CN108537363B - Electricity purchasing amount control method for electricity selling company under distribution and sale separated environment - Google Patents
Electricity purchasing amount control method for electricity selling company under distribution and sale separated environment Download PDFInfo
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Abstract
The invention discloses a method for controlling the electricity purchasing quantity of an electricity selling company in a distribution and sale separated environment, which comprises the following steps: establishing a three-layer electricity purchasing and selling service architecture of a power grid company, an electricity selling company and a user according to an electricity selling company Agent and a user Agent; constructing a user electric quantity elastic model consisting of an uncontrollable load, a transferable load and a variable load based on a three-layer electricity purchasing and selling service architecture; according to the electric quantity and electricity price elastic model of the user and the operation and evaluation parameters of the electricity selling company, establishing an electricity purchasing control model of the electricity selling company, wherein the model comprises the following steps: and respectively considering market stability constraint and electricity purchasing and selling balance constraint by taking the maximum sum of the electricity selling income and the potential income of the electricity selling company as an objective function. The invention fully considers the uncertainty of the power selling company for purchasing power and future expectation, establishes a decision model of the power selling company for electric quantity, and adopts chaotic particle swarm algorithm for solving, thereby realizing the optimization of the power purchasing quantity.
Description
Technical Field
The invention relates to the field of electric power of an electric power system, in particular to a method for controlling the purchase electric power of an electric power selling company in a distribution and sale separated environment.
Background
Renewable new energy sources such as wind power, photovoltaic and the like are currently used for replacing traditional fossil energy sources, and the replacement of an electric automobile for a traditional automobile is a trend, so that the power generation and the power consumption load of the system have randomness and intermittence, and meanwhile, a power consumer responds to the price of electric energy through a power consumption management system or equipment to translate or reduce the load. The retail business of electric power is implemented under the uncertainty of "source" and "load", so that the operation of electric power selling companies is concerned.
A complete power market is generally divided into a medium-long term contract trading market, a futures trading market, a day-ahead trading market, a real-time trading (balance) market and an auxiliary service trading market. Under the background of reformation and separation of distribution and sale of the electric power market in China, in order to ensure balance of supply and demand and maintain stability of a system, an electric power market operation department determines a power generation plan in each trading period in the next day through a spot-stock trading market, and calculates spot-stock prices of a reference node and a power generation unit in each area, namely a wholesale electricity price of electricity purchased from an electricity selling company to the electric power grid. The electricity selling company needs to purchase electricity from the power grid in the form of long-term contracts, futures transactions and the like, and the decision of purchasing electricity by the electricity selling company has great influence on the safe operation of the power system and the benefit of the electricity selling company.
For the electricity selling company, the electricity purchasing curve of the next day needs to be submitted, and the method is generally adopted that every 30 times is divided into one transaction period, and the whole time is finished or the half hour is finished. In the case that the retail electricity price of the electricity selling company is not released, the electricity purchasing quantity decision of the electricity selling company is only determined by the difference value between the predicted load of the electricity selling user and the contract electricity quantity. However, in the full marketization stage, the electricity selling company has the independent decision right of the retail electricity price, and the electricity consumption of the electricity consumers in the service range provided by the electricity selling company is influenced by the retail electricity price, so that the electricity purchasing quantity in the market which is required to be traded by the electricity selling company at the day before is influenced. Due to the factors such as the current market electricity price and uncertainty of the next day user electricity consumption, the traditional electricity purchasing decision method of the electricity selling company faces certain risks. How to make an effective electricity purchasing decision strategy enables the electricity selling company to obtain the maximum benefit under the condition that the electricity price of the spot market and the electricity consumption of the next day user are uncertain, and the strategy becomes the content concerned by the electricity selling company.
