CN112465181A - Two-stage optimization scheduling method supporting source-network-load-storage multi-element ubiquitous coordination - Google Patents
Two-stage optimization scheduling method supporting source-network-load-storage multi-element ubiquitous coordination Download PDFInfo
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Abstract
The invention relates to a two-stage optimization scheduling method supporting source-network-load-storage multi-element ubiquitous coordination, which comprises the following steps: 1: a day-ahead stage: predicting the next day data according to the historical data and a load uncertainty model considering demand side management; 2: the method comprises the following steps of taking low-carbon economy as a target, considering deep peak regulation working conditions and normal operation working conditions of the thermal power generating unit, carrying out random sampling by using a Monte Carlo method, solving a day-ahead low-carbon economic dispatching model by using a mixed bat algorithm, and determining a unit start-stop combination, electricity prices in each time period and a price type demand response 3: the in-day stage: and solving and adjusting the day-ahead scheduling plan by using a mixed bat algorithm based on an intra-day thermal power generating unit correction model and an intra-day low-carbon economic scheduling model according to the ultra-short-term predicted values of the wind power plant and the photovoltaic power station and the intra-day system load considering the day-ahead price demand response. The invention realizes low-carbon economic dispatching of the power system, effectively avoids the situation that the sinking enters the partial optimal state under the high-dimensional condition, and quickly obtains the global optimal solution.
Description
Technical Field
The invention relates to the technical field of electric power systems and automation thereof, in particular to low-carbon economic dispatching of an electric power system, and specifically relates to a two-stage optimized dispatching method for supporting source-network-load-storage multi-element ubiquitous coordination.
Background
With the strong construction of the fusion of the two networks of the smart power grid and the ubiquitous power internet of things, high-proportion renewable energy is connected into the power grid, and comprehensive new energy, energy storage and load data are obtained by artificial intelligence, sensors and advanced communication technology and are applied to power system optimization scheduling. In the face of a smart power grid with high permeability of new energy, the low-carbon economic dispatching problem of various energy sources such as wind power, photovoltaic power generation, thermal power, energy storage and the like under multiple time scales is very urgent by fully utilizing source load storage data.
Considering from the power generation side, a new energy power plant replaces a traditional power plant, and the technology of an energy storage power station is changing day by day. The carbon treatment cost of a power system can be effectively reduced by the new energy power generation, the multi-type energy storage power station has the charging and discharging characteristics, the line congestion can be relieved to a certain extent, the wind, light, fire, water and storage are reasonably allocated, and the influence of the intermittent and uncertain power generation of the renewable energy source on a power grid is reduced by utilizing the multi-energy complementary characteristics. And the user is actively guided to participate in demand response and a load curve is optimized from the consideration of the user side. The influence of demand side resources on load peak value reduction is 8% of electricity price type demand response and most of excitation type demand response, and the influence of fluctuation of wind power generation on a power system can be solved by combining the demand response with the wind power generation change characteristic.
The research of the above documents on the power generation side mainly focuses on the day-ahead scheduling of various renewable energy sources, while the research on the user side mainly focuses on the scheduling strategy of multi-time scale demand response, and the attention of the joint scheduling with various energy sources is less. The continuous penetration of new energy brings great difficulty to the peak regulation of a power grid, and a thermal power generating unit is often in a deep peak regulation state and a frequent climbing state, so that the operation of the whole system is greatly influenced.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a two-stage optimization scheduling method for supporting source-network-load-storage multi-element ubiquitous coordination.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a two-stage optimization scheduling method supporting source-network-load-storage multi-element ubiquitous coordination is characterized by comprising the following steps:
step 1: a day-ahead stage: forecasting the next day data according to historical data and a load uncertain model considering demand side management, wherein the next day data comprises wind speed, wind power plant output, outdoor temperature, photovoltaic power station output, system load and electricity price fluctuation;
step 2: the method comprises the steps of taking low-carbon economy as a target, considering a deep peak regulation working condition and a normal operation working condition of the thermal power generating unit, randomly sampling by using a Monte Carlo method, solving a day-ahead low-carbon economy scheduling model by using an improved bat algorithm, and determining a unit start-stop combination, electricity prices in each time period and price type demand response;
and step 3: the in-day stage: according to the ultra-short-term predicted values of the wind power plant and the photovoltaic power station and the daily system load considering the day-ahead price demand response, the day-ahead scheduling plan is adjusted based on the optimization solution of the daily scheduling model; the method comprises the following specific steps:
step 3.1: the method comprises the steps that a thermal power generating unit combination based on a day-ahead scheduling plan is used, a thermal power generating unit correction model is constructed according to parameter distribution functions of wind power, photovoltaic and load, the minimum average adjustment cost is taken as a target, a mixed bat algorithm is used for solving, and the output of the thermal power generating unit is corrected;
step 3.2: the low-carbon economic dispatching is taken as a target, based on an intraday low-carbon economic dispatching model, the incentive type demand response quantity and the intraday dispatching schemes of other units are obtained by utilizing a mixed bat algorithm.
Further: the load uncertainty model considering the demand side management in step 1 is:
at any time, the system load demand at the next moment is uncertain, and load demand uncertainty can be modeled by normally distributed and uniformly distributed probability density functions; using normally distributed probability density function to measure system load PL,tModeling, the actual system power after the user participates in the demand response can be expressed as:
PL,t,act＝PL,t-PPDR,t-PIDR,t
in the formula, PL,act,tActual system load power after the user participates in demand response at the time t; pL,tThe system load power at the moment; pPDR,t、PIDR,tAnd responding electric quantity of the price type demand response virtual machine set and responding electric quantity of the excitation type demand response virtual machine set at the time t.
