CN114204550A - Green scheduling method for electric power system containing multiple types of new energy - Google Patents

Green scheduling method for electric power system containing multiple types of new energy Download PDF

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Publication number
CN114204550A
CN114204550A CN202111404853.8A CN202111404853A CN114204550A CN 114204550 A CN114204550 A CN 114204550A CN 202111404853 A CN202111404853 A CN 202111404853A CN 114204550 A CN114204550 A CN 114204550A
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power
cost
load
formula
unit
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刘伟
鄂志君
王天昊
李振斌
刘颂
范瑞卿
孔祥玉
王宁
黄志刚
于光耀
杨帮宇
马世乾
王坤
宋国辰
王珍珍
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/10—The dispersed energy generation being of fossil origin, e.g. diesel generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B10/00—Integration of renewable energy sources in buildings
    • Y02B10/10—Photovoltaic [PV]

Abstract

The invention relates to a green dispatching method of an electric power system containing multiple types of new energy, which comprises the following steps: step 1: according to the load characteristics of the user, a system load uncertain model considering demand side management is established; step 2: predicting the data of the next day according to the historical data and the load model based on the electricity price, wherein the data comprises wind speed, wind power plant output, outdoor temperature, photovoltaic power station output, system load and electricity price fluctuation; and step 3: establishing a day-ahead low-carbon economic dispatching model by taking low-carbon economy as a target and considering the deep peak regulation working condition and the normal operation working condition of the thermal power generating unit; and 4, step 4: and (4) randomly sampling by utilizing a Monte Carlo method, solving the day-ahead low-carbon economic dispatching model established in the step (3) based on an improved bat algorithm, and determining the output of each unit, the electricity price in each time period and the price type demand response quantity. The invention can avoid the situation of trapping the partial optimization under the high-dimensional condition and quickly obtain the global optimal solution.

Description

Green scheduling method for electric power system containing multiple types of new energy
Technical Field
The invention belongs to the technical field of power systems and automation thereof, and relates to a power grid containing large-scale renewable energy sources and a user demand response participation scheduling method, in particular to a green scheduling method of a power system containing various types of new energy sources.
Background
At the present stage, with the great promotion of the construction of the fusion of a strong smart grid and a ubiquitous power internet of things, high-proportion renewable energy is accessed into the grid, and artificial intelligence, sensors and an advanced communication technology acquire comprehensive new energy, energy storage and load data and are applied to power system optimization scheduling. Meanwhile, global greenhouse effect has increasingly influenced the ecological system, and CO2 emission reduction in the power industry plays a key role in reducing greenhouse gas emission. Under the strategy of 'three-type two-network', in the face of a new energy high-permeability smart grid, the low-carbon economic scheduling 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 the source load storage data.
On the other hand, considering from the power generation side, the renewable energy power plant replaces the traditional power plant to generate power, the overall carbon emission of the power system can be greatly reduced, the uncertainty related to wind and solar power generation can be solved by the charging and discharging characteristics of stored energy, the circuit congestion is relieved, and the impact of new energy on the power grid can be relieved by reasonably utilizing the multi-energy complementary characteristics of the fire, wind, light and stored energy power stations. The rapid development of large-scale renewable energy power stations enables dispatch data to grow explosively, and the challenges of analyzing the characteristics and mutual correlation of new energy power generation such as water, light and wind and establishing a dispatch plan are the operation of a power grid. On the other hand, the user is actively guided to participate in demand response and the load curve is optimized from 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.
With the gradual deepening of the strong smart grid, the permeability of renewable energy sources is continuously increased, the intermittent and fluctuating properties bring great difficulty to the peak regulation of the power grid, and in order to match the randomness and the fluctuating properties of the renewable new energy sources, the thermal power generating unit is often in a deep peak regulation state and a frequent climbing state, so that the influence on the operation cost of the system is not negligible.
Current research is mainly focused on considering the impact of the volatility and intermittency of large-scale renewable energy power generation on power scheduling, but there is less concern about joint scheduling of multiple types of energy with the uncertainty of demand response on the user side. Meanwhile, in the scheduling solving process, due to the fact that uncertainty of renewable energy sources and demand response is considered, the calculation scale of the traditional solving method of the scheduling model is increased, efficiency is low, and the method is easy to fall into a local optimal solution.
Through searching, no prior art publication which is the same as or similar to the present invention is found.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a green scheduling method for an electric power system containing multiple types of new energy, which can avoid partial optimization under the high-dimensional condition and quickly obtain a global optimal solution.
The invention solves the practical problem by adopting the following technical scheme:
a green scheduling method of an electric power system containing multiple types of new energy comprises the following steps:
step 1: according to the load characteristics of the user, a system load uncertain model considering demand side management is established;
step 2: predicting the next day data according to the historical data and the load model based on the electricity price;
and step 3: establishing a day-ahead low-carbon economic dispatching model by taking low-carbon economy as a target and considering the deep peak regulation working condition and the normal operation working condition of the thermal power generating unit;
and 4, step 4: and (4) randomly sampling by utilizing a Monte Carlo method, solving the day-ahead low-carbon economic dispatching model established in the step (3) based on an improved bat algorithm, and determining the output of each unit, the electricity price in each time period and the price type demand response quantity.
Further, the specific steps of step 1 include:
step 1.1: the schedulable electricity price type demand response in a certain area is integrated into a virtual unit for scheduling, and an uncertainty model of the electricity price type demand response virtual response unit is established based on the elasticity coefficient of the electricity price;
step 1.2: and establishing a system load uncertainty model considering demand side management based on the user response condition of the electricity price type demand response.
Moreover, the uncertainty model of the electricity price type demand response virtual response unit is as follows:
the effect of rate of change of electricity price on rate of change of load is characterized by the coefficient of self-elasticity, defined as follows:
φΔL,t＝ett×φΔρ,t
in the formula, phiΔL,tLoad response rate at time t; phi is aΔρ,tThe change rate of the electricity price at the time t; e.g. of the typettIs the coefficient of self-elasticity at time t.