Disclosure of Invention
The invention provides a power purchase amount control method for an electricity selling company in a distribution and sale separated environment, which comprises the steps of establishing a user demand elasticity model for protecting user privacy and an optimal power purchase amount decision model considering an autonomous decision-making electricity price factor by analyzing load elasticity, introducing a condition risk value (CVaR) into risk evaluation of the electricity selling company, and realizing optimization of the power purchase amount by the electricity selling company, wherein the details are described as follows:
a method for controlling the purchase power of an electric power selling company in a distribution and separation environment comprises the following steps:
establishing a three-layer electricity purchasing and selling service architecture of a power grid company, an electricity selling company and a user according to an electricity selling company Agent and a user Agent; constructing a user electric quantity elastic model consisting of an uncontrollable load, a transferable load and a variable load based on a three-layer electricity purchasing and selling service architecture;
according to the user electric quantity elastic model and the operation and evaluation parameters of the power selling company, establishing a power purchasing control model of the power selling company, wherein the model comprises the following steps: respectively considering market stability constraint and purchasing and selling balance constraint by taking the maximum sum of the selling income and the potential income of the power selling company as an objective function;
the user electric quantity elastic model specifically comprises the following steps:
in the formula: i is the total time period number,
the reference electricity price of the user k at each time is shown as
Daily power curve of time user, WkIs a matrix related to the electricity price elasticity of the user electricity quantity;
representing the load m initial power consumption vector; emThe electricity price elastic matrix is the load electricity quantity;
is the electricity price vector;
wherein the objective function is:
max B＝B′+B″
in the formula, B 'represents the electric power selling income of the electric power selling company, and B' represents the potential income of the electric power selling company;
wherein:
f(os)＝r1+r2os+r3os 2
where T is the decision period set, K is the set of all participating users, Ps,tSelling electricity for a time period t, is a decision-making variable, rhos,tFor established day-ahead hourly electricity prices, pb,tCost of electricity purchase of t, Pb,tPurchase electric power for electric power selling companies, B(0)Potential profit influence coefficient, U is the total specification number of users in electricity selling area, U0Indicating the number of users, P, supplying power to the electricity-selling company on the dayt,kFunction representing electricity sales, i.e. t-th term, r in the demand elastic relation of user k1、r2And r3Estimating coefficients for market share, osThe price is the average price of the electricity selling price;
the market stability constraints are specifically:
ρsmin≤ρs,t≤ρsmax
Pbmin≤Pb,t≤Pbmax
in the formula, ρsmin、ρsmaxRespectively the upper and lower limits of the day-ahead hourly electricity price, Pbmin、PbmaxRespectively, the upper and lower limits of the purchased electricity quantity, Pa,minRepresenting the minimum value of daily electricity sales;
the electricity purchasing and selling balance constraint is specifically as follows: pb,t＝Ps,t+Ploss,t＝(1+α)Ps,t
In the formula, Ploss,tAlpha is the line loss proportionality coefficient, which is the total electric quantity lost in the power transmission process.
And the power selling company Agent is used for maximizing a target system ordering electric quantity optimization strategy according to own benefits after receiving all user load information, and reporting the finally predicted purchasing electric quantity to a power grid company.
The user Agent is used for interacting with the intelligent power utilization equipment through a physical interface to realize intelligent control on the equipment; the user Agent is also used for predicting the power utilization curve and the demand elasticity parameter of the user the next day based on the historical power utilization habit information or the set information, aggregating the user load data and reporting the aggregated user load data to the Agent of the power selling company; the user Agent is also used for optimizing the self energy utilization plan based on the retail electricity price information issued by the electricity selling company and realizing the energy management of the user; user agents are applied to industrial, commercial and residential loads.
Wherein the control method further comprises:
the Agent of the electricity selling company transmits information to the user in a broadcasting mode; the user Agent uploading information contains load data, so encrypted transmission is needed.
The technical scheme provided by the invention has the beneficial effects that:
1. the uncertainty of the power selling company in purchasing power and in future expectation is fully considered, a decision model of the power selling company for electric quantity is established, and the chaos particle swarm algorithm is adopted for solving, so that the optimization of the power purchasing quantity is realized;
2. by optimizing the electricity purchasing quantity under the condition of independently deciding the electricity price factor, the linkage effect between the user and the power system is improved, the electricity utilization elasticity of the user side is favorably excavated, the social overall benefit can be improved, and the green development of energy sources is realized.
Drawings
FIG. 1 is a schematic diagram of a MAS-based electricity purchasing and selling service framework provided by the present invention;
FIG. 2 is a flow chart of a method for controlling the purchase power of an electric power selling company in a distribution and distribution separated environment according to the present invention;
FIG. 3 is a spot market forecasted electricity price for an embodiment;
FIG. 4 shows the difference σ in the embodimentPThe electricity selling benefit is obtained;
FIG. 5 shows the difference σ in the embodimentPThe risk value of the electricity selling condition;
FIG. 6 shows a modification B of the embodiment(0)The electricity selling company decides the electricity price;
FIG. 7 shows the difference σ in the embodimentPAnd σBThe electricity selling benefit.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in further detail below.
