit is used as an auxiliary; the electric output of the backpressure unit is similar to the curve shape of the thermal output, and the operation principle of 'fixing electricity by heat' is met;
the charging and discharging strategy of the energy storage power station in the comprehensive energy system is as follows: in a low electricity price period, the energy storage power station is in a charging state; in a high electricity price period, the energy storage power station discharges electricity stored at low price to meet the electric load in the system; and in the rest time periods with the flat electricity price, the energy storage power station carries out flexible charging and discharging adjustment according to the supply and demand conditions of the system electric energy.
2. A two-stage optimized operation system of an integrated energy system based on scene analysis, for performing the two-stage optimized operation method of the integrated energy system based on scene analysis according to claim 1, comprising:
the first module is used for constructing a day-ahead stage optimization model by taking the lowest day-ahead operation cost as an objective function based on the prediction reference values of all energy sources and loads;
the second module is used for constructing an intra-day stage optimization model according to the objective function with the minimum increment expected value or the maximum decrement expected value of the cost before the day in the actual operation scene in the day; the actual operation scene is reduced by a multi-parameter rapid forward selection method so as to improve the optimization speed;
and the third module is used for obtaining a global optimal operation scheme through the day-ahead stage optimization model and the day-in stage optimization model.
3. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the scenario analysis based two-stage optimized operation method of an integrated energy system according to claim 1.
4. A computer device comprising a memory and a processor, the memory having stored thereon a computer program, wherein the computer program, when executed by the processor, performs the steps of the scenario analysis based two-stage optimized operation method of an integrated energy system according to claim 1.
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| Title |
|---|
| 热电机组比重及热负荷对风电消纳率影响的研究;张冲等;《电力系统保护与控制》;20131127(第23期);第125-130页 * |
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