CN111310957A - Internet and intelligent power marketing optimization scheduling method based on big data theory - Google Patents

Internet and intelligent power marketing optimization scheduling method based on big data theory Download PDF

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CN111310957A
CN111310957A CN201811517964.8A CN201811517964A CN111310957A CN 111310957 A CN111310957 A CN 111310957A CN 201811517964 A CN201811517964 A CN 201811517964A CN 111310957 A CN111310957 A CN 111310957A
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王忠锋
胡博
李力刚
黄剑龙
谷万江
李言谙
金宇坤
关明
刘君
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State Grid Corp of China SGCC
Shenyang Institute of Automation of CAS
State Grid Liaoning Electric Power Co Ltd
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Shenyang Institute of Automation of CAS
State Grid Liaoning Electric Power Co Ltd
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Abstract

The invention relates to an internet and intelligent electric power marketing optimization scheduling method based on big data theory, which comprises the following steps: establishing an energy supply equipment model for Internet and intelligent power marketing, and confirming constraint conditions of the energy supply equipment model; analyzing the optimization targets of power enterprises and power customers inside the intelligent power marketing system according to the structure of Internet and intelligent power marketing; and establishing a parallel big data model of the Internet and the intelligent electric power marketing, and performing optimized scheduling on the Internet and the intelligent electric power marketing. The method realizes the optimized scheduling of Internet and intelligent power marketing, effectively solves the problem that the respective optimization of multiple optimized main bodies cannot be realized in the conventional optimized scheduling, realizes the aims of minimizing the energy cost of power customers and maximizing the energy supply income of power enterprises, and has ingenious method and good application prospect.

Description

Internet and intelligent power marketing optimization scheduling method based on big data theory
Technical Field
The invention relates to the technical field of power systems, in particular to an internet and intelligent power marketing optimization scheduling method based on big data theory.
Background
Based on advanced information technologies such as the internet, cloud computing and big data, core services such as power marketing service, electricity purchasing and selling, distributed photovoltaic power generation and the like of a national power company are fully combined, research and application work of key core technologies is developed, technical bottlenecks are broken through, interaction between a power grid and users is better realized, the energy efficiency level is improved, and a novel state of Internet plus is developed.
The novel electricity marketing form is constructed by utilizing advanced technologies such as the Internet of things, cloud computing and big data and combining core services such as electricity marketing service, electricity purchasing and selling and distributed photovoltaic power generation, the electricity change point province (autonomous region) is selected to develop application verification, point-to-point transaction, real-time distribution and subsidy settlement of green electricity are realized, electronic commerce and Internet financial health development are promoted, an electric power enterprise and an electric power client are guided to expand the market, a novel internet plus industry state is developed, and a new economic growth point is created.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an optimized scheduling method of Internet and intelligent electric power marketing based on a big data theory, which solves the problem that how to realize respective optimization targets of electric power customers and electric power enterprises by participants of the existing networking and electric power sellers, and solves the problem that the respective optimization of multiple optimization main bodies cannot be realized in the conventional optimized electric power marketing.
The technical scheme adopted by the invention for realizing the purpose is as follows:
an Internet + intelligent electric power marketing optimization scheduling method based on big data theory comprises the following steps:
step 1: establishing an energy supply equipment model for Internet and intelligent power marketing, and confirming constraint conditions of the energy supply equipment model;
step 2: analyzing the optimization targets of power enterprises and power customers inside the intelligent power marketing system according to the structure of Internet and intelligent power marketing;
and step 3: and establishing a parallel big data model of the Internet and the intelligent electric power marketing, and performing optimized scheduling on the Internet and the intelligent electric power marketing.
Establish energy supply equipment model of internet + wisdom electric power marketing including: establishing a model of a schedulable distributed power supply, establishing a model of a non-schedulable distributed power supply, establishing a model of auxiliary energy supply equipment and establishing a model of energy storage equipment; wherein
The model of the schedulable distributed power supply is as follows:
Pi,DDG(t)＝δi,DDG·f(Qi,gas(t))
wherein, Pi,DDG(t) the output of the schedulable distributed power supply at time t; deltai,DDGThe on-off state of the schedulable distributed power supply is 1 and 0; qi,gas(t) the electricity purchased and sold consumed by the distributable power supply can be scheduled at the moment t; f (Q)i,gas(t)) is a corresponding relation between the electricity generated by the schedulable distributed power supply and the consumed purchased and sold electricity;
the model of the non-dispatchable distributed power source includes: the method comprises the following steps of photovoltaic power generation and wind power generation, wherein the output of the photovoltaic power generation and the wind power generation at the moment t is predicted according to historical data;
the auxiliary energy supply equipment comprises cold supply equipment and heat supply equipment; the model of the auxiliary energy supply equipment is as follows:
Pi,au(t)＝g(Pi,in(t))
wherein, Pi,au(t) is the output of the auxiliary equipment at time t; pi,in(t) is an input to the auxiliary device at time t; g (P)i,in(t)) is the output of the auxiliary device versus the input;
the energy storage equipment comprises a storage battery, cold accumulation equipment and a heat accumulation groove; the model of the energy storage equipment is as follows:
Figure BDA0001902506240000021
wherein, Δ t is the interval from time t to time (t + 1); ei,ESD(t) the energy stored by the energy storage device at time t, in kWh; pi,ESD,in(t) is the energy storage power at the moment t, and the unit is kw; pi,ESD,out(t) discharge power at time t in kW ηi,ESD,inFor energy storage efficiency ηi,ESD,outFor energy release efficiency; mu.si,ESDIs the energy loss coefficient.