A user participates in demand response according to a voluntary principle, the actual load response quantity has randomness and cannot be completely determined, and the uncertainty of the power price type DR load response rate is described by adopting a triangular membership function:
Figure BDA0003371936140000031
Figure BDA0003371936140000032
in the formula (I), the compound is shown in the specification,
Figure BDA0003371936140000033
for a period t of load response rate phiΔL,tThe fuzzy expression of (1); phi is aΔL1,t，φΔL2,t，φΔL3,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 the following formula is shown:
Figure BDA0003371936140000041
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.
Moreover, the system load uncertainty model considering demand side management:
modeling the system load before participating in demand response by adopting a normally distributed probability density function, wherein an uncertain model of the system load is as follows:
Figure BDA0003371936140000042
in the formula, l is system load; mu.sLAnd σLRespectively mean and standard deviation of the uncertain load.
PL,t,act＝PL,t-PDR,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; pDR,tAnd the power for the user to participate in the demand response at the time t comprises the electric quantity of the price type demand response and the incentive type demand response.
Moreover, the day-ahead low-carbon economic dispatching model and the constraint conditions thereof in the step 3 are as follows:
aiming at low-carbon economy, introducing the carbon emission cost into an economic dispatching objective function of the power system:
Figure BDA0003371936140000043
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 the t period is tonWhen the carbon emission of the unit is within the carbon content quota range, the carbon treatment cost is 0; t is the number of segments in the scheduling period, for day-ahead scheduling, T is 24.
Wind power and photovoltaic are generated by renewable energy sources, carbon emission is not generated in the power generation process, and the carbon emission of the wind power and photovoltaic is not considered. The carbon emission in the power system comes from the coal consumption of the thermal power generating unit, and the carbon emission quota allocation of the unit in the period t is as follows:
Figure BDA0003371936140000051
in the formula, NFThe number of thermal power generating units; etaDAnd allocating the unit active output carbon emission quota for the generator set.
The actual carbon emission of the thermal power generating unit in the period t is as follows:
Figure BDA0003371936140000052
in the formula, alphai、βi、δiAnd the emission factors are respectively the emission factors of the thermal power generating unit i.
The system operation cost is as follows:
Figure BDA0003371936140000053
in the formula (f)G,t、fGO,t、fE,t、fDR,tThe system comprises a multi-power-supply power generation and dispatching cost at the time t, an energy storage power station charging and discharging cost and a demand response dispatching cost.
The first item is the power supply operation cost, which comprises the operation and scheduling cost of a thermal power generator, the operation and maintenance cost of a wind-light renewable energy power generator set and the like.
fG,t＝fF,t+fW,t+fPV,t
In the formula (f)F,t、fW,t、fPV,tRespectively comprises the running cost of the thermal power generator set, the wind power and the photovoltaic power generation in each time periodAnd (5) electric operation and maintenance cost.
(1) Operating costs of thermal power generating units
Due to the fact that renewable energy sources with volatility are connected to the grid on a large scale, the climbing and starting times of a traditional thermal power generating unit are increased, deep peak shaving cost is not negligible, and according to the characteristics of a steam turbine, when the load of the steam turbine is lower, heat consumption is higher, and the service life loss of the unit is extremely large. The deep peak regulation cost comprises the unit operation coal consumption cost and the unit loss cost during deep peak regulation. The thermal power generating unit can be divided into a normal operation state and a deep peak regulation state in the operation process, and the operation cost can be expressed as:
Figure BDA0003371936140000054
in the formula uF,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，ciAnd respectively 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 caused by overlarge thermal stress of the rotor. w is acostFor the extra operating cost of deep peak shaving, the calculation formula is:
Figure BDA0003371936140000061
in the formula, α represents a boundary in 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; cunitAnd purchasing machine cost for the machine set.
(2) Operation and maintenance cost of renewable energy sources such as wind, light and the like
Wind and photovoltaic power generation does not consume fuel, but normal operation of a unit is influenced by considering randomness and fluctuation of wind and light, certain operation and maintenance cost is generated, and the operation and maintenance cost can be approximately expressed as a linear relation of generating power of the unit.
Figure BDA0003371936140000062
Figure BDA0003371936140000063
In the formula, NW、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,tAnd respectively representing the limit power generation capacity of the wind power plant and the photovoltaic active output power at the moment.
The second item is the scheduling cost of the power supply, including the start-stop and climbing cost of the conventional thermal power generating unit and the limited power generation cost of the renewable energy generating unit.
fGO,t＝CF1,i+CF2,i+CWL,i+CPVL,i
(1) Start-stop and ramp-up costs of thermal power plants
The method is characterized in that large-scale renewable energy sources are connected to the grid, and intermittent and fluctuating properties of the large-scale renewable energy sources inevitably cause frequent starting, stopping and climbing of the thermal power generating unit, so that the operation cost is increased. The start stop and climb cost function is as follows:
CF1,i＝uF,i,t(1-uF,i,t-1)SF,i,t
Figure BDA0003371936140000071
in the formula uF,i,tThe number of the starting and stopping of the fire generator sets in the t-th time period is determined; sF,i,tStarting and stopping costs of the fire-electricity generating set in the t-th time period; gamma rayFAnd the coefficients are the climbing cost function coefficients of the thermal power generating unit.
(2) Limiting power generation cost of wind-solar renewable energy source unit
Figure BDA0003371936140000072
Figure BDA0003371936140000073
In the formula, cWL、cPVLLimiting the cost of electricity generation for a unit; pW,Q,jThe limited power generation capacity of the jth wind turbine generator is obtained; pPV,Q,kThe limited power generation capacity of the kth wind turbine is obtained.
The third item is the energy storage power station adjustment cost.
fE,t＝ΔPBESS,tρE
In the formula,. 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; rhoEThe cost per unit power, dollar/kW, is regulated for energy storage.
