CN110728410A - Load aggregator economic scheduling method considering demand response flexibility and uncertainty - Google Patents

Load aggregator economic scheduling method considering demand response flexibility and uncertainty Download PDF

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CN110728410A
CN110728410A CN201910985269.2A CN201910985269A CN110728410A CN 110728410 A CN110728410 A CN 110728410A CN 201910985269 A CN201910985269 A CN 201910985269A CN 110728410 A CN110728410 A CN 110728410A
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scheduling
power
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power grid
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李春燕
谢开贵
胡博
牛涛
张谦
赵晨宇
蔡文悦
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Chongqing University
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Abstract

The invention relates to a load aggregator economic dispatching method considering demand response flexibility and uncertainty, and belongs to the field of demand response dispatching of power systems. The method adopts multi-period scheduling, and utilizes new energy to stimulate the electricity price and the flexibility of the method to schedule and arrange, so that the benefit of the method is maximized; the method specifically comprises the following three parts: and (3) a power grid dispatching layer: dispatching capacity and tasks are issued through an information acquisition system to obtain electric power; and (3) an aggregator decision layer: the aggregator realizes the interaction between the power grid and users by formulating a scheduling protocol, distributes and reports schedulable capacity meeting the requirements of the power grid to a power grid scheduling layer, and issues a scheduling plan to a load response layer; a load response layer: according to an agreement signed with the aggregator, by combining own habits and requirements, the intelligent control terminal collects power utilization information and receives a scheduling signal, and the scheduling task of the aggregator is executed. The invention maximizes the benefits of the aggregator and enhances the flexibility of the system.