The constraint condition for confirming the energy supply equipment model comprises the following steps:
first, constraints of the energy supply device are established, and then, operational mechanism constraints of the energy supply device are established.
The establishing constraints of the energy supply device comprises:
establishing the constraint of the schedulable distributed power supply:
Pi,DDG,min·δi,DDG≤Pi,DDG(t)≤Pi,DDG,max·δi,DDG
-Ri,DDG,max≤Pi,DDG(t+1)-Pi,DDG(t)≤Ri,DDG,max
wherein, Pi,DDG,minAnd Pi,DDG,maxThe minimum and maximum values of the output, R, of the dispatchable distributed power supply, respectivelyi,DDG,maxThe maximum value of the output value change of the schedulable distributed power supply; pi，DDG(t +1) is the output of the schedulable distributed power supply at the time of t + 1;
establishing constraints of the non-dispatchable distributed power supply:
considering the abandoned wind rate and the abandoned light rate, the output values of the photovoltaic power generation and the wind power generation are limited as follows:
0≤Pi,WT(t)≤Pi,WT,max
0≤Pi,PV(t)≤Pi,PV,max
wherein, Pi,WT(t) and Pi,PV(t) the output of wind power generation and photovoltaic power generation at the moment t respectively; pi,WT,maxAnd Pi,PV,maxThe maximum values of the output of wind power generation and photovoltaic power generation are respectively;
establishing constraints of the auxiliary function device:
0≤Pi,au(t)≤Pi,au,max
wherein, Pi,au,maxIs the maximum output of the auxiliary energy supply equipment;
establishing constraints of the energy storage equipment:
Ei,ESD,min≤Ei,ESD(t)≤Ei,ESD,max
δi,ESD,in(t)·Pi,ESD,in,min≤Pi,ESD,in(t)≤δi,ESD,in(t)·Pi,ESD,in,max
δi,ESD,out(t)·Pi,ESD,out,min≤Pi,ESD,out(t)≤δi,ESD,out(t)·Pi,ESD,out,max
δi,ESD,in(t)+δi,ESD,out(t)≤1
wherein E isi,ESD,minAnd Ei,ESD,maxRespectively representing the minimum value and the maximum value of the capacity of the energy storage equipment; pi,ESD,in,minAnd Pi,ESD,in,maxRespectively the minimum value and the maximum value of the energy storage power of the energy storage equipment; pi,ESD,out,minAnd Pi,ESD,out,maxRespectively the minimum value and the maximum value of the energy discharge power of the energy storage equipment; deltai,ESD,in(t) the energy storage state of the energy storage equipment at the moment t, the non-working state is represented when the value is 0, and the energy storage is represented when the value is 1; deltai,ESD,out(t) is the energy release state of the energy storage equipment at the moment t, and when the value is 0, the energy storage equipment does not work, and is takenWhen the value is 1, discharging is indicated, and the energy storage and discharging of the energy storage equipment cannot be performed simultaneously.
The operation mechanism constraints of the energy supply device comprise: supply-demand balance constraints and power flow constraints, wherein
The supply and demand balance constraint comprises an electric balance constraint, a heat balance constraint and a cold balance constraint;
the power flow constraints include voltage constraints and power constraints.
The structure of internet + wisdom electric power marketing does:
the Internet and intelligent power marketing is in a limited area, and the limited area comprises a plurality of power customers and a plurality of power enterprises;
the power customers provide user load requirements through respective energy supply equipment; and/or
Purchasing electric energy from an electric power enterprise and providing the electric energy for users; and/or
Cold and hot energy sources are purchased from other electric power customers and are provided for users; and/or
Surplus electric energy of the power customer is sold to power enterprises, and surplus cold and hot energy is sold to other power users;
electric power enterprise, supplying electric energy to electric power customers, and/or
Electrical energy is purchased from an electrical customer.
The optimization targets of the power enterprise are as follows:
Cu(t)＝f(Pu(t))
Figure BDA0001902506240000041
wherein, Pu(t) represents the amount of electrical energy produced by the power utility; cu(t) represents the corresponding cost of electricity production; p is a radical ofu,in(t) and pu,out(t) respectively representing the prices of electricity purchased and sold by the power company from the power customer; t is the time period of the optimized schedule; ΨuIs the operating cost of the power enterprise;
the optimization goals of the power customer are as follows:
Ci,DDG(t)＝f(Pi,DDG(t))
Figure BDA0001902506240000051
wherein, Ci,DDG(t) represents the electricity production cost of the dispatchable distributed power supply; ci,OM,DDG(t)、Ci,OM,PV(t)、Ci,OM,WT(t)、Ci,OM,au(t)、Ci,OM,ESD(t) sequentially representing the operation and maintenance cost of unit output of the dispatchable power supply equipment, the photovoltaic power, the wind power, the auxiliary equipment and the storage battery; pi,in(t) and Pi,out(t) represents the prices of the electricity purchased and sold by the electric power customer from the electric power company or other electric power customers, respectively, Pi,in(t) and Pi,out(t) representing the amount of energy purchased and sold by the power company from the power customer, respectively; ΨiIs the cost of use for the electricity consumer.