Example 1
A method for controlling the purchase power of an electric power selling company in a distribution and separation environment, referring to fig. 1 and 2, the method comprising the steps of:
101: establishing a three-layer electricity purchasing and selling service architecture of a power grid company, an electricity selling company and a user according to an electricity selling company Agent and a user Agent;
102: constructing a user electric quantity elastic model consisting of an uncontrollable load, a transferable load and a variable load based on a three-layer electricity purchasing and selling service architecture;
103: according to the electric quantity and electricity price elastic model of the user and the operation and evaluation parameters of the electricity selling company, establishing an electricity purchasing control model of the electricity selling company, wherein the model comprises the following steps: and respectively considering market stability constraint and electricity purchasing and selling balance constraint by taking the maximum sum of the electricity selling income and the potential income of the electricity selling company as an objective function.
And further, the power selling company Agent is used for maximizing a target system ordering electric quantity optimization strategy according to own benefits after receiving all user load information, and reporting the finally predicted purchasing electric quantity to the power grid company.
Furthermore, the user Agent is used for interacting with the intelligent power utilization equipment through a physical interface to realize intelligent control on the equipment;
the user Agent is also used for predicting the power utilization curve and the demand elasticity parameter of the user the next day based on the historical power utilization habit information or the set information, aggregating the user load data and reporting the aggregated user load data to the Agent of the power selling company;
and the user Agent is also used for optimizing the self energy utilization plan based on the retail electricity price information issued by the electricity selling company and realizing the energy management of the user. The user Agent can be applied to industrial, commercial and residential loads.
In specific implementation, the control method further includes:
the Agent of the electricity selling company transmits information to the user in a broadcasting mode; the user Agent uploading information contains load data, so encrypted transmission is needed.
In conclusion, the embodiment of the invention optimizes the electricity purchasing quantity under the condition of independently deciding the electricity price factor, increases the linkage effect between the user and the power system, is beneficial to excavating the electricity utilization elasticity of the user side, can improve the social overall benefit and realizes the green development of energy.
Example 2
The scheme of example 1 is further described below with reference to fig. 1, fig. 2, calculation formulas, and examples, and is described in detail below:
201: the electricity-selling company purchases electricity-selling service architecture and Agent setting;
the embodiment of the invention is based on a three-layer framework of 'power grid company-electricity selling company-user', comprises an electricity selling company Agent and a user Agent, and has the following specific functions:
the power selling company Agent comprises:
a) interacting with an information platform of a power grid company to acquire the current running state of the power grid and wholesale electricity price information of the power grid company;
b) the system is responsible for issuing retail electricity price information to a lower-layer Agent and receiving load information fed back by the lower-layer Agent;
c) after receiving all user load information, maximizing a target system ordering electric quantity optimization strategy according to own benefits, and reporting the finally predicted ordering electric quantity to a power grid company.
Secondly, the user Agent comprises:
a) the intelligent control system is interacted with intelligent electric equipment through a physical interface to realize intelligent control on the equipment;
b) predicting the power utilization curve and the demand elasticity parameter of the user the next day based on the historical power utilization habit information or the information set by the user, aggregating the user load data, and reporting to the Agent of the power selling company;
c) based on retail electricity price information issued by an electricity selling company, the energy utilization plan of the user is optimized, and the energy management of the user is realized. The user Agent can be applied to industrial, commercial and residential loads.
202: the electricity purchasing decision of an electricity selling company under the electricity purchasing and selling framework is made;
the user Agent can be applied to industrial, commercial and residential loads, and in order to simplify analysis, only residential users are taken as analysis objects, and other types of loads can adopt the same method.
The method for constructing the load electric quantity elastic model based on different load characteristics comprises the following steps: the user electric quantity elastic model of uncontrollable load, transferable load and variable load is characterized in that firstly, a load electric quantity elastic matrix of different loads is modeled to obtain the user electric quantity elastic model, the processes are all completed by a user Agent, and the detailed steps are as follows:
(1) uncontrollable load, including lighting facilities, television and other loads with strict requirements on demand and time, the electricity utilization characteristics are fixed, an electricity utilization curve can be obtained through load prediction, and the load electricity utilization elastic matrix is considered to be used for the load
All of the elastic coefficients of (a) and (b) are 0, i.e.:
in the formula, U is an uncontrollable load set, and m is a certain load in the set.