Day-ahead electricity price type demand response virtual machine set uncertainty model:
the schedulable electricity price type demand response in a certain area is integrated into a virtual unit for scheduling, the virtual unit is named as a day-ahead electricity price type virtual response unit, the decision variable is the electricity price, and the output of the virtual unit is influenced by the change of a price mechanism; the power department improves the power consumption of the user based on the electricity price, and the influence of the change rate of the electricity price on the change rate of the load is characterized by the self-elasticity coefficient and is defined as follows:
φ△L,t＝ett×φ△ρ,t
in the formula, phi△L,tLoad response rate at time t; phi is a△ρ,tThe change rate of the electricity price at the time t; e.g. of the typettIs the coefficient of self-elasticity at time t;
the invention adopts a triangular membership function to describe the uncertainty of the electricity price type DR load response rate:
in the formula (I), the compound is shown in the specification,
for a period t of load response rate phi△L,tThe fuzzy expression of (1); phi is a△L1,t，φ△L2,t，φ△L3,tIs a membership parameter; e.g. of the typettThe self-elasticity coefficient of the time period t in the price elasticity matrix is obtained; delta deltatAnd the maximum error value predicted for the load response rate at the time t is related to the electricity price change rate.
The expectation value after the triangular number in the fuzzy expression of the load demand response rate is converted into the determined variable can represent the actual response electric quantity of the user due to price variation, and then the response electric quantity of the price type demand response virtual machine set can be represented as:
in the formula, PPDR,t,actResponding the actual response power of the user for the day-ahead electricity price type demand at the time t; pPDR,tAnd the electricity load before the user participates in the electricity price demand response at the moment t.
An intra-day excitation type demand response virtual machine set uncertainty model:
the response time of users with excitation type demand response is short, the elastic margin is large, the users participating in the excitation type demand response in the region are integrated into a virtual machine set, and the users are scheduled by a power company in a step compensation electricity price excitation mode in daily scheduling, so that the load is increased and decreased quickly in the operation of a power system. According to the excitation type demand response curve, the price rho is compensated according to a certain valueIDRThe user's load shedding ratio lambda is in lambda2(ρIDR),λ1(ρIDR)]Fluctuation in range, uncertainty of user engagement response at a certain level of incentive can be reduced toThe uniform distribution indicates that:
in the formula, λ (ρ)IDR) To be at the incentive price pIDRThe load shedding rate of the lower excitation type response virtual machine set; lambda [ alpha ]1(ρIDR)、λ2(ρIDR) The upper and lower limits of the load shedding factor are provided.
Response electric quantity P of intra-day excitation type demand response virtual machine setIDR,t,actComprises the following steps:
PIDR,t,act＝λIDR,tPIDR,t
in the formula, λIDR,tLoad shedding rate of the excitation type user at the time t; pIDR,tThe amount of power used before the user participates in the incentive type demand response at time t.
Further: the day-ahead-day two-stage low-carbon economic dispatching model in the step 2 and the step 3 is as follows:
in a day-ahead scheduling stage, introducing carbon emission cost and constructing a day-ahead scheduling model; in the scheduling stage in the day, according to the wind power and photovoltaic power ultra-short term prediction data, the adjustment of the scheduling plan in the day ahead is divided into two steps: correcting the output of the thermal power generating unit by taking the minimum average adjustment cost as a target based on the thermal power generating unit combination of the day-ahead scheduling plan; obtaining a daily scheduling scheme of each unit by taking low-carbon economic scheduling as a target;
introducing carbon emission cost, and constructing a day-ahead low-carbon economic dispatching model:
in the formula, F1A cost function, element, for system operation; ctaxDollar/ton, unit carbon treatment cost on the market; eC,tThe carbon emission is ton of thermal power generating units; eD,tThe carbon content quota of the generator set at t time period is ton, and when the carbon emission of the generator set is within the carbon content quota range, the carbon treatment cost is 0; t isThe number of segments in the scheduling period, T24 for the day-ahead scheduling.
An optimal scheduling strategy of combined operation of a thermal power plant and a renewable energy power plant is adopted, and the system operation cost is as follows:
in the formula (f)G,t、fGO,t、fE,t、fDR,tThe method comprises the following steps of respectively obtaining power supply operation cost at the time t, power supply scheduling cost, energy storage power station charging and discharging cost and scheduling cost of day-ahead demand response virtual machine sets; f. ofF,t、fW,t、fPV,tRespectively calculating the running cost of the thermal generator set, the wind power and photovoltaic power generation operation and maintenance cost in each time period; u. ofF,i,tStarting and stopping a thermal power generating unit by 0-1 variable; pF,i,tThe power output of the ith thermal power generating unit at the moment t; a isi，bi，ciRespectively representing the fuel cost coefficients of the ith thermal generator set in the normal operation state; when the thermal power generating unit carries out deep peak shaving, the unit loss cost is caused by overlarge thermal stress of a rotor; w is acostAdditional operating costs for deep peak shaving; α represents a boundary of a low load state, and is usually 0.6; chi is the loss coefficient of the actual operation of the thermal power generating unit; n is a radical off(PF,i) Determining the cycle frequency of the rotor cracking by the low cycle fatigue property of the rotor material; cunitThe machine purchasing cost for the machine set; n is a radical ofW、NPVThe number of the wind power generation units and the number of the photovoltaic units are respectively; rhoW,j、ρPV,kRespectively representing the operating and maintaining cost coefficients of the wind power plant and the photovoltaic power station; pW,j,t、PPV,k,tRespectively representing the limiting generating capacity of the wind power plant and the photovoltaic active output power at the moment t; cF1,i、CF2,i、CWL,i、CPVL,iThe cost of starting and stopping and climbing of a conventional thermal power generating unit and the limited power generation cost of a renewable energy power generating unit are respectively saved; delta PBESS,tAdjusting power for the energy storage at the time t, taking discharging as a positive direction, and taking a negative value when the energy storage equipment is charged; rhoECost per unit power, dollar/kW, to store energy; rhot,0Is tAn initial electricity price at the time; rhotThe price of electricity at time t.