The parallel big data model of establishing internet + wisdom electric power marketing, carry out the optimal scheduling to internet + wisdom electric power marketing and include:
step 3.1: inputting information and related parameters of optimized scheduling in an energy supply equipment model of Internet + intelligent power marketing;
step 3.2: the method comprises the steps that power enterprises and power customers in Internet + intelligent power marketing determine a big data strategy set according to respective optimization targets;
step 3.3: setting an initial value of a big data balance point of a power customer, and providing the initial value of the big data balance point for a power enterprise;
step 3.4: performing master-slave big data between the power customer and the power enterprise, calculating corresponding income and energy price by the power enterprise, and informing the power customer;
step 3.5: non-cooperative big data exists among power customers, a fluctuation penalty function is introduced,
Figure BDA0001902506240000052
wherein the content of the first and second substances,
Figure BDA0001902506240000061
is a fluctuation penalty function of the kth game iteration;
Figure BDA0001902506240000062
is a fluctuation penalty factor of the kth game iteration; the fluctuation penalty function quantifies the fluctuation value of each schedulable energy supply device;
Figure BDA0001902506240000063
the game processing method comprises the steps that the stored energy power of a power customer, the output of energy storage equipment, the output of schedulable equipment and the output of auxiliary equipment at the time t in the kth game iteration are respectively;
Figure BDA0001902506240000064
the game iteration of the kth-1 th time comprises the energy storage power of the power client, the output of energy storage equipment, the output of schedulable equipment and the output of auxiliary equipment at the t moment;
step 3.6: the power customers are optimized in parallel, the optimized objective function is the energy consumption cost and the penalty cost of the power customers,
Figure BDA0001902506240000065
wherein the content of the first and second substances,
Figure BDA0001902506240000066
the energy consumption cost of the power customer of the kth game iteration is obtained, the power customer obtains the optimal operation plan of the kth iteration and transmits the optimal operation plan to the power enterprise;
step 3.7: and judging whether the big data of the power enterprise and the corresponding power customer reach balance or not according to the optimal operation plan, if so, outputting the optimal operation plan, and otherwise, returning to the step 3.4.
The equalization is that the fluctuation range of each schedulable energy supply device in two continuous iterations is not more than the threshold value, and the equalization method comprises the following steps:
the fluctuation range of the schedulable distributed power supply in two continuous iterations is not greater than the fluctuation threshold of the schedulable distributed power supply;
Figure BDA0001902506240000067
wherein epsilon1Is a fluctuation threshold of the schedulable distributed power supply;
Figure BDA0001902506240000068
for the schedulable device contribution at time t in the kth game iteration,
Figure BDA0001902506240000069
the contribution of the schedulable device at time t in the kth-1 game iteration,
the fluctuation range of the energy storage equipment in two continuous iterations is not greater than the fluctuation threshold of the energy storage power of the energy storage equipment and the fluctuation threshold of the energy discharge power of the energy storage equipment;
Figure BDA0001902506240000071
Figure BDA0001902506240000072
wherein epsilon2Is the fluctuation threshold of the energy storage power of the energy storage equipment3Is the fluctuation threshold of the discharging power of the energy storage equipment;
Figure BDA0001902506240000073
the game iteration of the kth time is the game iteration of the kth time, and the game iteration of the kth time is the game iteration of the kth time;
Figure BDA0001902506240000074
the energy storage power of the power customer and the output of the energy storage equipment at the moment t in the kth-1 th game iteration are respectively;
the range of fluctuation of the auxiliary device in two consecutive iterations is not greater than the fluctuation threshold of the auxiliary device,
Figure BDA0001902506240000075
wherein epsilon4Is the fluctuation threshold of the auxiliary equipment,
Figure BDA0001902506240000076
is the contribution of the auxiliary equipment at time t in the kth game iteration;
Figure BDA0001902506240000077
is the contribution of the auxiliary device at time t in the (k-1) th game iteration.
The invention has the following beneficial effects and advantages:
according to the method, the operation constraint of the energy supply equipment of the Internet and the intelligent power marketing is determined by establishing a model of the energy supply equipment, obtaining the optimization targets of the power customers and the power enterprises in the Internet and the intelligent power marketing, and establishing a big data model between the power enterprises and the power customers and between the power customers in the Internet and the intelligent power marketing, so that the optimization scheduling of the Internet and the intelligent power marketing is realized, the problem that the optimization of multiple optimization main bodies cannot be realized respectively in the conventional optimization scheduling is effectively solved, the purposes of minimizing the energy cost of the power customers and maximizing the energy supply income of the power enterprises are realized, and the method is ingenious and has a good application prospect.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather should be construed as modified in the spirit and scope of the present invention as set forth in the appended claims.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Fig. 1 shows a flow chart of the method of the present invention.