(2) Transferable load mainly refers to a kind of load that has strict demand requirement but not strict electricity consumption time, such as electric automobile, water heater, etc. The load of the type is characterized in that the total electricity consumption amount cannot be changed, only the distribution of the electricity consumption amount in time is influenced, and the electricity consumption amount is transferred according to the relative height of the retail electricity price in the electricity consumption period.
In order to simplify the load model, the total power consumption of the load is assumed to be evenly distributed in the working interval of the transferable load, the initial power consumption of each period is equal to a fixed value, and the electric quantity elastic matrix
Satisfies the following relationship:
wherein S is a transferable load set, eij,mCoefficient of ith row and jth column of electric quantity elastic matrix capable of transferring load mm、bmThe lower and upper time limits of the working interval of the load m.
(3) The variable load specifically includes: air conditioner and other loads with continuous power consumption characteristics and loads capable of being interrupted at any time according to the price of electricity, and the requirements on the requirements are not strict and can be met at any timeThe change of the retail electricity price at the electricity consumption time interval adjusts the electricity consumption in time to save the electricity fee. In the case of neglecting air cold storage, the transfer characteristic is not obvious, and the cross elastic coefficient of the load is assumed to be 0, namely, the electric quantity elastic matrix
Satisfies the following relationship:
in the formula, A is a variable load set.
(4) The demand elasticity relationship between the power consumption and the electricity rate of the load m can be expressed as:
in the formula, Pm＝[P1,m,P2,m,…,PI,m]TAs a vector of the amount of electricity used at each moment of the load m,
representing the vector of the initial electricity consumption of the load m, I is the total time period number,
is shown in
Each element being a diagonal matrix of diagonal elements, the price term ρs,mExpressed as:
in the formula, ρs,iIndicates the price of electricity sold at the moment I (I is 1,2, …, I),
is a constant term.
(5) Adding the demand elasticity relational expressions of all the loads to obtain a demand elasticity relational expression of a user k:
in the formula:
the reference electricity price of the user k at each time is shown as
Daily power curve of time user, WkIs a matrix related to the electricity price elasticity of the user electricity quantity.
Equation (6) is the electricity quantity and electricity price elastic model of user k. After the user Agent receives the control start signal, the user Agent will
WkAnd
and uploading the three parameters to an Agent of an electric power selling company.
Second, parameters needed to be obtained by power purchase control of power selling company
With the development of marketization, the types and service connotations of power selling companies are expanded. In an electric retail environment, the types of electricity selling companies can be roughly divided into three categories:
(1) the system is constructed by a power grid company or an incremental power distribution network operator invested in social capital and has the obligation of providing guaranteed-base power supply service and power transmission and distribution service;
(2) the power generation company or the distributed power source owner invests and constructs a company which can directly build electricity selling transaction with users;
(3) the power generation system is established by investment of social enterprises with great asset strength, and does not have power grid operation right and power generation capacity. The electricity selling companies are numerous and have strong competitive awareness, and are the most basic and common type of electricity selling markets.
In this case, the control of the electricity purchase amount by the electricity selling company needs to consider the acquired parameters including: the method comprises the following steps of expected electricity purchasing cost values, line loss proportionality coefficients, upper and lower limits of electricity selling prices, upper and lower limits of electricity purchasing quantity, minimum daily electricity selling value, potential income influence coefficients, the number of power supply users of electricity selling companies on the same day, the total specification modulus of users in electricity selling areas, market share estimation coefficients, risk assessment confidence coefficients, distribution standard deviations of electricity selling quantity, electricity purchasing cost and potential factor influence coefficients and risk avoidance factors.
Third, the electricity purchasing quantity control model of the electricity selling company
(1) The transaction between the grid company and the electricity selling company can be designed into various types, including: the electricity is purchased in the spot market in the form of Power Pool, or the electricity purchasing price is determined in the form of bilateral contract and the like. In order to simplify the analysis, the operation mode between the electricity selling company and the user is mainly researched, the electricity purchasing side is simplified, and the electricity purchasing cost of the electricity selling company in the next day time period t is considered to be rhob,tThe expected value may be obtained by a day-ahead prediction.