The fourth item is the scheduling cost of the demand response in the day ahead, and in the day ahead stage, only the electricity price type demand response virtual unit participates in scheduling, and the scheduling cost can be represented by the change of the electricity selling income of the power grid side:
fDR,t＝PPDR,tρt,0-PPDR,t,actρt
in the formula, ρt,0Is the initial electricity price at the time t; rhotThe price of electricity at time t.
Constraint conditions of a day-ahead low-carbon economic dispatching model:
(1) system load balancing constraints
And for any moment, the sum of the output of the power generation and energy storage thermal power generating unit, the fan, the photovoltaic power station, the water pumping power station and the storage battery power station of the system is equal to the load after the system participates in demand response.
Figure BDA0003371936140000074
In the formula, PL,act,tIs the actual power of the system load at time t.
(2) Generator set restraint
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.
And (3) output power constraint of the wind driven generator:
0≤PW,j≤PW,j,r
in the formula, PW,j,rRated output power for each fan is provided by the wind power plant. Actual output P of fanW,jThe variation range is as follows:
Figure BDA0003371936140000081
in the formula (I), the compound is shown in the specification,
Figure BDA0003371936140000082
the maximum output of the jth wind turbine at each moment of the wind turbine is obtained according to the predicted wind speed, and the maximum output is variable and fluctuating. The output power constraint of the photovoltaic power station is the same as above.
(3) Battery energy storage power station constraints
The power requirement of the storage battery energy storage power station meets the following requirements:
0≤PB,c≤PB,c,max
0≤PB≤PB，max
in the formula, 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 energy storage power station has the following electric quantity balance and electric power constraint:
WB,t+1＝WB,t+PB,tΔt
WB,min≤WB,t≤WB,max
in the formula, WB,min、WB,maxRespectively the minimum and maximum residual electric quantity of the energy storage power station; wB,tAnd the residual electric quantity of the energy storage power station in the t-th time period.
Further, the specific steps of step 4 include:
(1) initializing algorithm parameters, generating bat individual gene sequences, setting parameters of fans, photovoltaics, loads and the like in individual gene segments according to a source-load uncertainty model, determining the number of iterations for 1000 times,
an objective function f (X) for setting the total bat number NpopPosition X0The flying speed v0Loudness A0And frequency f0；
(2) Randomly generating N within the feasible range of the control variablepopOnly the bat is taken as the current optimal solution set, the objective function values under the current bat gene sequence are calculated and sorted from small to large, and the bat individuals which are 1/2 before the sorting are reserved for updating and propagating processes;
(3) the iterative calculation is started. Changing frequency and updating individual speed, and selecting, speed advancing average crossing and mutation operations by taking the new bat population as a parent to form the first half part of the daughter bat population;
(4) updating the position of the parent bat population individual and judging whether the random number rand1 meets the local search condition>rIIf yes, selecting an optimal solution to perform local search to obtain a new position to replace the original position;
(5) determining whether the new fitness value satisfies f (X)I)＜fbestAnd a random number rand2<AI. If so, accepting the new solution to form the latter half of the daughter bat population, and updating the objective function value of the bat individual.
(6) The objective function values of the whole filial generation population are ranked and evaluated, and the current optimal position (solution) X is updated*。
(7) Judging whether the maximum iteration times are exceeded or not, and if the maximum iteration times are exceeded, returning to the step (2); otherwise, the algorithm is ended and the optimal value is output.
The invention has the advantages and beneficial effects that:
1. aiming at a large-scale renewable energy grid-connected power system, the day-ahead scheduling model provided by the invention considers the normal operation state and the deep peak regulation state of the thermal power generating unit, quantifies the power generation cost of the thermal power generating unit during large-scale wind and light grid connection, and enables the established day-ahead scheduling model to reflect 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 quantity of the electricity price type demand response virtual machine set in the day-ahead stage, 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. According to the invention, a particle swarm improved bat algorithm is adopted to solve a day-ahead scheduling optimization model of an electric power system, and the particle swarm algorithm is introduced on the basis of the bat algorithm, so that bat individuals have the genetic characteristics of the particle swarm algorithm, the diversity of bat populations is improved by fusing the particle swarm algorithm, the situation that the bats are trapped into a local optimum under the high-dimensional condition is avoided, and the global optimum solution is obtained quickly.
Drawings
FIG. 1 is a flowchart of a green scheduling method for an electric power system including multiple types of new energy according to the present invention;
FIG. 2 is a flow chart of the two-stage model solution of the present invention based on the improved bat algorithm;
FIG. 3 is a graph of wind and light predicted output curves and system load predictions obtained by an embodiment of the present invention;
fig. 4 is a graph of the start-stop cost and the loss cost of the thermal power generating unit at different peak shaving depths, which is obtained by the specific embodiment of the present invention.
Detailed Description
The embodiments of the invention will be described in further detail below with reference to the accompanying drawings:
a green scheduling method of an electric power system containing multiple types of new energy comprises the following steps:
step 1: and establishing a system load uncertain model considering demand side management according to the load characteristics of the user.
The specific steps of the step 1 comprise:
step 1.1: and integrating schedulable electricity price type demand response in a certain area into a virtual unit for scheduling, and establishing an uncertainty model of the electricity price type demand response virtual response unit based on the electricity price elasticity coefficient.
The uncertainty model of the electricity price type demand response virtual response unit is as follows:
the electricity utilization behavior habit of the user of electricity price type demand response is fixed, the response time is long, the adjustable range is limited, and the electricity price mechanism is utilized to guide the user to select a more economical electricity utilization mode in the day-ahead scheduling stage, so that the elastic adjustment of the load is realized. Under the background of an energy Internet of things, on the basis of a strong intelligent power grid system, the number of user participation demand responses is increased, schedulable electricity price type demand responses in a certain area can be integrated into a virtual unit for scheduling, the virtual unit is named as a day-ahead electricity price type virtual response unit, a decision variable is electricity price, and the output of the virtual unit is influenced by the change of a price mechanism.