An internet and intelligent electric power marketing optimization scheduling method based on big data theory comprises the following steps,
step (A), establishing an energy supply equipment model for Internet and intelligent electric power marketing, and confirming constraint conditions of the energy supply equipment model;
analyzing the optimization targets of the power enterprises and the power customers inside the intelligent power marketing system according to the structure of Internet and intelligent power marketing;
and (C) establishing a parallel big data model of the Internet and the intelligent electric power marketing, and realizing the optimized scheduling of the Internet and the intelligent electric power marketing.
The optimized scheduling method of Internet and intelligent electric marketing based on big data theory, step (A), establishing energy supply equipment model of Internet and intelligent electric marketing, includes the following steps,
(A11) establishing a model of the schedulable distributed power source, the model being represented as follows,
Pi,DDG(t)＝δi,DDG·f(Qi,gas(t))；
wherein, Pi,DDG(t) the output of the schedulable distributed power supply at time t; deltai,DDGThe on-off state of the schedulable distributed power supply is 1 and 0; qi,gas(t) the electricity purchased and sold consumed by the distributable power supply can be scheduled at the moment t; f (Q)i,gas(t)) is a corresponding relation between the electricity generated by the schedulable distributed power supply and the consumed purchased and sold electricity;
(A12) establishing a model of the non-dispatchable distributed power supply:
the method comprises the following steps that a non-dispatchable distributed power supply comprises photovoltaic power generation and wind power generation, and the output of the photovoltaic power generation and the wind power generation at the time t is predicted according to historical data;
(A13) establishing a model of auxiliary energy supply equipment:
auxiliary energy supply equipment, including cooling equipment and heating equipment, the model is expressed as follows,
Pi,au(t)＝g(Pi,in(t))；
wherein Pi, au (t) is the output of the auxiliary equipment at time t; pi,in(t) is an input to the auxiliary device at time t; g (P)i,in(t)) is the output of the auxiliary device versus the input;
(A14) establishing a model of the energy storage equipment:
the energy storage equipment comprises a storage battery, a cold accumulation device and a heat accumulation groove, the model is expressed as follows,
wherein, Δ t is the interval from time t to time (t + 1); ei,ESD(t) the energy stored by the energy storage device at time t, in kWh; pi,ESD,in(t) the energy storage power at the moment t, wherein the unit is kW; pi,ESD,out(t) discharge power at time t in kW ηi,ESD,inFor energy storage efficiency ηi,ESD,outFor energy release efficiency; mu.si,ESDIs the energy loss coefficient.
The method for optimizing and scheduling Internet and intelligent power marketing based on big data theory, step (A) and confirming the constraint conditions thereof, comprises the following steps,
(A21) establishing constraints of the energy supply equipment:
the constraints on the schedulable distributed power supply are as follows,
Pi,DDG,min·δi,DDG≤Pi,DDG(t)≤Pi,DDG,max·δi,DDG
-Ri,DDG,max≤Pi,DDG(t+1)-Pi,DDG(t)≤Ri,DDG,max
wherein, Pi,DDG,minAnd Pi,DDG,maxThe minimum and maximum values of the output, R, of the dispatchable distributed power supply, respectivelyi,DDG,maxThe maximum value of the output value change of the schedulable distributed power supply; pi，DDG(t +1) is the output of the schedulable distributed power supply at the time of t + 1;
the constraints of a non-dispatchable distributed power supply are as follows,
considering the abandoned wind rate and the abandoned light rate, the output values of the photovoltaic power generation and the wind power generation are limited as follows:
0≤Pi,WT(t)≤Pi,WT,max
0≤Pi,PV(t)≤Pi,PV,max
wherein, Pi,WT(t) and Pi,PV(t) the output of wind power generation and photovoltaic power generation at the moment t respectively; pi,WT,maxAnd Pi,PV,maxThe maximum values of the output of wind power generation and photovoltaic power generation are respectively;
the constraints of the auxiliary energy supply device are as follows,
0≤Pi,au(t)≤Pi,au,max
wherein, Pi,au,maxIs the maximum output of the auxiliary energy supply equipment;
the constraints of the energy storage device are as follows,
Ei,ESD,min≤Ei,ESD(t)≤Ei,ESD,max
δi,ESD,in(t)·Pi,ESD,in,min≤Pi,ESD,in(t)≤δi,ESD,in(t)·Pi,ESD,in,max
δi,ESD,out(t)·Pi,ESD,out,min≤Pi,ESD,out(t)≤δi,ESD,out(t)·Pi,ESD,out,max
δi,ESD,in(t)+δi,ESD,out(t)≤1
wherein the content of the first and second substances,Ei,ESD,minand Ei,ESD,maxRespectively representing the minimum value and the maximum value of the capacity of the energy storage equipment; pi,ESD,in,minAnd Pi,ESD,in,maxRespectively the minimum value and the maximum value of the energy storage power of the energy storage equipment; pi,ESD,out,minAnd Pi,ESD,out,maxRespectively the minimum value and the maximum value of the energy discharge power of the energy storage equipment; deltai,ESD,in(t) the energy storage state of the energy storage equipment at the moment t, the non-working state is represented when the value is 0, and the energy storage is represented when the value is 1; deltai,ESD,out(t) the energy release state of the energy storage equipment at the moment t, the energy release state is represented when the value is 0, the energy release state is represented when the value is 1, and the energy storage and the energy release of the energy storage equipment cannot be carried out simultaneously;
(A22) establishing an operation mechanism constraint of the energy supply equipment:
a supply and demand balance constraint comprising: electrical balance constraints, thermal balance constraints, cold balance constraints;
a power flow constraint comprising: voltage constraints and power constraints.