Further, the method comprises the following steps: the constraint conditions of the day-ahead low-carbon economic dispatching model are as follows:
in the formula, PL,act,tThe actual power of the system load at the moment t; pW,j,rRated output power for each fan;
the jth wind turbine generator unit obtains the maximum output of the fan at each moment according to the predicted wind speed; pB,c，PBRespectively representing the charging and discharging power of the energy storage power station; pB,c,max、PB，maxEach represents the maximum value of the charge/discharge power. The output power of the thermal power generating unit is limited by the minimum and maximum output of the thermal power generating unit and the upward and downward climbing speeds of the generator, and the start-stop state of the thermal power generating unit is constrained by the maximum and minimum start-stop time; the output power constraint of the photovoltaic power station and the constraint of the wind turbine generator are not repeated.
Further, the thermal power unit output correction model of the mathematical model scheduled in step 3 is as follows:
considering the influence of wind, light, demand response and load uncertainty on optimal scheduling, the output correction model of the thermal power unit can be divided into the following two models;
(1) deterministic optimal scheduling model
The thermal power generating unit is usually in a deep peak regulation state in the day scheduling, uncertainty of wind power and photovoltaic power generation prediction is not considered in the model, the minimum day adjustment cost of the thermal power generating unit is taken as a target, output of the started thermal power generating unit is corrected, and the model is as follows:
PDEV,i,t＝|PG,i,t,DA-PG,i,t,IN|
in the formula, CIN,i,tScheduling a cost function of the thermal power generating unit for the system at the intraday stage; pDEV,i,tFor scheduling power P by day-aheadG,i,t,DAAnd the daily power PG,i,t,INThe resulting bias power; x is the number ofi、yi、ziAnd the scheduling cost coefficients are the scheduling cost coefficients of the ith thermal power generating unit in the day.
(2) Uncertainty optimization scheduling model
In the model, the day-ahead scheduling is a determined value, the errors of wind and light prediction and loads on different time scales are considered, the output of the hot-electric generator set is changed in the day-inside scheduling, the average adjustment cost is minimized as an objective function, and the model is as follows:
in the formula (I), the compound is shown in the specification,
is a varying bias power;
the power in the day is changed due to errors of prediction of wind power and photovoltaic power stations.
In scheduling in the day, when the actual wind power output PW,jHas a prediction error of muWWhen in (%), the constraint change of the actual wind power output power is as follows:
in the formula (I), the compound is shown in the specification,
the actual maximum output power P of the fan caused by the fluctuation of the actual wind speedW,j,maxMaximum, minimum power of change, and satisfying the following constraints:
0≤Pw,j≤PW,j,max。
the actual output power variation expression of the photovoltaic power station power generation is the same as the expression.
Further: the intraday low-carbon economic dispatching model in the step 3 is as follows:
in the formula, Δ f is average adjustment cost, and in the day-to-day scheduling, the thermal power generating unit deviates from the day-ahead scheduling plan due to uncertainty of wind speed, solar radiation and load demand prediction.
The demand response cost in the low-carbon economic dispatching objective function is the day-ahead electricity price demand response cost CPDR,tAnd intra-day incentive demand response cost CIDR,tThe composition, which can be expressed as:
the intra-day scheduling incentive type demand response has certain scheduling cost, namely corresponding economic compensation is given to users adjusting the load quantity according to the requirements.
In the formula, ωtA variable of 0-1 is used for judging whether the virtual machine set participates in scheduling in the day at the moment t; n is a radical ofIDRNumber of stepped electricity price segments;
respectively increasing and decreasing electric quantity when the excitation type response virtual machine set is at the d-th stage of the electricity price at the time t;
the unit cost for increasing or decreasing the electric quantity is respectively.
In order to fully mobilize the flexibility of demand response of the load side and realize the consumption of wind power and light power generation in the multi-energy cooperative scheduling system, the load response quantity of the incentive type response users is not transferred to other time periods.
In the formula, PIDR，maxThe maximum value of demand response is referred to by the incentive type response user in the whole scheduling period.
The invention has the advantages and positive effects that:
(1) aiming at a large-scale renewable energy grid-connected power system, the two-stage day-ahead and day-in coordinated scheduling model provided by the invention considers the normal operation state and the deep peak regulation state of the thermal power generating unit, so that the power generation cost of the thermal power generating unit during large-scale wind and light grid connection is quantized, and the model reflects the actual working condition more truly. With the increase of the peak regulation depth of the thermal power generating unit, the starting and stopping times of the thermal power generating unit are reduced, so that the intermittent and fluctuating properties of the renewable energy power generation can be favorably absorbed, and powerful support is improved for the low-carbon economic dispatching of the power system under the condition of large-scale renewable energy grid connection.
(2) According to the invention, based on the response quantities of the electricity price type demand response virtual machine set and the excitation type demand response virtual machine set in the day-ahead and day-in stages, the carbon treatment cost is introduced into the economic dispatching model, and the low-carbon economic dispatching of the power system is realized. The introduction of demand response adjusts the power utilization time of users to a certain extent, and the source-load interaction reduces the limit power generation cost of renewable energy sources to a great extent.
(3) The improved bat algorithm is adopted to solve the day-ahead-day two-stage optimized dispatching model of the electric power system, the gene of the genetic algorithm is fused, the diversity of bat population is improved, the method is applied to the optimization scheme of the multi-energy combined dispatching, the partial optimization is effectively avoided under the high-dimensional condition, and the global optimal solution is quickly obtained.
Drawings
Fig. 1 is a schematic diagram of a scheduling mode of a large-scale renewable energy grid-connected power system considering demand response provided by the present invention.