From the economic perspective, the demand response based on the elastic coefficient of electricity price is less when the electricity price is higher; otherwise, the electricity consumption is large. The power department improves the electricity consumption of the user through the electricity price, and the influence of the electricity price change rate on the load change rate is characterized by the self-elasticity coefficient and is defined as follows:
φΔL,t＝ett×φΔρ,t
in the formula, phiΔL,tLoad response rate at time t; phi is aΔρ,tThe change rate of the electricity price at the time t; e.g. of the typettIs the coefficient of self-elasticity at time t.
A user participates in demand response according to a voluntary principle, the actual load response quantity has randomness and cannot be completely determined, and the uncertainty of the power price type DR load response rate is described by adopting a triangular membership function:
Figure BDA0003371936140000111
Figure BDA0003371936140000112
in the formula (I), the compound is shown in the specification,
Figure BDA0003371936140000113
for a period t of load response rate phiΔL,tThe fuzzy expression of (1); phi is aΔL1,t，φΔL2,t，φΔL3,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 the following formula is shown:
Figure BDA0003371936140000121
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.
Step 1.2: and establishing a system load uncertainty model considering demand side management based on the user response condition of the electricity price type demand response.
The system load uncertainty model considering demand side management is as follows:
at any time, the system load demand at the next time is uncertain, and the load demand uncertainty can be modeled generally by a normally distributed and uniformly distributed probability density function. Modeling the system load before participating in demand response by adopting a normally distributed probability density function, wherein an uncertain model of the system load is as follows:
Figure BDA0003371936140000122
in the formula, l is system load; mu.sLAnd σLRespectively mean and standard deviation of the uncertain load.
PL,t,act＝PL,t-PDR,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; pDR,tAnd the power for the user to participate in the demand response at the time t comprises the electric quantity of the price type demand response and the incentive type demand response.
Step 2: and predicting the data of the next day according to the historical data and the load model based on the electricity price, wherein the data comprises wind speed, wind power plant output, outdoor temperature, photovoltaic power station output, system load and electricity price fluctuation.
And step 3: the method comprises the steps of taking low-carbon economy as a target, and establishing a day-ahead low-carbon economy scheduling model by considering a deep peak regulation working condition and a normal operation working condition of the thermal power generating unit.
The day-ahead low-carbon economic dispatching model and the constraint conditions of the step 3 are as follows:
aiming at low-carbon economy, introducing the carbon emission cost into an economic dispatching objective function of the power system:
Figure BDA0003371936140000123
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, for day-ahead scheduling, T is 24.
Wind power and photovoltaic are generated by renewable energy sources, carbon emission is not generated in the power generation process, and the carbon emission of the wind power and photovoltaic is not considered. The carbon emission in the power system comes from the coal consumption of the thermal power generating unit, and the carbon emission quota allocation of the unit in the period t is as follows:
Figure BDA0003371936140000131
in the formula, NFThe number of thermal power generating units;ηDand allocating the unit active output carbon emission quota for the generator set.
The actual carbon emission of the thermal power generating unit in the period t is as follows:
Figure BDA0003371936140000132
in the formula, alphai、βi、δiAnd the emission factors are respectively the emission factors of the thermal power generating unit i.
The system running cost is as follows:
Figure BDA0003371936140000133
in the formula (f)G,t、fGO,t、fE,t、fDR,tThe system comprises a multi-power-supply power generation and dispatching cost at the time t, an energy storage power station charging and discharging cost and a demand response dispatching cost.
The first item is the power supply operation cost, which comprises the operation and scheduling cost of a thermal power generator, the operation and maintenance cost of a wind-light renewable energy power generator set and the like.
fG,t＝fF,t+fW,t+fPV,t
In the formula (f)F,t、fW,t、fPV,tThe running cost of the thermal power generation unit, the operation and maintenance cost of the wind power generation and the photovoltaic power generation in each time period are respectively.
(1) Operating costs of thermal power generating units
Due to the fact that renewable energy sources with volatility are connected to the grid on a large scale, the climbing and starting times of a traditional thermal power generating unit are increased, deep peak shaving cost is not negligible, and according to the characteristics of a steam turbine, when the load of the steam turbine is lower, heat consumption is higher, and the service life loss of the unit is extremely large. The deep peak regulation cost comprises the unit operation coal consumption cost and the unit loss cost during deep peak regulation. The thermal power generating unit can be divided into a normal operation state and a deep peak regulation state in the operation process, and the operation cost can be expressed as:
Figure BDA0003371936140000141
in the formula uF,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，ciAnd respectively 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 caused by overlarge thermal stress of the rotor. w is acostFor the extra operating cost of deep peak shaving, the calculation formula is:
Figure BDA0003371936140000142
in the formula, α represents a boundary in 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; cunitAnd purchasing machine cost for the machine set.
(2) Operation and maintenance cost of renewable energy sources such as wind, light and the like
Wind and photovoltaic power generation does not consume fuel, but normal operation of a unit is influenced by considering randomness and fluctuation of wind and light, certain operation and maintenance cost is generated, and the operation and maintenance cost can be approximately expressed as a linear relation of generating power of the unit.
Figure BDA0003371936140000143
Figure BDA0003371936140000144
In the formula, NW、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,tAnd respectively representing the limit power generation capacity of the wind power plant and the photovoltaic active output power at the moment.