The optimized scheduling method of Internet and intelligent electric power marketing based on big data theory, step (B), according to the structure of Internet and intelligent electric power marketing, analyzes the optimization targets of the electric power enterprise and the electric power customer in the method, comprises the following steps,
(B1) analysis internet + wisdom electric power marketing's structure:
the method comprises the following steps that a plurality of power customers and power enterprises are arranged in an internet and smart power marketing area, the power customers can meet user load requirements through respective energy supply equipment, electric energy can be purchased from the power enterprises to meet the user load requirements, or cold and hot energy sources are purchased from other power customers to meet the user load requirements, redundant electric energy of the power customers can be sold to the power enterprises, and redundant cold and hot energy sources can be sold to other power customers; the power generated by the power enterprise is supplied to each power customer, and the power customers can purchase electric energy;
(B2) and determining the optimization targets of the power enterprise and the power customer:
for power enterprises, including the operation cost, the electric energy production cost, the purchase electric energy cost and the income of selling electric energy need to be considered, the optimization goals are as follows:
Cu(t)＝f(Pu(t))
wherein, Pu(t) represents the amount of electrical energy produced by the power utility; cu(t) represents the corresponding cost of electricity production; p is a radical ofu,in(t) and pu,out(t) respectively representing the prices of electricity purchased and sold by the power company from the power customer; pu,in(t) and Pu,out(t) respectively representing electric energy purchased and sold by the electric power enterprise from the electric power customer; t is the time period of the optimized schedule; ΨuIs the operating cost of the power enterprise;
for power customers, including energy use costs that require consideration of maintenance costs of energy supply equipment, costs of producing power from distributed power sources, costs of purchasing energy from power enterprises and other power customers, and revenue for selling energy to power enterprises and other power customers, optimization objectives are as follows:
Ci,DDG(t)＝f(Pi,DDG(t))
Figure BDA0001902506240000111
wherein, Ci,DDG(t) represents the electricity production cost of the dispatchable distributed power supply; ci,OM,DDG(t)、Ci,OM,PV(t)、Ci,OM,WT(t)、Ci,OM,au(t)、Ci,OM,ESD(t) sequentially representing the operation and maintenance cost of unit output of the dispatchable power supply equipment, the photovoltaic power, the wind power, the auxiliary equipment and the storage battery; pi,in(t) and Pi,out(t) represents the price of energy purchased and sold by the electric power customer from the electric power company or other electric power customers, respectively, Pi,in(t) and Pi,out(t) representing the amount of energy purchased and sold by the power company from the power customer, respectively; Ψ i is the cost of use for the power customer.
The optimized scheduling method of the Internet and the intelligent electric power marketing based on the big data theory comprises the following steps of (C) establishing a parallel big data model of the Internet and the intelligent electric power marketing to realize the optimized scheduling of the Internet and the intelligent electric power marketing,
(C1) inputting information and related parameters of optimized scheduling by the energy supply equipment model of the internet and intelligent power marketing obtained in the step (A);
(C2) determining a big data strategy set by the power enterprises and power customers in the Internet + intelligent power marketing according to the respective optimization targets obtained in the step (B);
(C3) setting an initial value of a big data balance point of a power customer, and providing the initial value of the big data balance point for a power enterprise;
(C4) carrying out master-slave big data between the power customer and the power enterprise, calculating corresponding income and energy price by the power enterprise, and informing the price to the power customer;
(C5) non-cooperative big data exists among all power customers, a fluctuation penalty function is introduced,
Figure BDA0001902506240000121
wherein the content of the first and second substances,
Figure BDA0001902506240000122
is a fluctuation penalty function of the kth game iteration;
Figure BDA0001902506240000123
is a fluctuation penalty factor of the kth game iteration; the fluctuation penalty function quantifies the fluctuation value of each schedulable energy supply device;
Figure BDA0001902506240000124
the game processing method comprises the steps that the stored energy power of a power customer, the output of energy storage equipment, the output of schedulable equipment and the output of auxiliary equipment at the time t in the kth game iteration are respectively;
Figure BDA0001902506240000125
the game iteration of the kth-1 th time comprises the energy storage power of the power client, the output of energy storage equipment, the output of schedulable equipment and the output of auxiliary equipment at the t moment;
(C6) the power customer is optimized in parallel, and the optimized objective function is the energy consumption cost and the penalty cost of the power customer, which are specifically expressed as:
Figure BDA0001902506240000126
wherein the content of the first and second substances,
Figure BDA0001902506240000127
the energy consumption cost of the power customer of the kth game iteration is obtained, the power customer obtains the optimal operation plan of the kth iteration and transmits the optimal operation plan to the power enterprise;
(C7) and judging whether the big data of the power enterprise and the corresponding power customer reach balance or not according to the optimal operation plan, if so, outputting the optimal operation plan, and otherwise, returning to the step (C4) to continue execution.