Description

Load aggregator economic scheduling method considering demand response flexibility and uncertainty
Technical Field
The invention belongs to the field of demand response scheduling of power systems, and relates to a non-cooperative game multi-period economic scheduling method for a medium and small user load aggregator, which considers demand response flexibility.
Background
Demand Response (DR) refers to a process in which a power user responds to a price or an excitation signal, changes normal power utilization habits and modes, and finally realizes power utilization optimization and comprehensive optimization configuration of system resources. Because the new energy power generation generally has the inverse peak regulation characteristic, the power generation amount is more in the load valley period, and the wind abandoning and light abandoning amount is also obviously increased along with the increase of the power generation amount of the renewable energy.
Due to the high proportional penetration of renewable energy, increasing the flexibility of the system becomes a major problem facing the power grid. Demand response is increasingly being used for the consumption of renewable energy sources due to its low cost and fast response. Demand response is introduced into a high-proportion renewable energy system, and the capacity of the system for peak-load regulation and frequency modulation and new energy consumption can be effectively improved.
However, the scheduling difficulty of the power grid is increased due to the massive dispersed loads, and a new scheduling mode needs to be researched. Load Aggregators (LA), aggregators for short, as entities for integrating the demand response potential of users, obtain rewards or reduce energy cost through sales Load flexibility to promote users to enter the retail power market, so that the users can participate in the smart grid.
In the smart grid environment, the grid, the load aggregators and the users are three independent entities. The grid is responsible for providing the power demand required by the customer, and a reasonably designed electricity price prompts the load aggregator to participate in the demand response. The load aggregator helps the user to optimize energy use through price response or contract reward, namely, energy conservation is realized, operation cost is saved, and the requirement of the user on the service quality of energy is met. The user then receives revenue from the load aggregator by changing the electricity usage behavior. This is a multi-objective optimization problem in which the goal of the grid is to minimize the operating costs of providing peak and valley clipping rewards to the aggregator. The goal of the aggregator is to maximize profit or minimize scheduling deviations, providing compensation for associated end users to change their power usage patterns. The goal of the user is to maximize the trade-off between revenue obtained from the aggregator and the discomfort of changing the electricity usage behavior.
Aiming at the research of the participation of aggregators in demand response scheduling, the market mechanism and the scheduling strategy are mainly designed, and the aggregators have the largest profit and the smallest cost or have the smallest scheduling deviation by playing games with the market or designing an optimization target. However, there is less literature standing on the consumption of new energy, designing mechanisms that facilitate the consumption of new energy, and less consideration of interactions between different aggregators.
Disclosure of Invention
In view of the above, the present invention provides a non-cooperative game-based aggregator multi-period optimization scheduling method, which allows medium and small users to participate in scheduling, and schedules a plurality of medium and small users as a whole load aggregator, so that the load aggregator meets performance requirements and capacity thresholds, and ensures safe operation and new energy consumption of a system, thereby maximizing the utilization of system demand response resources.
In order to achieve the purpose, the invention provides the following technical scheme:
a load aggregator economic dispatching method considering demand response flexibility and uncertainty utilizes new energy to stimulate electricity price and self flexibility to carry out dispatching arrangement, so that self income is maximized; the method specifically comprises the following three parts:
1) and (3) a power grid dispatching layer: the main body of the power grid dispatching layer is a system operator, and dispatching capacity and tasks are issued through the information acquisition system; the way for the power grid to obtain power is as follows: generating power by traditional units and distributed new energy in the region or transmitting the power through a power grid connected with the traditional units and the distributed new energy;
2) and (3) an aggregator decision layer: the aggregator realizes the interaction between the power grid and users by formulating a scheduling protocol, distributes and reports schedulable capacity meeting the requirements of the power grid to a power grid scheduling layer, and issues a scheduling plan to a load response layer;
3) a load response layer: according to an agreement signed with the aggregator, by combining own habits and requirements, the intelligent control terminal collects power utilization information and receives a scheduling signal, and the scheduling task of the aggregator is executed.
Furthermore, the scheduling method adopts multi-period scheduling, including day-ahead scheduling and real-time scheduling;
the day-ahead scheduling adopts day-ahead clearing, a power generation plan and an electricity price signal containing new energy are issued by a power grid, an aggregator is used as a receiver of the consumption amount and the electricity price of the new energy, a scheduling plan is arranged through a contract signed with a user, the new energy is consumed, meanwhile, rewards from the power grid are obtained, and a load scheduling command is issued to a load response layer;
the real-time scheduling is finished every hour, and an aggregator arranges a real-time scheduling plan by considering default conditions of users on the basis of receiving real-time power generation conditions and real-time electricity prices of the power grid, realizes power balance by purchasing and selling electricity to the power grid, and charges the electricity to the users to obtain benefits.
Further, in the day-ahead scheduling, an aggregator reports a schedulable capacity plan to a power grid according to the flexibility of the aggregator; the income of the day-ahead scheduling of the aggregator comes from the profit of selling the flexibility of the load to the power grid after the penalty incapable of accepting new energy is deducted; that is, the aggregator revenue function consists of two parts: the profit obtained by consuming the new energy and the punishment caused by not consuming the new energy in the last scheduling period are calculated according to the following formula:
Figure BDA0002236499800000021
wherein p isi_incentiveFor new energy incentive price, p, within scheduling period TpenaltyIn order to penalize the price,
Figure BDA0002236499800000024
for the demand response flexibility of the aggregator n,
Figure BDA0002236499800000022
default electric quantity generated for the last scheduling period of the aggregator;
the incentive price calculation formula for consuming new energy is as follows:
Figure BDA0002236499800000023
wherein alpha is a correction factor larger than 0, beta is a constant term and is the highest electricity price acceptable by the power grid, and the method is set to be twice of the highest real-time electricity price; n represents the number of aggregators;
the update strategy of the correction factor is as follows:
Figure BDA0002236499800000031
wherein q ispvThe new energy is photovoltaic output.
Further, the power consumption reported by the aggregator must not exceed the contract amount reported by the user, and there are the following constraints:
Pil_t≤Pilr_t
Pddl_t≤Pddlr_t
Pil_t,Pddl_t≥0