The second item is the scheduling cost of the power supply, including the start-stop and climbing cost of the conventional thermal power generating unit and the limited power generation cost of the renewable energy generating unit.
fGO,t＝CF1,i+CF2,i+CWL,i+CPVL,i
(1) Start-stop and ramp-up costs of thermal power plants
The method is characterized in that large-scale renewable energy sources are connected to the grid, and intermittent and fluctuating properties of the large-scale renewable energy sources inevitably cause frequent starting, stopping and climbing of the thermal power generating unit, so that the operation cost is increased. The start stop and climb cost function is as follows:
CF1,i＝uF,i,t(1-uF,i,t-1)SF,i,t
Figure BDA0003371936140000151
in the formula uF,i,tThe number of the starting and stopping of the fire generator sets in the t-th time period is determined; sF,i,tStarting and stopping costs of the fire-electricity generating set in the t-th time period; gamma rayFAnd the coefficients are the climbing cost function coefficients of the thermal power generating unit.
(2) Limiting power generation cost of wind-solar renewable energy source unit
Figure BDA0003371936140000152
Figure BDA0003371936140000153
In the formula, cWL、cPVLLimiting the cost of electricity generation for a unit; pW,Q,jThe limited power generation capacity of the jth wind turbine generator is obtained; pPV,Q,kThe limited power generation capacity of the kth wind turbine is obtained.
The third item is the energy storage power station adjustment cost.
fE,t＝ΔPBESS,tρE
In the formula,. DELTA.PBESS,tRegulating power for stored energy at time t, with discharge positiveDirection, negative when the energy storage device is charged; rhoEThe cost per unit power, dollar/kW, is regulated for energy storage.
The fourth item is the scheduling cost of the demand response in the day ahead, and in the day ahead stage, only the electricity price type demand response virtual unit participates in scheduling, and the scheduling cost can be represented by the change of the electricity selling income of the power grid side:
fDR,t＝PPDR,tρt,0-PPDR,t,actρt
in the formula, ρt,0Is the initial electricity price at the time t; rhotThe price of electricity at time t.
Constraint conditions of a day-ahead low-carbon economic dispatching model:
(1) system load balancing constraints
And for any moment, the sum of the output of the power generation and energy storage thermal power generating unit, the fan, the photovoltaic power station, the water pumping power station and the storage battery power station of the system is equal to the load after the system participates in demand response.
Figure BDA0003371936140000161
In the formula, PL,act,tIs the actual power of the system load at time t.
(2) Generator set restraint
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.
And (3) output power constraint of the wind driven generator:
0≤PW,j≤PW,j,r
in the formula, PW,j,rRated output power for each fan is provided by the wind power plant. Actual output P of fanW,jThe variation range is as follows:
Figure BDA0003371936140000162
in the formula (I), the compound is shown in the specification,
Figure BDA0003371936140000163
the maximum output of the jth wind turbine at each moment of the wind turbine is obtained according to the predicted wind speed, and the maximum output is variable and fluctuating. The output power constraint of the photovoltaic power station is the same as above.
(3) Battery energy storage power station constraints
The power requirement of the storage battery energy storage power station meets the following requirements:
0≤PB,c≤PB,c,max
0≤PB≤PB，max
in the formula, 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 energy storage power station has the following electric quantity balance and electric power constraint:
WB,t+1＝WB,t+PB,tΔt
WB,min≤WB,t≤WB,max
in the formula, WB,min、WB,maxRespectively the minimum and maximum residual electric quantity of the energy storage power station; wB,tAnd the residual electric quantity of the energy storage power station in the t-th time period.
And 4, step 4: and (4) randomly sampling by utilizing a Monte Carlo method, solving the day-ahead low-carbon economic dispatching model established in the step (3) based on an improved bat algorithm, and determining the output of each unit, the electricity price in each time period and the price type demand response quantity.
The specific steps of the step 4 comprise:
the bat algorithm based on the genetic algorithm is applied to solving the two-stage optimized scheduling of wind, light and fire storage, and the algorithm comprises the following steps:
(1) initializing algorithm parameters, generating bat individual gene sequences, setting parameters of fans, photovoltaics, loads and the like in individual gene segments according to a source-load uncertainty model, determining the number of iterations for 1000 times,
an objective function f (X) for setting the total bat number NpopPosition X0The flying speed v0Loudness A0And frequency f0；
(2) Randomly generating N within the feasible range of the control variablepopOnly the bat is taken as the current optimal solution set, the objective function values under the current bat gene sequence are calculated and sorted from small to large, and the bat individuals which are 1/2 before the sorting are reserved for updating and propagating processes;
(3) the iterative calculation is started. Changing frequency and updating individual speed, and selecting, speed advancing average crossing and mutation operations by taking the new bat population as a parent to form the first half part of the daughter bat population;
(4) updating the position of the parent bat population individual and judging whether the random number rand1 meets the local search condition>rIIf yes, selecting an optimal solution to perform local search to obtain a new position to replace the original position;
(5) determining whether the new fitness value satisfies f (X)I)＜fbestAnd a random number rand2<AI. If so, accepting the new solution to form the latter half of the daughter bat population, and updating the objective function value of the bat individual.
(6) The objective function values of the whole filial generation population are ranked and evaluated, and the current optimal position (solution) X is updated*。
(7) Judging whether the maximum iteration times are exceeded or not, and if the maximum iteration times are exceeded, returning to the step (2); otherwise, the algorithm is ended and the optimal value is output.
The bat algorithm based on the genetic algorithm:
the bat algorithm has simple parameter setting and understandable principle, is widely applied to engineering, and randomly distributes the position X of each bat in the optimization processIThe flying speed vIPulse frequency fIPulse volume AIAnd the pulse wave emission frequency rIBut is easy to premature convergence, and introduces genetic algorithm, so that bat individuals have GA genetic characteristics, the inter-individual connection is enhanced, and the global search is facilitated.
The invention takes the objective function in the day-ahead scheduling model of the wind-light-fire-storage system as the position points of the bat food in the space random distribution, the process of searching the food and updating the position of the bat individual is the process of searching the source-load optimal scheduling 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 and loudness of the bat individual are increased and 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 systempopBat individuals, each source-load scheduling scheme as a bat individual for coding, can compose gene sequence [27]And any bat individual contains 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.