In the foregoing optimized scheduling method for internet + smart power marketing based on big data theory, (C7), whether the big data of the power enterprise and the corresponding power customer is balanced is determined according to the optimal operation plan, and the balancing standard is that the fluctuation range of each schedulable energy supply device in two consecutive iterations is less than or equal to the threshold, specifically as follows:
the fluctuation range of the schedulable distributed power supply in two continuous iterations is less than or equal to the fluctuation threshold of the schedulable distributed power supply,
Figure BDA0001902506240000131
wherein epsilon1Is a fluctuation threshold of the schedulable distributed power supply;
Figure BDA0001902506240000132
for the schedulable device contribution at time t in the kth game iteration,
Figure BDA0001902506240000133
the contribution of the schedulable device at time t in the kth-1 game iteration,
the fluctuation range of the energy storage equipment in two continuous iterations is less than or equal to the fluctuation threshold of the energy storage power of the energy storage equipment and the fluctuation threshold of the energy discharge power of the energy storage equipment,
Figure BDA0001902506240000134
Figure BDA0001902506240000135
wherein epsilon2Is the fluctuation threshold of the energy storage power of the energy storage equipment3Is the fluctuation threshold of the discharging power of the energy storage equipment;
Figure BDA0001902506240000136
the game iteration of the kth time is the game iteration of the kth time, and the game iteration of the kth time is the game iteration of the kth time;
Figure BDA0001902506240000137
the energy storage power of the power customer and the output of the energy storage equipment at the moment t in the kth-1 th game iteration are respectively;
the range of fluctuation of the auxiliary device in two consecutive iterations is not greater than the fluctuation threshold of the auxiliary device,
Figure BDA0001902506240000138
wherein epsilon4Is the fluctuation threshold of the auxiliary equipment,
Figure BDA0001902506240000139
is the contribution of the auxiliary equipment at time t in the kth game iteration;
Figure BDA0001902506240000141
is the contribution of the auxiliary device at time t in the (k-1) th game iteration.

Claims (9)

1. An Internet + intelligent electric power marketing optimization scheduling method based on big data theory is characterized by comprising the following steps:
step 1: establishing an energy supply equipment model for Internet and intelligent power marketing, and confirming constraint conditions of the energy supply equipment model;
step 2: analyzing the optimization targets of power enterprises and power customers inside the intelligent power marketing system according to the structure of Internet and intelligent power marketing;
and step 3: and establishing a parallel big data model of the Internet and the intelligent electric power marketing, and performing optimized scheduling on the Internet and the intelligent electric power marketing.
2. The optimized scheduling method for internet + smart power marketing based on big data theory as claimed in claim 1, wherein: establish energy supply equipment model of internet + wisdom electric power marketing including: establishing a model of a schedulable distributed power supply, establishing a model of a non-schedulable distributed power supply, establishing a model of auxiliary energy supply equipment and establishing a model of energy storage equipment; wherein
The model of the schedulable distributed power supply is as follows:
Pi,DDG(t)＝δi,DDG·f(Qi,gas(t))
wherein, Pi,DDG(t) the output of the schedulable distributed power supply at time t; deltai,DDGThe on-off state of the schedulable distributed power supply is 1 and 0; qi,gas(t) the electricity purchased and sold consumed by the distributable power supply can be scheduled at the moment t; f (Q)i,gas(t)) is a corresponding relation between the electricity generated by the schedulable distributed power supply and the consumed purchased and sold electricity;
the model of the non-dispatchable distributed power source includes: the method comprises the following steps of photovoltaic power generation and wind power generation, wherein the output of the photovoltaic power generation and the wind power generation at the moment t is predicted according to historical data;
the auxiliary energy supply equipment comprises cold supply equipment and heat supply equipment; the model of the auxiliary energy supply equipment is as follows:
Pi,au(t)＝g(Pi,in(t))
wherein, Pi,au(t) is the output of the auxiliary equipment at time t; pi,in(t) is an input to the auxiliary device at time t; g (P)i,in(t)) is the output of the auxiliary device versus the input;
the energy storage equipment comprises a storage battery, cold accumulation equipment and a heat accumulation groove; the model of the energy storage equipment is as follows:
Figure FDA0001902506230000021
wherein, Δ t is the interval from time t to time (t + 1); ei,ESD(t) the energy stored by the energy storage device at time t, in kWh; pi,ESD,in(t) is the energy storage power at the moment t, and the unit is kw; pi,ESD,out(t) discharge power at time t in kW ηi,ESD,inFor energy storage efficiency ηi,ESD,outFor energy release efficiency; mu.si,ESDIs the energy loss coefficient.
3. The optimized scheduling method for internet + smart power marketing based on big data theory as claimed in claim 1, wherein: the constraint condition for confirming the energy supply equipment model comprises the following steps:
first, constraints of the energy supply device are established, and then, operational mechanism constraints of the energy supply device are established.