FIG. 2 is a flow chart of the two-stage model solving based on the improved bat algorithm provided by the invention.
Fig. 3 is a flow chart of the power flow calculation based on two-point estimation provided by the present invention.
FIG. 4 is a wind, light predicted output curve and system load predicted curve for a specific application example.
Fig. 5 is a curve of the start-stop cost and the loss cost of the thermal power generating unit under different peak shaving depths of a specific application example.
FIG. 6 is a system load curve before and after demand response for a particular example application.
Fig. 7 is a diagram of a system optimization scheduling result in a comprehensive scenario of a specific application example.
FIG. 8 shows the total cost of the system for different load prediction errors for a specific application example.
Detailed Description
The present invention will be described in further detail with reference to the following embodiments, which are illustrative, not restrictive, and the scope of the invention is not limited thereto.
A two-stage optimal scheduling method supporting source-network-load-storage multi-element ubiquitous coordination, please refer to fig. 1-8, the invention point is that the method comprises the following steps:
step 1: a day-ahead stage: and predicting the data of the next day according to the historical data and the load uncertain model considering the management of the demand side, wherein the data comprises wind speed, wind power plant output, outdoor temperature, photovoltaic power station output, system load and electricity price fluctuation.
The load uncertainty model considering demand side management is as follows:
at any time, the system load demand at the next moment is uncertain, and load demand uncertainty can be modeled by normally distributed and uniformly distributed probability density functions; using normally distributed probability density function to measure system load PL,tModeling, the actual system power after the user participates in the demand response can be expressed as:
PL,t,act＝PL,t-PPDR,t-PIDR,t
in the formula, PL,act,tActual system load power after the user participates in demand response at the time t; pL,tThe system load power at the moment; pPDR,t、PIDR,tAnd responding electric quantity of the price type demand response virtual machine set and responding electric quantity of the excitation type demand response virtual machine set at the time t.
Day-ahead electricity price type demand response virtual machine set uncertainty model:
the schedulable electricity price type demand response in a certain area is integrated into a virtual unit for scheduling, the virtual unit is named as a day-ahead electricity price type virtual response unit, the decision variable is the electricity price, and the output of the virtual unit is influenced by the change of a price mechanism; the power department improves the power consumption of the user based on the electricity price, and the influence of the change rate of the electricity price on the change rate of the load is characterized by the self-elasticity coefficient and is defined as follows:
φ△L,t＝ett×φ△ρ,t
in the formula, phi△L,tLoad response rate at time t; phi is a△ρ,tThe change rate of the electricity price at the time t; e.g. of the typettIs the coefficient of self-elasticity at time t;
the invention adopts a triangular membership function to describe the uncertainty of the electricity price type DR load response rate:
in the formula (I), the compound is shown in the specification,
for a period t of load response rate phi△L,tThe fuzzy expression of (1); phi is a△L1,t，φ△L2,t，φ△L3,tIs a membership parameter; e.g. of the typettThe self-elasticity coefficient of the time period t in the price elasticity matrix is obtained; delta deltatAnd the maximum error value predicted for the load response rate at the time t is related to the electricity price change rate.
The expectation value after the triangular number in the fuzzy expression of the load demand response rate is converted into the determined variable can represent the actual response electric quantity of the user due to price variation, and then the response electric quantity of the price type demand response virtual machine set can be represented as:
in the formula, PPDR,t,actResponding the actual response power of the user for the day-ahead electricity price type demand at the time t; pPDR,tAnd the electricity load before the user participates in the electricity price demand response at the moment t.
An intra-day excitation type demand response virtual machine set uncertainty model:
the response time of users with excitation type demand response is short, the elastic margin is large, the users participating in the excitation type demand response in the region are integrated into a virtual machine set, and the users are scheduled by a power company in a step compensation electricity price excitation mode in daily scheduling, so that the load is increased and decreased quickly in the operation of a power system. According to the excitation type demand response curve, the price rho is compensated according to a certain valueIDRThe user's load shedding ratio lambda is in lambda2(ρIDR),λ1(ρIDR)]The uncertainty of the user engagement response at a certain level of motivation can be reduced to a uniformly distributed representation:
in the formula, λ (ρ)IDR) To be at the incentive price pIDRThe load shedding rate of the lower excitation type response virtual machine set; lambda [ alpha ]1(ρIDR)、λ2(ρIDR) The upper and lower limits of the load shedding factor are provided.
Response electric quantity P of intra-day excitation type demand response virtual machine setIDR,t,actComprises the following steps:
PIDR,t,act＝λIDR,tPIDR,t
in the formula, λIDR,tLoad shedding rate of the excitation type user at the time t; pIDR,tThe amount of power used before the user participates in the incentive type demand response at time t.
Step 2: the method comprises the steps of taking low-carbon economy as a target, considering a deep peak regulation working condition and a normal operation working condition of the thermal power generating unit, conducting random sampling by using a Monte Carlo method, solving a day-ahead low-carbon economy scheduling model by using an improved bat algorithm, and determining a unit start-stop combination, electricity prices in each time period and price type demand response.
The day-ahead low-carbon economic dispatching model comprises the following steps:
introducing carbon emission cost, and constructing a day-ahead low-carbon economic dispatching model:
in the formula, F1A cost function, element, for system operation; ctaxDollar/ton, unit carbon treatment cost on the market; eC,tThe carbon emission is ton of thermal power generating units; eD,tThe carbon quota of the generator set in t time period is ton, and the carbon emission of the generator set is in the carbon quotaWithin the range, the carbon treatment cost is 0; t is the number of segments in the scheduling period, for day-ahead scheduling, T is 24.