(2) According to the electric quantity and electricity price elastic model of the user and the operation and evaluation parameters of the electricity selling company, establishing an electricity purchasing control model of the electricity selling company, comprising the following steps: respectively considering market stability constraint and purchasing and selling balance constraint by taking the maximum sum of the selling income and the potential income of the power selling company as an objective function;
1) the electricity price capable of being autonomously decided is particularly the electricity price of the day hour;
2) the maximum objective function of the sum of the electric sales income and the potential income of the electric sales company can be expressed as maxB ═ B '+ B' (7)
In the formula, B 'represents the electric power selling income of the electric power selling company, and B' represents the potential income of the electric power selling company, which are respectively expressed as follows:
wherein:
f(os)＝r1+r2os+r3os 2 (12)
where T is a set of decision periods, T ═ 1,2, …, I]K is the set of all participating users, Ps,tSelling electricity for a time period t, is a decision-making variable, rhos,tFor established day-ahead hourly electricity prices, pb,tThe electricity purchase cost is t, Pb,tPurchase electric power for electric power selling companies, B(0)Potential profit influence coefficient, U is the total specification number of users in electricity selling area, U0Representing the number r of users of the electricity supply of the electricity selling company on the day1、r2And r3Estimating coefficients for market share, osFor equality of electricity selling price, Pt,kThe method can be expressed as a function of the electricity selling quantity, namely, the t-th item in the demand elastic relational expression of the user k is obtained by receiving load information uploaded by the user Agent through the Agent of the electricity selling company.
3) The above market stability constraints:
ρsmin≤ρs,t≤ρsmax (13)
Pbmin≤Pb,t≤Pbmax (14)
in the formula, ρsmin、ρsmaxRespectively the upper and lower limits of the day-ahead hourly electricity price, Pbmin、PbmaxRespectively, the upper and lower limits of the purchased electricity quantity, Pa,minIndicating the minimum daily electricity sales.
4) The above power purchase and sale balance constraint:
Pb,t＝Ps,t+Ploss,t＝(1+α)Ps,t (16)
in the formula, Ploss,tAlpha is the line loss proportionality coefficient, which is the total electric quantity lost in the power transmission process.
(3) Due to the fact that uncertainty of user power utilization in the next day and potential income prediction of a future market exists on the power selling side and uncertainty of spot market power price exists on the power purchasing side, random factors cause that the power selling company needs to carry out risk assessment on uncertainty factors in power purchasing quantity decision so as to reduce market risk to the maximum extent. The CVaR index overcomes the defects of variance and VaR evaluation risk, and is widely applied to the aspect of electric power market risk evaluation.
According to the electric power purchasing quantity certainty control model of the electric power selling company established in the step (2), considering the uncertainty of the next day user power utilization and the future market potential income prediction of the electric power selling side and the uncertainty of the spot market power price of the electric power purchasing side, establishing the electric power purchasing quantity risk control model of the electric power selling company considering the uncertainty on the basis, and comprising the following steps:
1) let certain asset earnings satisfy the normal distribution N (mu, sigma)2) On the premise of the model, the analytical calculation formula of the relative CVaR of the asset is shown as the following formula:
wherein, beta is the confidence coefficient,
is a probability density function of a standard normal distribution N (1, 0) (. phi.)-1(β) is the upper beta quantile of the standard normal distribution N (1, 0).
2) The probability distribution of the power consumption of the user the next day, the potential income of the future market and the spot market power price of the power grid company is normal distribution, namely:
in the formula (I), the compound is shown in the specification,
and
for the next day of electricity consumption, future market potential income and actual value of spot market electricity price, sigmaP、σρAnd σBThe three parameters are respectively the standard deviation of the power selling amount, the power purchasing cost and the probability distribution of the potential factor influence coefficient, and are mutually independent.
3) After introducing the power utilization error of the user and the power price error of the spot market, the actual profit of the power selling company can be expressed as follows:
in the formula (I), the compound is shown in the specification,
it can be obtained that the benefits of the electricity selling company meet:
B～N(E(B),σ2) (22)
the expressions of the expected income E (B) and the risk measure item CVaR (B) of the power selling company are as follows:
and establishing a power purchasing amount risk decision model of the power selling company based on the risk measurement model, wherein an objective function can be expressed as:
max W(B)＝E(B)-λ·CVaR(B) (25)
in the formula, λ is a risk avoiding factor, e (b) is expected income of an electricity selling company, and cvar (b) is a risk measure item.
The constraints of the optimization problem (25) are (6) and (13-16).
(4) And (4) risk control flow for purchasing electric quantity by the electric power selling company.