4. The optimized scheduling method for Internet + intelligent electric power marketing based on big data theory as claimed in claim 3, wherein: the establishing constraints of the energy supply device comprises:
establishing the constraint of the schedulable distributed power supply:
Pi,DDG,min·δi,DDG≤Pi,DDG(t)≤Pi,DDG,max·δi,DDG
-Ri,DDG,max≤Pi,DDG(t+1)-Pi,DDG(t)≤Ri,DDG,max
wherein, Pi,DDG,minAnd Pi,DDG,maxThe minimum and maximum values of the output, R, of the dispatchable distributed power supply, respectivelyi,DDG,maxThe maximum value of the output value change of the schedulable distributed power supply; pi，DDG(t +1) is the output of the schedulable distributed power supply at the time of t + 1;
establishing constraints of the non-dispatchable distributed power supply:
considering the abandoned wind rate and the abandoned light rate, the output values of the photovoltaic power generation and the wind power generation are limited as follows:
0≤Pi,WT(t)≤Pi,WT,max
0≤Pi,PV(t)≤Pi,PV,max
wherein, Pi,WT(t) and Pi,PV(t) the output of wind power generation and photovoltaic power generation at the moment t respectively; pi,WT,maxAnd Pi,PV,maxThe maximum values of the output of wind power generation and photovoltaic power generation are respectively;
establishing constraints of the auxiliary function device:
0≤Pi,au(t)≤Pi,au,max
wherein, Pi,au,maxIs the maximum output of the auxiliary energy supply equipment;
establishing constraints of the energy storage equipment:
Ei,ESD,min≤Ei,ESD(t)≤Ei,ESD,max
δi,ESD,in(t)·Pi,ESD,in,min≤Pi,ESD,in(t)≤δi,ESD,in(t)·Pi,ESD,in,max
δi,ESD,out(t)·Pi,ESD,out,min≤Pi,ESD,out(t)≤δi,ESD,out(t)·Pi,ESD,out,max
δi,ESD,in(t)+δi,ESD,out(t)≤1
wherein E isi,ESD,minAnd Ei,ESD,maxRespectively representing the minimum value and the maximum value of the capacity of the energy storage equipment; pi,ESD,in,minAnd Pi,ESD,in,maxRespectively the minimum value and the maximum value of the energy storage power of the energy storage equipment; pi,ESD,out,minAnd Pi,ESD,out,maxRespectively the minimum value and the maximum value of the energy discharge power of the energy storage equipment; deltai,ESD,in(t) the energy storage state of the energy storage equipment at the moment t, the non-working state is represented when the value is 0, and the energy storage is represented when the value is 1; deltai,ESD,outAnd (t) is the energy release state of the energy storage equipment at the moment t, the energy release state is represented when the value is 0, the energy release state is represented when the value is 1, and the energy storage and the energy release of the energy storage equipment cannot be carried out simultaneously.
5. The optimized scheduling method for Internet + intelligent electric power marketing based on big data theory as claimed in claim 3, wherein: the operation mechanism constraints of the energy supply device comprise: supply-demand balance constraints and power flow constraints, wherein
The supply and demand balance constraint comprises an electric balance constraint, a heat balance constraint and a cold balance constraint;
the power flow constraints include voltage constraints and power constraints.
6. The optimized scheduling method for internet + smart power marketing based on big data theory as claimed in claim 1, wherein: the structure of internet + wisdom electric power marketing does:
the Internet and intelligent power marketing is in a limited area, and the limited area comprises a plurality of power customers and a plurality of power enterprises;
the power customers provide user load requirements through respective energy supply equipment; and/or
Purchasing electric energy from an electric power enterprise and providing the electric energy for users; and/or
Cold and hot energy sources are purchased from other electric power customers and are provided for users; and/or
Surplus electric energy of the power customer is sold to power enterprises, and surplus cold and hot energy is sold to other power users;
electric power enterprise, supplying electric energy to electric power customers, and/or
Electrical energy is purchased from an electrical customer.
7. The optimized scheduling method for internet + smart power marketing based on big data theory as claimed in claim 1, wherein: the optimization targets of the power enterprise are as follows:
Cu(t)＝f(Pu(t))
Figure FDA0001902506230000041
wherein, Pu(t) represents the amount of electrical energy produced by the power utility; cu(t) represents the corresponding cost of electricity production; p is a radical ofu,in(t) and pu,out(t) respectively representing the prices of electricity purchased and sold by the power company from the power customer; t is the time period of the optimized schedule; ΨuIs the operating cost of the power enterprise;
the optimization goals of the power customer are as follows:
Ci,DDG(t)＝f(Pi,DDG(t))
Figure FDA0001902506230000042
wherein, Ci,DDG(t) tunable distributed power supplyThe cost of electrical energy production; ci,OM,DDG(t)、Ci,OM,PV(t)、Ci,OM,WT(t)、Ci,OM,au(t)、Ci,OM,ESD(t) sequentially representing the operation and maintenance cost of unit output of the dispatchable power supply equipment, the photovoltaic power, the wind power, the auxiliary equipment and the storage battery; pi,in(t) and Pi,out(t) represents the prices of the electricity purchased and sold by the electric power customer from the electric power company or other electric power customers, respectively, Pi,in(t) and Pi,out(t) representing the amount of energy purchased and sold by the power company from the power customer, respectively; ΨiIs the cost of use for the electricity consumer.