The invention is further illustrated below with reference to specific examples:
taking a power grid system in a certain area as an example for analysis, the system comprises a thermal power generating unit with the installed capacity of 1200MW, a wind power generating unit with the capacity of 200MW and a photovoltaic unit with the capacity of 400 MW. The actual active power output and the prediction error of the wind power and the photovoltaic are considered to be 20%, and the sample follows the Weber distribution
Figure BDA0003371936140000181
And
Figure BDA0003371936140000182
and calculating the average adjustment cost by using a two-point estimation method. The cost factors of wind power and photovoltaic power generation are shown in table 1. According to the practical situation, the day-ahead electricity price change of the area is improved and reduced by 50% to the maximum extent, and the inflection point of the electricity price change rate is +/-0.3.
TABLE 1 wind and photovoltaic power generation operation and maintenance cost coefficient
Capacity (MW) Cost factor ($/MW. h)
Wind power generation 45 3.25
Photovoltaic system 40 3.5
The existing power system has more generators installed, more thermal power generation, limited capacity of energy storage power stations, large-scale new energy power grid connection and great peak-to-valley difference of user power consumption behaviors, a thermal power generating unit is excessively started at the moment of high peak power consumption, and is frequently operated in a deep peak regulation state due to the fact that the thermal power generating unit cannot be frequently started and stopped at the time of low valley power consumption, the operation cost of the thermal power generating unit is increased along with the increase of the peak regulation depth, and a wind and light predicted output curve and a system load prediction curve are obtained. And calculating a unit cost change curve of the start-stop cost and the loss cost of the thermal power generating unit under different peak shaving depths according to the standard coal price 603 yuan/t, as shown in fig. 4. The parameter settings for the improved bat algorithm are shown in table 2.
TABLE 2 improved Bat Algorithm parameter initialization settings
Parameter name Parameter value
Npop 100
fI [0,100]
r0 0.5
γ 0.05
A0 0.25
ε 0.95
Assuming that the changes of wind power, photovoltaic power generation and load level are consistent with the prediction, a bat algorithm and a particle swarm algorithm before and after improvement are respectively used for solving a day-ahead scheduling model of the wind-solar-fire-containing power system without considering the deep peak regulation state and the demand response of the thermal power generating unit, and the performance of the bat algorithm before and after improvement is shown in a table 3. The improved bat algorithm has obviously better convergence precision than the original algorithm, improves the diversity of bat population due to the gene characteristics of the fused particle swarm algorithm, avoids the partial optimization under the high-dimensional condition, and quickly obtains the global optimal solution.
TABLE 3 Bat Algorithm front and rear Performance comparison
Algorithm Mean value/ten thousand yuan Standard deviation of Simulation time/s
BA Algorithm 409459.88 4.2719 2.04
Improved BA algorithm 389024.49 1.9625 2.96
GA 396102.31 3.2481 2.43
According to the scheduling method provided by the invention, the operation condition of the system under the condition of considering source-load interaction and not considering source-load interaction under the limited energy storage scene is designed and analyzed. The capacity of the storage battery energy storage power station is 50MW, the initial energy storage is 0, the maximum charge-discharge power is 20MW, and the loss during charge and discharge is 0.04. The electricity price before the electricity price demand response is 71$/MW · h, and the load self-elasticity coefficient is-0.3. A low carbon scene, wherein carbon treatment cost is introduced on the basis of Case1, and the unit electric quantity CO of the traditional coal-fired unit2The emission is 0.88-1.21 t/(MW & h), the unit electricity emission of the region is set to be 0.798t/(MW & h), and the carbon trading price is 17$/t CO2。
According to the magnitude of wind power, photoelectricity and load requirements in the calculation example, wind and light are fully consumed in the calculation example, adjustment is carried out only by a thermal power generating unit and an excitation type demand response user in daily scheduling, and the optimal scheduling result of each scheduling scene is analyzed.
TABLE 4 System scheduling cost under different scenarios
Figure BDA0003371936140000201
Figure BDA0003371936140000211
Table 4 shows the scheduling costs of the systems in different scenes, and in the day-ahead scheduling without considering the source-load interaction, the energy storage system relieves the peak shaving pressure of the thermal power generating unit to a certain extent, and the output cost of the thermal power generating unit is high. In the source-load interactive system scheduling, the user-side electricity price type demand response virtual machine set participates in scheduling, the uncertainty of system load is improved, and the influence on system operation caused by load fluctuation is effectively avoided. Therefore, the scheduling model provided by the invention has significant advantages in reducing the total scheduling cost of the system under the condition of limited energy storage.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Claims (6)

1. A green scheduling method of an electric power system containing multiple types of new energy is characterized by comprising the following steps: the method comprises the following steps:
step 1: according to the load characteristics of the user, a system load uncertain model considering demand side management is established;
step 2: predicting the next day data according to the historical data and the load model based on the electricity price;
and step 3: establishing a day-ahead low-carbon economic dispatching model by taking low-carbon economy as a target and considering the deep peak regulation working condition and the normal operation working condition of the thermal power generating unit;
and 4, step 4: and (4) randomly sampling by utilizing a Monte Carlo method, solving the day-ahead low-carbon economic dispatching model established in the step (3) based on an improved bat algorithm, and determining the output of each unit, the electricity price in each time period and the price type demand response quantity.
2. The green dispatching method for the power system containing the multiple types of new energy sources as claimed in claim 1, wherein: the specific steps of the step 1 comprise:
step 1.1: the schedulable electricity price type demand response in a certain area is integrated into a virtual unit for scheduling, and an uncertainty model of the electricity price type demand response virtual response unit is established based on the elasticity coefficient of the electricity price;
step 1.2: and establishing a system load uncertainty model considering demand side management based on the user response condition of the electricity price type demand response.