The participation of the electricity selling company in the day-ahead market comprises three steps:
a) forecasting the next day spot market electricity price and estimating the parameter fluctuation condition according to historical data;
b) the electricity selling company interacts with the user to control the optimal electricity purchasing amount;
c) the electricity selling company reports the end user load curve.
The information issued by the Agent of the electricity selling company is specific to all users, so that the information can be transmitted to the users in a broadcasting mode; the user Agent uploading information contains load data, so encrypted transmission is needed. In the electricity purchasing control process, the user Agent uploads the contents twice in total, and the load curve fed back for the second time can reduce the load difference caused by the deviation of the model, so that the curve reported by the electricity selling company is closer to the actual electricity consumption of the next day, and the loss of the electricity selling company caused by unbalanced electricity is reduced. If the final optimization curve is different from the curve calculated by the user elasticity information, the user Agent needs to update the user model data, but the electricity selling company Agent does not make price decision again, so as to reduce the network burden of the MAS system.
The electricity price decision flow of the electricity selling company is shown in fig. 2, wherein the solution of the formula (25) model is the key for applying the method. The model is a nonlinear programming problem, the decision variable is the hourly electricity price of 24 time intervals, and the model can be solved by adopting a chaotic particle swarm optimization algorithm.
In summary, the electric power purchasing risk control method for the power selling company based on the multi-agent system established in the embodiment of the present invention is based on solving the problem of making electric power purchasing of the power selling company, fully considers the uncertainty of electric power purchasing of the power selling company and future expectation, establishes the electric power purchasing risk control method for the power selling company, and provides the optimal electric power purchasing for the power selling company by solving with the chaotic particle swarm algorithm.
Example 3
The following examples are presented to demonstrate the feasibility of the embodiments of examples 1 and 2, and are described in detail below:
the predicted electricity prices for the grid company spot market are shown in fig. 3. The user parameters are set according to typical intelligent electricity users. The electricity selling company has 3 ten thousands of users in the same day, and the total regional user scale is 6 thousands of users. The load demand elasticity coefficient is calculated by the user Agent for a period of historical data. Setting power constraint upper and lower limits of an electricity selling company to be 20MW and 40MW at each moment, setting the initial electricity price after conversion to be 50$/MWh, setting the price upper and lower limits to be 40$/MWh and 120$/MWh, and setting the total electricity selling quantity to be not lower than 70% of the initial electricity quantity within one day; α is taken to be 0.05 and confidence β is taken to be 0.95.
And (3) carrying out model solution by adopting Matlab programming, carrying out chaotic initialization on 100 particles, and finally selecting 50 better initial particles for carrying out optimal solution. Factors influencing the electricity-selling company to control the electricity purchasing amount comprise user parameters and electricity-selling company parameters, and the pricing strategy is analyzed from the two aspects.
1) User factor impact.
Fig. 4 and 5 respectively show the relationship between the total benefit of the electricity selling company and the fluctuation variance of the CVaR along with the error percentage between the actual power consumption and the theoretical power consumption of the user. It can be seen that when λ is constant, with σPThe income expectation of the power selling companies is gradually reduced, and the CVaR is gradually increased. This is because when λ is constant, σPThe increase leads to the increase of the risk of purchasing electricity, the strategy of purchasing electricity by the electricity selling company tends to be conservative, the electricity selling amount is reduced, and the income is reduced. SigmaPThe increase makes the power selling company avoid the risk, and CVaR still is increasing trend wholly.
2) Electricity selling company factor influence.
The factors of the electricity selling company mainly research the prediction error of the electricity purchasing price, B(0)Influence of errors on electricity sales revenue and B(0)Size and sizeInfluence of electricity price. FIG. 6 shows study B(0)The influence on the electricity price which can be autonomously decided is divided into 5 scenes, and the influence along with B can be seen(0)The electricity prices are reduced more and more until the lowest electricity price limit is approached, because with the increase of the potential profit ratio, the electricity selling company sets the electricity prices to be more inclined to the low electricity prices to attract more users to join the electricity selling company. FIG. 7 shows that when B(0)The income of the electricity selling company is according to sigma when the fluctuation is smallρIncrease of (a) increases and then decreasesBMonotonically decreasing at 0); within the range shown, with σBIncrease of (3) the revenue of the electricity selling company is only according to sigmaρAnd increases with an increase. This is represented by σρAnd σBDue to the opposite effect on the quantity of electricity purchased, [ sigma ]ρThe increase in (a) may lead the electricity selling company to reduce the amount of electricity purchased by raising the price of electricity sold, sigmaBThe increase in electricity prices is the opposite of the electricity prices as the electricity selling companies make lower electricity selling prices. The optimal electricity selling price when the risk factors are not considered is recorded as rhoopThe two act simultaneously to make the electricity price appear to be close to rho firstopRear distance rhoopThe condition (2) causes the profit to increase first and then decrease. When sigma isBLarger, only the growth occurs, which is represented by σBThe specific gravity is so large that σ isρWhen the maximum value of the research range is reached, the price of electricity cannot be far away from rhoopAnd the result is that.