An optimal scheduling strategy of combined operation of a thermal power plant and a renewable energy power plant is adopted, and the system operation cost is as follows:
in the formula (f)G,t、fGO,t、fE,t、fDR,tThe method comprises the following steps of respectively obtaining power supply operation cost at the time t, power supply scheduling cost, energy storage power station charging and discharging cost and scheduling cost of day-ahead demand response virtual machine sets; f. ofF,t、fW,t、fPV,tRespectively calculating the running cost of the thermal generator set, the wind power and photovoltaic power generation operation and maintenance cost in each time period; u. ofF,i,tStarting and stopping a thermal power generating unit by 0-1 variable; pF,i,tThe power output of the ith thermal power generating unit at the moment t; a isi，bi，ciRespectively representing the fuel cost coefficients of the ith thermal generator set in the normal operation state; when the thermal power generating unit carries out deep peak shaving, the unit loss cost is caused by overlarge thermal stress of a rotor; w is acostAdditional operating costs for deep peak shaving; α represents a boundary of a low load state, and is usually 0.6; chi is the loss coefficient of the actual operation of the thermal power generating unit; n is a radical off(PF,i) Determining the cycle frequency of the rotor cracking by the low cycle fatigue property of the rotor material; cunitThe machine purchasing cost for the machine set; n is a radical ofW、NPVThe number of the wind power generation units and the number of the photovoltaic units are respectively; rhoW,j、ρPV,kRespectively representing the operating and maintaining cost coefficients of the wind power plant and the photovoltaic power station; pW,j,t、PPV,k,tRespectively representing the limiting generating capacity of the wind power plant and the photovoltaic active output power at the moment t; cF1,i、CF2,i、CWL,i、CPVL,iThe cost of starting and stopping and climbing of a conventional thermal power generating unit and the limited power generation cost of a renewable energy power generating unit are respectively saved; delta PBESS,tAdjusting power for the energy storage at the time t, taking discharging as a positive direction, and taking a negative value when the energy storage equipment is charged; rhoERegulating specific power for stored energyCost, dollars/kW; rhot,0Is the initial electricity price at the time t; rhotThe price of electricity at time t.
The constraint conditions of the day-ahead low-carbon economic dispatching model are as follows:
in the formula, PL,act,tThe actual power of the system load at the moment t; pW,j,rRated output power for each fan;
the jth wind turbine generator unit obtains the maximum output of the fan at each moment according to the predicted wind speed; pB,c，PBRespectively representing the charging and discharging power of the energy storage power station; pB,c,max、PB，maxEach represents the maximum value of the charge/discharge power. The output power of the thermal power generating unit is limited by the minimum and maximum output of the thermal power generating unit and the upward and downward climbing speeds of the generator, and the start-stop state of the thermal power generating unit is constrained by the maximum and minimum start-stop time; the output power constraint of the photovoltaic power station and the constraint of the wind turbine generator are not repeated.
And step 3:
the in-day stage: according to the ultra-short-term predicted values of the wind power plant and the photovoltaic power station and the daily system load considering the day-ahead price demand response, the day-ahead scheduling plan is adjusted based on the optimization solution of the daily scheduling model; the method comprises the following specific steps:
step 3.1: the method comprises the steps that a thermal power generating unit combination based on a day-ahead scheduling plan is used, a thermal power generating unit correction model is built according to parameter distribution functions of wind power, photovoltaic and load, the minimum average adjustment cost is taken as a target, and the output of the thermal power generating unit is corrected by solving based on a mixed bat algorithm;
step 3.2: the low-carbon economic dispatching is taken as a target, based on an intraday low-carbon economic dispatching model, the incentive type demand response quantity and the intraday dispatching schemes of other units are obtained by utilizing a mixed bat algorithm.
The mathematical model of the scheduling in the day is as follows:
in the scheduling stage in the day, according to the wind power and photovoltaic power ultra-short term prediction data, the adjustment of the scheduling plan in the day ahead is divided into two steps: correcting the output of the thermal power generating unit by taking the minimum average adjustment cost as a target based on the thermal power generating unit combination of the day-ahead scheduling plan; obtaining a daily scheduling scheme of each unit by taking low-carbon economic scheduling as a target;
the output correction model of the thermal power generating unit is as follows:
considering the influence of wind, light, demand response and load uncertainty on optimal scheduling, the output correction model of the thermal power unit can be divided into the following two models;
(1) deterministic optimal scheduling model
The thermal power generating unit is usually in a deep peak regulation state in the day scheduling, uncertainty of wind power and photovoltaic power generation prediction is not considered in the model, the minimum day adjustment cost of the thermal power generating unit is taken as a target, output of the started thermal power generating unit is corrected, and the model is as follows:
PDEV,i,t＝|PG,i,t,DA-PG,i,t,IN|
in the formula, CIN,i,tScheduling a cost function of the thermal power generating unit for the system at the intraday stage; pDEV,i,tFor scheduling power P by day-aheadG,i,t,DAAnd the daily power PG,i,t,INThe resulting bias power; x is the number ofi、yi、ziAnd the scheduling cost coefficients are the scheduling cost coefficients of the ith thermal power generating unit in the day.
(2) Uncertainty optimization scheduling model
In the model, the day-ahead scheduling is a determined value, the errors of wind and light prediction and loads on different time scales are considered, the output of the hot-electric generator set is changed in the day-inside scheduling, the average adjustment cost is minimized as an objective function, and the model is as follows:
in the formula (I), the compound is shown in the specification,
is a varying bias power;
the power in the day is changed due to errors of prediction of wind power and photovoltaic power stations.
In scheduling in the day, when the actual wind power output PW,jHas a prediction error of muWWhen in (%), the constraint change of the actual wind power output power is as follows:
in the formula (I), the compound is shown in the specification,
the actual maximum output power P of the fan caused by the fluctuation of the actual wind speedW,j,maxMaximum, minimum power of change, and satisfying the following constraints:
0≤Pw,j≤PW,j,max
the actual output power variation expression of the photovoltaic power station power generation is the same as the expression.