3. The green dispatching method for the power system containing the multiple types of new energy sources as claimed in claim 2, wherein: the uncertainty model of the electricity price type demand response virtual response unit is as follows:
the effect of rate of change of electricity price on rate of change of load is characterized by the coefficient of self-elasticity, defined as follows:
φΔL,t＝ett×φΔρ,t
in the formula, phiΔL,tLoad response rate at time t; phi is aΔρ,tThe change rate of the electricity price at the time t; e.g. of the typettIs the coefficient of self-elasticity at time t.
A user participates in demand response according to a voluntary principle, the actual load response quantity has randomness and cannot be completely determined, and the uncertainty of the power price type DR load response rate is described by adopting a triangular membership function:
Figure FDA0003371936130000021
Figure FDA0003371936130000022
in the formula (I), the compound is shown in the specification,
Figure FDA0003371936130000023
for a period t of load response rate phiΔL,tThe fuzzy expression of (1); phi is aΔL1,t，φΔL2,t，φΔL3,tIs a membership parameter; e.g. of the typettFor self-bounce of t periods in price elastic matrixA coefficient of sex; 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 the following formula is shown:
Figure FDA0003371936130000024
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.
4. The green dispatching method for the power system containing the multiple types of new energy sources as claimed in claim 2, wherein: the system load uncertainty model considering demand side management is as follows:
modeling the system load before participating in demand response by adopting a normally distributed probability density function, wherein an uncertain model of the system load is as follows:
Figure FDA0003371936130000025
in the formula, l is system load; mu.sLAnd σLRespectively mean and standard deviation of the uncertain load.
PL,t,act＝PL,t-PDR,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; pDR,tAnd the power for the user to participate in the demand response at the time t comprises the electric quantity of the price type demand response and the incentive type demand response.
5. The green dispatching method for the power system containing the multiple types of new energy sources as claimed in claim 1, wherein: the day-ahead low-carbon economic dispatching model and the constraint conditions of the step 3 are as follows:
aiming at low-carbon economy, introducing the carbon emission cost into an economic dispatching objective function of the power system:
Figure FDA0003371936130000031
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, for day-ahead scheduling, T is 24.
Wind power and photovoltaic are generated by renewable energy sources, carbon emission is not generated in the power generation process, and the carbon emission of the wind power and photovoltaic is not considered. The carbon emission in the power system comes from the coal consumption of the thermal power generating unit, and the carbon emission quota allocation of the unit in the period t is as follows:
Figure FDA0003371936130000032
in the formula, NFThe number of thermal power generating units; etaDAnd allocating the unit active output carbon emission quota for the generator set.
The actual carbon emission of the thermal power generating unit in the period t is as follows:
Figure FDA0003371936130000033
in the formula, alphai、βi、δiAnd the emission factors are respectively the emission factors of the thermal power generating unit i.
The system operation cost is as follows:
Figure FDA0003371936130000034
in the formula (f)G,t、fGO,t、fE,t、fDR,tThe system comprises a multi-power-supply power generation and dispatching cost at the time t, an energy storage power station charging and discharging cost and a demand response dispatching cost.
The first item is the power supply operation cost, which comprises the operation and scheduling cost of a thermal power generator, the operation and maintenance cost of a wind-light renewable energy power generator set and the like.
fG,t＝fF,t+fW,t+fPV,t
In the formula (f)F,t、fW,t、fPV,tThe running cost of the thermal power generation unit, the operation and maintenance cost of the wind power generation and the photovoltaic power generation in each time period are respectively.
(1) Operating costs of thermal power generating units
Due to the fact that renewable energy sources with volatility are connected to the grid on a large scale, the climbing and starting times of a traditional thermal power generating unit are increased, deep peak shaving cost is not negligible, and according to the characteristics of a steam turbine, when the load of the steam turbine is lower, heat consumption is higher, and the service life loss of the unit is extremely large. The deep peak regulation cost comprises the unit operation coal consumption cost and the unit loss cost during deep peak regulation. The thermal power generating unit can be divided into a normal operation state and a deep peak regulation state in the operation process, and the operation cost can be expressed as:
Figure FDA0003371936130000041
in the formula uF,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，ciAnd respectively 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 caused by overlarge thermal stress of the rotor. w is acostFor the extra operating cost of deep peak shaving, the calculation formula is:
Figure FDA0003371936130000043
in the formula, α represents a boundary in 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; cunitAnd purchasing machine cost for the machine set.
(2) Operation and maintenance cost of renewable energy sources such as wind, light and the like
Wind and photovoltaic power generation does not consume fuel, but normal operation of a unit is influenced by considering randomness and fluctuation of wind and light, certain operation and maintenance cost is generated, and the operation and maintenance cost can be approximately expressed as a linear relation of generating power of the unit.
Figure FDA0003371936130000042
Figure FDA0003371936130000051
In the formula, NW、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,tAnd respectively representing the limit power generation capacity of the wind power plant and the photovoltaic active output power at the moment.
The second item is the scheduling cost of the power supply, including the start-stop and climbing cost of the conventional thermal power generating unit and the limited power generation cost of the renewable energy generating unit.
fGO,t＝CF1,i+CF2,i+CWL,i+CPVL,i
(1) Start-stop and ramp-up costs of thermal power plants
The method is characterized in that large-scale renewable energy sources are connected to the grid, and intermittent and fluctuating properties of the large-scale renewable energy sources inevitably cause frequent starting, stopping and climbing of the thermal power generating unit, so that the operation cost is increased. The start stop and climb cost function is as follows:
CF1,i＝uF,i,t(1-uF,i,t-1)SF,i,t
Figure FDA0003371936130000052
in the formula uF,i,tThe number of the starting and stopping of the fire generator sets in the t-th time period is determined; sF,i,tStarting and stopping costs of the fire-electricity generating set in the t-th time period; gamma rayFAnd the coefficients are the climbing cost function coefficients of the thermal power generating unit.