Those skilled in the art will appreciate that the drawings are only schematic illustrations of preferred embodiments, and the above-described embodiments of the present invention are merely provided for description and do not represent the merits of the embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (4)
1. A method for controlling the purchase power of an electric power selling company in a distribution and distribution separated environment is characterized by comprising the following steps:
establishing a three-layer electricity purchasing and selling service architecture of a power grid company, an electricity selling company and a user according to an electricity selling company Agent and a user Agent; constructing a user electric quantity elastic model consisting of an uncontrollable load, a transferable load and a variable load based on a three-layer electricity purchasing and selling service architecture;
according to the user electric quantity elastic model and the operation and evaluation parameters of the power selling company, establishing a power purchasing control model of the power selling company, wherein the model comprises the following steps: respectively considering market stability constraint and purchasing and selling balance constraint by taking the maximum sum of the selling income and the potential income of the power selling company as an objective function;
the user electric quantity elastic model specifically comprises the following steps:
in the formula: i is the total time period number,
the reference electricity price of the user k at each time is shown as
Daily power curve of time user, WkIs a matrix related to the electricity price elasticity of the user electricity quantity;
representing the load m initial power consumption vector; emThe electricity price elastic matrix is the load electricity quantity;
is the electricity price vector;
wherein the objective function is:
max B＝B′+B″
in the formula, B 'represents the electric power selling income of the electric power selling company, and B' represents the potential income of the electric power selling company;
wherein:
f(os)＝r1+r2os+r3os 2
where T is the decision period set, K is the set of all participating users, Ps,tSelling electricity for a time period t, is a decision-making variable, rhos,tFor established day-ahead hourly electricity prices, pb,tCost of electricity purchase of t, Pb,tPurchase electric power for electric power selling companies, B(0)Potential profit influence coefficient, U is the total specification number of users in electricity selling area, U0Indicating the number of users, P, supplying power to the electricity-selling company on the dayt,kFunction representing electricity sales, i.e. t-th term, r in the demand elastic relation of user k1、r2And r3Estimating coefficients for market share, osThe price is the average price of the electricity selling price;
the market stability constraints are specifically:
ρsmin≤ρs,t≤ρsmax
Pbmin≤Pb,t≤Pbmax
in the formula, ρsmin、ρsmaxRespectively the upper and lower limits of the day-ahead hourly electricity price, Pbmin、PbmaxRespectively, the upper and lower limits of the purchased electricity quantity, Pa,minRepresenting the minimum value of daily electricity sales;
the electricity purchasing and selling balance constraint is specifically as follows: pb,t＝Ps,t+Ploss,t＝(1+α)Ps,t
In the formula, Ploss,tAlpha is the line loss proportionality coefficient, which is the total electric quantity lost in the power transmission process.
2. The method as claimed in claim 1, wherein the power purchase amount control method for the power selling company under the separate distribution environment,
and the electricity selling company Agent is used for maximizing a target system ordering electric quantity optimization strategy according to own benefits after receiving all user load information, and reporting the finally predicted electric quantity to the power grid company.
3. The method as claimed in claim 1, wherein the power purchase amount control method for the power selling company under the separate distribution environment,
the user Agent is used for interacting with the intelligent power utilization equipment through a physical interface to realize intelligent control on the equipment;
the user Agent is also used for predicting the power utilization curve and the demand elasticity parameter of the user the next day based on the historical power utilization habit information or the set information, aggregating the user load data and reporting the aggregated user load data to the Agent of the power selling company;
the user Agent is also used for optimizing the self energy utilization plan based on the retail electricity price information issued by the electricity selling company and realizing the energy management of the user; user agents are applied to industrial, commercial and residential loads.
4. The method as claimed in claim 1, wherein the method further comprises:
the Agent of the electricity selling company transmits information to the user in a broadcasting mode; the user Agent uploading information contains load data, so encrypted transmission is needed.
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