The day-interior low-carbon economic dispatching model comprises the following steps:
in the formula, Δ f is average adjustment cost, and in the day-to-day scheduling, the thermal power generating unit deviates from the day-ahead scheduling plan due to uncertainty of wind speed, solar radiation and load demand prediction.
The demand response cost in the low-carbon economic dispatching objective function is the day-ahead electricity price demand response cost CPDR,tAnd intra-day incentive demand response cost CIDR,tThe composition, which can be expressed as:
the intra-day scheduling incentive type demand response has certain scheduling cost, namely corresponding economic compensation is given to users adjusting the load quantity according to the requirements.
In the formula, ωtA variable of 0-1 is used for judging whether the virtual machine set participates in scheduling in the day at the moment t; n is a radical ofIDRNumber of stepped electricity price segments;
respectively increasing and decreasing electric quantity when the excitation type response virtual machine set is at the d-th stage of the electricity price at the time t;
the unit cost for increasing or decreasing the electric quantity is respectively.
In order to fully mobilize the flexibility of demand response of the load side and realize the consumption of wind power and light power generation in the multi-energy cooperative scheduling system, the load response quantity of the incentive type response users is not transferred to other time periods.
In the formula, PIDR，maxThe maximum value of demand response is referred to by the incentive type response user in the whole scheduling period.
The step 2 and the step 3 adopt a mixed bat algorithm based on a genetic algorithm to solve, and specifically comprise the following steps:
the invention takes the objective function in the two-stage optimized dispatching model of the wind-light-fire-storage system as the position point of the bat food in the space random distribution, the process of searching food and updating the position of the bat individual is the process of searching the source-load optimal dispatching plan, the size of the objective function shows the position of the bat individual, the bat individual position is continuously close to the global optimal value of the objective function after a plurality of times of optimization, and the pulse frequency of the bat individual is increased and the loudness is reduced by the system in the solution space so as to improve the solving precision.
Generating N in multidimensional solution space formed by constraint conditions of operation of joint scheduling systempopEach source-load scheduling scheme of the bat individual is used as a bat individual to be coded, a gene sequence can be formed, and any bat individual comprises wind power, photovoltaic power generation and load information:
XI＝[PW,PPV,PL,PDR]I＝1,2,…Npop
and (4) selecting, crossing and mutating the genetic algorithm of the encoded bat individuals, encoding and transmitting individual information to offspring.
A two-stage optimization scheduling solution based on the improved bat algorithm is shown in FIG. 2 and comprises two stages of day-ahead scheduling and day-in scheduling. In the day-ahead stage, the input quantity is randomly sampled by using a Monte Carlo method according to the wind energy, the photovoltaic, the total system load and the price type demand response model, a day-ahead scheduling plan is solved by using an improved bat algorithm, and a unit start-stop combination, the electricity price in each time period and the price type demand response quantity are determined; in the day-ahead stage, a two-point estimation method random optimization model is constructed according to the parameter distribution functions of wind power, photovoltaic and load, the minimum value of average adjustment cost is obtained through optimization, the output of the thermal power generating unit is determined, and the solving process is shown in figure 3. Based on the method, the incentive type demand response quantity and the output of other units are obtained by using the improved bat algorithm.
In summary, a large-scale renewable energy grid-connected power system scheduling mode considering demand response is shown in fig. 1. Day-ahead scheduling is performed one day ahead, and the total scheduling period is 24h in units of 1 h. The power grid department informs a day-ahead price type demand response user in advance, predicts the next day system load, receives the next day output predicted values of the wind power plant and the photovoltaic power plant through the dispatching center, and arranges the outputs of each thermal power generating unit, the wind power plant, the photovoltaic power plant and the energy storage power plant in the next day. Scheduling in the day is executed 15min in advance, the total scheduling period is 1h, and the response of the unit, the energy storage power station and the excitation demand in each time period is corrected in a rolling mode. And the power grid department informs the intra-day incentive type demand response users in advance, corrects the output of the started thermal power generating unit based on the ultra-short-term predicted values of the wind power plant and the photovoltaic power plant received by the dispatching center and the intra-day system load considering the price demand response before the day, and determines incentive type demand response quantity and the output of the energy storage power station.
Although the embodiments and figures of the present invention have been disclosed for illustrative purposes, those skilled in the art will appreciate that: various substitutions, changes and modifications are possible without departing from the spirit and scope of the invention and the appended claims, and therefore the scope of the invention is not limited to the disclosure of the embodiments and figures.
Claims (6)
1. A two-stage optimization scheduling method supporting source-network-load-storage multi-element ubiquitous coordination is characterized by comprising the following steps:
step 1: a day-ahead stage: forecasting the next day data according to historical data and a load uncertain model considering demand side management, wherein the next day data comprises wind speed, wind power plant output, outdoor temperature, photovoltaic power station output, system load and electricity price fluctuation;
step 2: the method comprises the steps of taking low-carbon economy as a target, considering a deep peak regulation working condition and a normal operation working condition of the thermal power generating unit, randomly sampling by using a Monte Carlo method, solving a day-ahead low-carbon economy scheduling model by using an improved bat algorithm, and determining a unit start-stop combination, electricity prices in each time period and price type demand response;
and step 3: the in-day stage: according to the ultra-short-term predicted values of the wind power plant and the photovoltaic power station and the daily system load considering the day-ahead price demand response, the day-ahead scheduling plan is adjusted based on the optimization solution of the daily scheduling model; the method comprises the following specific steps:
step 3.1: the method comprises the steps that a thermal power generating unit combination based on a day-ahead scheduling plan is used, a thermal power generating unit correction model is constructed according to parameter distribution functions of wind power, photovoltaic and load, the minimum average adjustment cost is taken as a target, a mixed bat algorithm is used for solving, and the output of the thermal power generating unit is corrected;
step 3.2: the low-carbon economic dispatching is taken as a target, based on an intraday low-carbon economic dispatching model, the incentive type demand response quantity and the intraday dispatching schemes of other units are obtained by utilizing a mixed bat algorithm.