8. The optimized scheduling method for internet + smart power marketing based on big data theory as claimed in claim 1, wherein: the parallel big data model of establishing internet + wisdom electric power marketing, carry out the optimal scheduling to internet + wisdom electric power marketing and include:
step 3.1: inputting information and related parameters of optimized scheduling in an energy supply equipment model of Internet + intelligent power marketing;
step 3.2: the method comprises the steps that power enterprises and power customers in Internet + intelligent power marketing determine a big data strategy set according to respective optimization targets;
step 3.3: setting an initial value of a big data balance point of a power customer, and providing the initial value of the big data balance point for a power enterprise;
step 3.4: performing master-slave big data between the power customer and the power enterprise, calculating corresponding income and energy price by the power enterprise, and informing the power customer;
step 3.5: non-cooperative big data exists among power customers, a fluctuation penalty function is introduced,
Figure FDA0001902506230000051
wherein the content of the first and second substances,
Figure FDA0001902506230000052
is the kth game iterationA fluctuation penalty function;
Figure FDA0001902506230000053
is a fluctuation penalty factor of the kth game iteration; the fluctuation penalty function quantifies the fluctuation value of each schedulable energy supply device;
Figure FDA0001902506230000054
the game processing method comprises the steps that the stored energy power of a power customer, the output of energy storage equipment, the output of schedulable equipment and the output of auxiliary equipment at the time t in the kth game iteration are respectively;
Figure FDA0001902506230000055
the game iteration of the kth-1 th time comprises the energy storage power of the power client, the output of energy storage equipment, the output of schedulable equipment and the output of auxiliary equipment at the t moment;
step 3.6: the power customers are optimized in parallel, the optimized objective function is the energy consumption cost and the penalty cost of the power customers,
Figure FDA0001902506230000056
wherein the content of the first and second substances,
Figure FDA0001902506230000057
the energy consumption cost of the power customer of the kth game iteration is obtained, the power customer obtains the optimal operation plan of the kth iteration and transmits the optimal operation plan to the power enterprise;
step 3.7: and judging whether the big data of the power enterprise and the corresponding power customer reach balance or not according to the optimal operation plan, if so, outputting the optimal operation plan, and otherwise, returning to the step 3.4.
9. The optimized scheduling method for internet + smart power marketing based on big data theory as claimed in claim 8, wherein: the equalization is that the fluctuation range of each schedulable energy supply device in two continuous iterations is not more than the threshold value, and the equalization method comprises the following steps:
the fluctuation range of the schedulable distributed power supply in two continuous iterations is not greater than the fluctuation threshold of the schedulable distributed power supply;
Figure FDA0001902506230000061
wherein epsilon1Is a fluctuation threshold of the schedulable distributed power supply;
Figure FDA0001902506230000064
for the schedulable device contribution at time t in the kth game iteration,
Figure FDA0001902506230000065
the contribution of the schedulable device at time t in the kth-1 game iteration,
the fluctuation range of the energy storage equipment in two continuous iterations is not greater than the fluctuation threshold of the energy storage power of the energy storage equipment and the fluctuation threshold of the energy discharge power of the energy storage equipment;
Figure FDA0001902506230000062
Figure FDA0001902506230000063
wherein epsilon2Is the fluctuation threshold of the energy storage power of the energy storage equipment3Is the fluctuation threshold of the discharging power of the energy storage equipment;
Figure FDA0001902506230000066
the game iteration of the kth time is the game iteration of the kth time, and the game iteration of the kth time is the game iteration of the kth time;
Figure FDA0001902506230000067
the energy storage power of the power customer and the output of the energy storage equipment at the moment t in the kth-1 th game iteration are respectively;
the range of fluctuation of the auxiliary device in two consecutive iterations is not greater than the fluctuation threshold of the auxiliary device,
Figure FDA0001902506230000071
wherein epsilon4Is the fluctuation threshold of the auxiliary equipment,
Figure FDA0001902506230000072
is the contribution of the auxiliary equipment at time t in the kth game iteration;
Figure FDA0001902506230000073
is the contribution of the auxiliary device at time t in the (k-1) th game iteration.
CN201811517964.8A 2018-12-12 2018-12-12 Internet and intelligent power marketing optimization scheduling method based on big data theory Pending CN111310957A (en)

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CN108334980A (en) * 2018-01-16 2018-07-27 国电南瑞科技股份有限公司 The Optimization Scheduling of internet based on game theory+wisdom energy net

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CN108334980A (en) * 2018-01-16 2018-07-27 国电南瑞科技股份有限公司 The Optimization Scheduling of internet based on game theory+wisdom energy net

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Publication number Priority date Publication date Assignee Title
CN116562597A (en) * 2023-07-07 2023-08-08 北京国网电力技术有限公司 Energy internet scheduling and controlling method
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