(2) Limiting power generation cost of wind-solar renewable energy source unit
Figure FDA0003371936130000053
Figure FDA0003371936130000054
In the formula, cWL、cPVLLimiting the cost of electricity generation for a unit; pW,Q,jThe limited power generation capacity of the jth wind turbine generator is obtained; pPV,Q,kThe limited power generation capacity of the kth wind turbine is obtained.
The third item is the energy storage power station adjustment cost.
fE,t＝ΔPBESS,tρE
In the formula,. 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; rhoEThe cost per unit power, dollar/kW, is regulated for energy storage.
The fourth item is the scheduling cost of the demand response in the day ahead, and in the day ahead stage, only the electricity price type demand response virtual unit participates in scheduling, and the scheduling cost can be represented by the change of the electricity selling income of the power grid side:
fDR,t＝PPDR,tρt,0-PPDR,t,actρt
in the formula, ρt,0Is the initial electricity price at the time t; rhotThe price of electricity at time t.
Constraint conditions of a day-ahead low-carbon economic dispatching model:
(1) system load balancing constraints
And for any moment, the sum of the output of the power generation and energy storage thermal power generating unit, the fan, the photovoltaic power station, the water pumping power station and the storage battery power station of the system is equal to the load after the system participates in demand response.
Figure FDA0003371936130000061
In the formula, PL,act,tIs the actual power of the system load at time t.
(2) Generator set restraint
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.
And (3) output power constraint of the wind driven generator:
0≤PW,j≤PW,j,r
in the formula, PW,j,rRated output power for each fan is provided by the wind power plant. Actual output P of fanW,jThe variation range is as follows:
Figure FDA0003371936130000062
in the formula (I), the compound is shown in the specification,
Figure FDA0003371936130000063
the maximum output of the jth wind turbine at each moment of the wind turbine is obtained according to the predicted wind speed, and the maximum output is variable and fluctuating. The output power constraint of the photovoltaic power station is the same as above.
(3) Battery energy storage power station constraints
The power requirement of the storage battery energy storage power station meets the following requirements:
0≤PB,c≤PB,c,max
0≤PB≤PB，max
in the formula，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 energy storage power station has the following electric quantity balance and electric power constraint:
WB,t+1＝WB,t+PB,tΔt
WB,min≤WB,t≤WB,max
in the formula, WB,min、WB,maxRespectively the minimum and maximum residual electric quantity of the energy storage power station; wB,tAnd the residual electric quantity of the energy storage power station in the t-th time period.
6. The green dispatching method for the power system containing the multiple types of new energy sources as claimed in claim 1, wherein: the specific steps of the step 4 comprise:
(1) initializing algorithm parameters, generating bat individual gene sequences, setting parameters of fans, photovoltaics, loads and the like in individual gene segments according to a source-load uncertainty model, determining the number of iterations for 1000 times,
an objective function f (X) for setting the total bat number NpopPosition X0The flying speed v0Loudness A0And frequency f0；
(2) Randomly generating N within the feasible range of the control variablepopOnly the bat is taken as the current optimal solution set, the objective function values under the current bat gene sequence are calculated and sorted from small to large, and the bat individuals which are 1/2 before the sorting are reserved for updating and propagating processes;
(3) the iterative calculation is started. Changing frequency and updating individual speed, and selecting, speed advancing average crossing and mutation operations by taking the new bat population as a parent to form the first half part of the daughter bat population;
(4) updating the position of the parent bat population individual and judging whether the random number rand1 meets the local search condition>rIIf yes, selecting an optimal solution to perform local search to obtain a new position to replace the original position;
(5) determine a new fitness value ofWhether or not f (X) is satisfiedI)＜fbestAnd a random number rand2<AI. If so, accepting the new solution to form the latter half of the daughter bat population, and updating the objective function value of the bat individual.
(6) The objective function values of the whole filial generation population are ranked and evaluated, and the current optimal position (solution) X is updated*。
(7) Judging whether the maximum iteration times are exceeded or not, and if the maximum iteration times are exceeded, returning to the step (2); otherwise, the algorithm is ended and the optimal value is output.
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CN116722547A (en) * 2023-08-09 2023-09-08 深圳江行联加智能科技有限公司 Virtual power plant demand response regulation and control method, device, equipment and storage medium
CN116760025A (en) * 2023-06-25 2023-09-15 南京国电南自电网自动化有限公司 Risk scheduling optimization method and system for electric power system
CN117154736A (en) * 2023-09-01 2023-12-01 华能罗源发电有限责任公司 Method and system for optimizing deep peak shaving of thermal power unit by participation of hybrid energy storage system
CN117374974A (en) * 2023-12-06 2024-01-09 国网浙江省电力有限公司 Distribution network scheduling method, system, medium and equipment
CN117559490A (en) * 2023-03-22 2024-02-13 长沙学院 Multi-dimensional collaborative scheduling method for energy storage power station based on carbon emission reduction

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CN117559490A (en) * 2023-03-22 2024-02-13 长沙学院 Multi-dimensional collaborative scheduling method for energy storage power station based on carbon emission reduction
CN117559490B (en) * 2023-03-22 2024-03-29 长沙学院 Multi-dimensional collaborative scheduling method for energy storage power station based on carbon emission reduction
CN116760025A (en) * 2023-06-25 2023-09-15 南京国电南自电网自动化有限公司 Risk scheduling optimization method and system for electric power system
CN116722547A (en) * 2023-08-09 2023-09-08 深圳江行联加智能科技有限公司 Virtual power plant demand response regulation and control method, device, equipment and storage medium
CN116722547B (en) * 2023-08-09 2024-03-26 深圳江行联加智能科技有限公司 Virtual power plant demand response regulation and control method, device, equipment and storage medium
CN117154736A (en) * 2023-09-01 2023-12-01 华能罗源发电有限责任公司 Method and system for optimizing deep peak shaving of thermal power unit by participation of hybrid energy storage system
CN117374974A (en) * 2023-12-06 2024-01-09 国网浙江省电力有限公司 Distribution network scheduling method, system, medium and equipment

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