2. The two-stage optimal scheduling method supporting source-network-load-storage multi-element ubiquitous coordination according to claim 1, wherein: the load uncertainty model considering demand side management in step 1 is:
at any time, the system load demand at the next moment is uncertain, and load demand uncertainty can be modeled by normally distributed and uniformly distributed probability density functions; using normally distributed probability density function to measure system load PL,tModeling, the actual system power after the user participates in the demand response can be expressed as:
PL,t,act＝PL,t-PPDR,t-PIDR,t
in the formula, PL,act,tActual system load power after the user participates in demand response at the time t; pL,tThe system load power at the moment; pPDR,t、PIDR,tResponding electric quantity of the price type demand response virtual machine set and responding electric quantity of the excitation type demand response virtual machine set at the time t;
day-ahead electricity price type demand response virtual machine set uncertainty model:
the schedulable electricity price type demand response in a certain area is integrated into a virtual unit for scheduling, the virtual unit is named as a day-ahead electricity price type virtual response unit, the decision variable is the electricity price, and the output of the virtual unit is influenced by the change of a price mechanism; the power department improves the power consumption of the user based on the electricity price, and the influence of the change rate of the electricity price on the change rate of the load is characterized by the self-elasticity coefficient and is defined as follows:
φ△L,t＝ett×φ△ρ,t
in the formula, phi△L,tLoad response rate at time t; phi is a△ρ,tThe change rate of the electricity price at the time t; e.g. of the typettIs the coefficient of self-elasticity at time t;
the invention adopts a triangular membership function to describe the uncertainty of the electricity price type DR load response rate:
in the formula (I), the compound is shown in the specification,
for a period t of load response rate phi△L,tThe fuzzy expression of (1); phi is a△L1,t，φ△L2,t，φ△L3,tIs a membership parameter; e.g. of the typettThe self-elasticity coefficient of the time period t in the price elasticity matrix is obtained; delta deltatNot less than 0, and is load at time tThe maximum error value of the response rate prediction is related to the electricity price change rate;
the expectation value after the triangular number in the fuzzy expression of the load demand response rate is converted into the determined variable can represent the actual response electric quantity of the user due to price variation, and then the response electric quantity of the price type demand response virtual machine set can be represented as:
in the formula, PPDR,t,actResponding the actual response power of the user for the day-ahead electricity price type demand at the time t; pPDR,tThe electricity load before the user participates in the electricity price demand response at the moment t;
an intra-day excitation type demand response virtual machine set uncertainty model:
the response time of users with excitation type demand response is short, the elastic margin is large, the users participating in the excitation type demand response in the region are integrated into a virtual machine set, and the users are scheduled by a power company in a step compensation electricity price excitation mode in daily scheduling, so that the load is increased and decreased quickly in the operation of a power system; according to the excitation type demand response curve, the price rho is compensated according to a certain valueIDRThe user's load shedding ratio lambda is in lambda2(ρIDR),λ1(ρIDR)]The uncertainty of the user engagement response at a certain level of motivation can be reduced to a uniformly distributed representation:
in the formula, λ (ρ)IDR) To be at the incentive price pIDRThe load shedding rate of the lower excitation type response virtual machine set; lambda [ alpha ]1(ρIDR)、λ2(ρIDR) The upper limit and the lower limit of the load shedding rate are respectively;
response electric quantity P of intra-day excitation type demand response virtual machine setIDR,t,actComprises the following steps:
PIDR,t,act＝λIDR,tPIDR,t
in the formula, λIDR,tLoad shedding rate of the excitation type user at the time t; pIDR,tThe amount of power used before the user participates in the incentive type demand response at time t.
3. The two-stage optimal scheduling method supporting source-network-load-storage multi-element ubiquitous coordination according to claim 1, wherein: the day-ahead-day two-stage low-carbon economic dispatching model in the step 2 and the step 3 is as follows:
in a day-ahead scheduling stage, introducing carbon emission cost and constructing a day-ahead scheduling model; in the scheduling stage in the day, according to the wind power and photovoltaic power ultra-short term prediction data, the adjustment of the scheduling plan in the day ahead is divided into two steps: correcting the output of the thermal power generating unit by taking the minimum average adjustment cost as a target based on the thermal power generating unit combination of the day-ahead scheduling plan; obtaining a daily scheduling scheme of each unit by taking low-carbon economic scheduling as a target;
introducing carbon emission cost, and constructing a day-ahead low-carbon economic dispatching model:
in the formula, F1A cost function, element, for system operation; ctaxDollar/ton, unit carbon treatment cost on the market; eC,tThe carbon emission is ton of thermal power generating units; eD,tThe carbon content quota of the generator set at t time period is ton, and when the carbon emission of the generator set is within the carbon content quota range, the carbon treatment cost is 0; t is the number of segments in the scheduling period, and for day-ahead scheduling, T is 24;
an optimal scheduling strategy of combined operation of a thermal power plant and a renewable energy power plant is adopted, and the system operation cost is as follows:
in the formula (f)G,t、fGO,t、fE,t、fDR,tThe method comprises the following steps of respectively obtaining power supply operation cost at the time t, power supply scheduling cost, energy storage power station charging and discharging cost and scheduling cost of day-ahead demand response virtual machine sets; f. ofF,t、fW,t、fPV,tRespectively calculating the running cost of the thermal generator set, the wind power and photovoltaic power generation operation and maintenance cost in each time period; u. ofF,i,tStarting and stopping a thermal power generating unit by 0-1 variable; pF,i,tThe power output of the ith thermal power generating unit at the moment t; a isi，bi，ciRespectively representing the fuel cost coefficients of the ith thermal generator set in the normal operation state; when the thermal power generating unit carries out deep peak shaving, the unit loss cost is caused by overlarge thermal stress of a rotor; w is acostAdditional operating costs for deep peak shaving; α represents a boundary of a low load state, and is usually 0.6; chi is the loss coefficient of the actual operation of the thermal power generating unit; n is a radical off(PF,i) Determining the cycle frequency of the rotor cracking by the low cycle fatigue property of the rotor material; cunitThe machine purchasing cost for the machine set; n is a radical ofW、NPVThe number of the wind power generation units and the number of the photovoltaic units are respectively; rhoW,j、ρPV,kRespectively representing the operating and maintaining cost coefficients of the wind power plant and the photovoltaic power station; pW,j,t、PPV,k,tRespectively representing the limiting generating capacity of the wind power plant and the photovoltaic active output power at the moment t; cF1,i、CF2,i、CWL,i、CPVL,iThe cost of starting and stopping and climbing of a conventional thermal power generating unit and the limited power generation cost of a renewable energy power generating unit are respectively saved; delta PBESS,tAdjusting power for the energy storage at the time t, taking discharging as a positive direction, and taking a negative value when the energy storage equipment is charged; rhoECost per unit power, dollar/kW, to store energy; rhot,0Is the initial electricity price at the time t; rhotThe price of electricity at time t.
4. The two-stage optimal scheduling method supporting source-network-load-storage multi-element ubiquitous coordination according to claim 3, wherein:
the constraint conditions of the day-ahead low-carbon economic dispatching model are as follows:
in the formula, PL,act,tThe actual power of the system load at the moment t; pW,j,rRated output power for each fan;
the jth wind turbine generator unit obtains the maximum output of the fan at each moment according to the predicted wind speed; pB,c，PBRespectively representing the charging and discharging power of the energy storage power station; pB,c,max、PB，maxRespectively representing the maximum values of charge and discharge power; the output power of the thermal power generating unit is limited by the minimum and maximum output of the thermal power generating unit and the upward and downward climbing speeds of the generator, and the start-stop state of the thermal power generating unit is constrained by the maximum and minimum start-stop time; the output power constraint of the photovoltaic power station is the same as that of the wind turbine generator.
5. The two-stage optimal scheduling method supporting source-network-load-storage multi-element ubiquitous coordination according to claim 1, wherein: the thermal power unit output correction model of the mathematical model scheduled in the step 3 is as follows:
considering the influence of wind, light, demand response and load uncertainty on optimal scheduling, the output correction model of the thermal power unit can be divided into the following two models;
(1) deterministic optimal scheduling model
The thermal power generating unit is usually in a deep peak regulation state in the day scheduling, uncertainty of wind power and photovoltaic power generation prediction is not considered in the model, the minimum day adjustment cost of the thermal power generating unit is taken as a target, output of the started thermal power generating unit is corrected, and the model is as follows:
PDEV,i,t＝|PG,i,t,DA-PG,i,t,IN|
in the formula, CIN,i,tScheduling a cost function of the thermal power generating unit for the system at the intraday stage; pDEV,i,tFor scheduling power P by day-aheadG,i,t,DAAnd the daily power PG,i,t,INThe resulting bias power; x is the number ofi、yi、ziRespectively obtaining scheduling cost coefficients of the ith thermal power generating unit in the day;
(2) uncertainty optimization scheduling model
In the model, the day-ahead scheduling is a determined value, the errors of wind and light prediction and loads on different time scales are considered, the output of the hot-electric generator set is changed in the day-inside scheduling, the average adjustment cost is minimized as an objective function, and the model is as follows:
in the formula (I), the compound is shown in the specification,
is a varying bias power;
the power in the day is changed due to errors of prediction of wind power and photovoltaic power stations;
in scheduling in the day, when the actual wind power output PW,jHas a prediction error of muWWhen in (%), the constraint change of the actual wind power output power is as follows:
in the formula (I), the compound is shown in the specification,
the actual maximum output power P of the fan caused by the fluctuation of the actual wind speedW,j,maxMaximum, minimum power of change, and satisfying the following constraints:
0≤Pw,j≤PW,j,max。
6. the two-stage optimal scheduling method supporting source-network-load-storage multi-element ubiquitous coordination according to claim 4, wherein: the intraday low-carbon economic dispatching model in the step 3 is as follows:
in the formula, delta f is average adjustment cost, and in the day scheduling, the thermal power generating unit deviates from the day-ahead scheduling plan due to uncertainty of wind speed, solar radiation and load demand prediction;
the demand response cost in the low-carbon economic dispatching objective function is the day-ahead electricity price demand response cost CPDR,tAnd intra-day incentive demand response cost CIDR,tThe composition, which can be expressed as:
the intra-day scheduling incentive type demand response has certain scheduling cost, namely corresponding economic compensation is given to users adjusting the load according to the demands;
in the formula, ωtA variable of 0-1 is used for judging whether the virtual machine set participates in scheduling in the day at the moment t; n is a radical ofIDRNumber of stepped electricity price segments;
respectively increasing and decreasing electric quantity when the excitation type response virtual machine set is at the d-th stage of the electricity price at the time t;
unit cost when the electric quantity is increased or decreased;
in order to fully transfer the flexibility of demand response of a load side and realize the consumption of wind power and light power generation in a multi-energy cooperative scheduling system, the load response quantity of an excitation type response user is not transferred to other time periods;
in the formula, PIDR，maxThe maximum value of demand response is referred to by the incentive type response user in the whole scheduling period.
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