wherein, Pilr_tAnd Pddlr_tInterruptible load and translatable load power consumption, P, reported separately for the useril_tAnd Pddl_tRespectively, interruptible load and translatable load reported to the power grid by the aggregator.
Furthermore, the aggregator-oriented day-ahead economic dispatch is a non-cooperative game problem, namely a complete information static limited game problem, wherein all game-participating offices simultaneously select own action strategies; the method specifically comprises the following steps:
(1) aggregator day-ahead capacity reporting game model
Definition G ═ S1,...,SN；U1,...,UNThe method is a non-cooperative game model for the economic dispatch of the day ahead of the aggregator, and consists of participants N, a strategy S and a payment function U, wherein:
① participants, N aggregators in the region participating in economic dispatch day before are regarded as a set of participants, wherein the aggregators i e r ∈ Γ ═ {1, 2.., N };
② strategy, each aggregator reports the electricity consumption of controllable load according to its own demand, namely, load interruption and load translation,
Figure BDA0002236499800000033
③ Payment function:
Figure BDA0002236499800000032
wherein q is-n＝{q1,...,qn-1,qn+1,...,qNRepresenting the set of reported electric quantity of all aggregators except the aggregation quotient n in a scheduling time interval;
for each aggregator, it is desirable to maximize its revenue. Therefore, the aggregators participate in the game by changing the reported power consumption in each time interval, and finally obtain the self optimal power consumption reporting strategy;
(2) nash equilibrium of aggregator day-ahead capacity reporting game
If each participant i selects strategy SiWhen any participant cannot obtain more profits by changing the strategy under the condition that other participants keep the strategy, the nash equilibrium of the game is called to be achieved, and the current strategy set { S }1*,...,SN*The Nsh equilibrium solution is obtained;
according to Nash equilibrium existence theorem 2, the game model of the invention is proved to have Nash equilibrium, and the equivalent transformation is carried out on the payment function as follows:
Figure BDA0002236499800000041
to q isnAnd (3) calculating a second partial derivative to obtain:
Figure BDA0002236499800000042
therefore, un(qn,q-n) Is qnThe non-cooperative game has nash equilibrium.
Best reporting plans for all aggregators q1*,q2*,...,qN*The Nash equilibrium solution of the current capacity reporting game is obtained; at this time, when any one of the aggregators in the participators adopts the optimal strategy, the other aggregators n cannot increase the profit by changing the existing strategy and omitting to establish a new strategy, namely:
wherein q is-n*＝{q1*,...,qn-1*,qn+1*,...,qN*Representing the optimal strategy combination of other aggregators in the game except the aggregator n;
the optimal planned power consumption reported by the aggregator in the day-ahead scheduling is transmitted to the real-time scheduling, the aggregator readjusts the power utilization plan by considering the user default and the uncertainty of the new energy output, and the maximization of the real-time scheduling profit is realized.
Further, in the real-time scheduling, the user executes the power utilization plan according to a contract agreed in advance, the power utilization control right is handed to the aggregator, and the aggregator collects the power fee to the user according to the real-time power price and the power utilization amount to obtain income; however, there is uncertainty in real-time scheduling due to both grid and customer aspects. Because the new energy power generation has uncertainty, the planned power generation amount released by the power grid in the day ahead is not always equal to the actual new energy output. The method comprises the steps that uncertainty of new energy output is taken into account by a power grid, real-time new energy output is issued to an aggregator, and the aggregator needs to adjust a power utilization plan according to new energy output prediction issued during real-time scheduling of the power grid; on the user side, a breach may be caused for two reasons:
① the lack of agreement is subjective for reasons such as comfort or electricity cost savings.
② since real-time scheduling is a discrete state control and the load has certain physical properties, it is an objective cause of the breach.
In summary, the planned power consumption of the user is not always equal to the actual power consumption, and for the power consumption of the user beyond the plan, the aggregator needs to purchase additional power from the power grid to meet the user demand, so it is necessary to consider the impact of the user default on the real-time scheduling.
Further, the considering of the influence of the user default on the real-time scheduling specifically includes: when the user default electric quantity xi follows the truncation normal distribution, xi is in xil,ξu]The probability density function of (d) is shown as:
wherein,
Figure BDA0002236499800000052
phi represents the probability density function of standard normal distribution and its cumulative distribution function, xi, muξ、σξRespectively representing independent variable, mean value and standard deviation of the density function;
the default power expectations of users are:
Figure BDA0002236499800000053
setting an upper limit xi of the cutoff distributionuQ' and a lower limit ξ l0, the default electric quantity Q of the user is 0, Q']Within the range; by changing the mean and variance of the truncated distributions, the differential impact of different user default distributions on scheduling can be studied. Since this is not the main subject of the present invention, the problem is reduced to μξ＝0，σξK.q', value range [0,1]. Thereby obtaining simplified violationsThe electricity quantity is expected to be:
Figure BDA0002236499800000054
wherein k is a coefficient representing the difference of different users;
according to the default expectation of the user, the default rate of the aggregator is as follows:
Figure BDA0002236499800000055
on the basis of considering the default of the user, the aggregator schedules with the maximum income target; the revenue objective function for an aggregator is shown below:
wherein,
Figure BDA0002236499800000057
is a decision variable, which is the real-time electricity consumption, p, of the users belonging to the aggregator i at the moment tr_tIn order to achieve a real-time electricity rate,
Figure BDA0002236499800000058
the method is characterized in that the aggregator needs to purchase electric quantity from a power grid due to default of users belonging to the aggregator i at the moment t, and q is providedp_t＞0，pp_tThe price of electricity is bought and sold according to the power grid protocol; the objective function shows that the income of the real-time scheduling of the aggregator comes from income obtained by paying the electric charge by the user, and the rest part of the income after the expense of purchasing the electricity from the power grid is deducted.
Further, the following constraints need to be considered in real-time scheduling: the method comprises the following steps of user power consumption upper and lower limit constraint, supply and demand balance constraint, node power flow equation constraint, node voltage constraint, node phase angle constraint and family load modeling constraint.
The invention has the beneficial effects that: the invention constructs a load aggregator economic dispatching model considering demand response flexibility and uncertainty. Based on the modeling of the flexibility load, a multi-period scheduling strategy for day-ahead and real-time scheduling is designed. In the day-ahead scheduling, new energy is utilized to stimulate the electricity price to consume new energy, and the aggregator considers the flexibility of the aggregator and plays games with other aggregators, so that the income maximization of the aggregator is realized; in real-time scheduling, a user default amount caused by meeting comfort level or saving electric charge is simulated by truncation distribution, and an aggregator scheduling strategy considering user default is established, so that the maximum real-time scheduling benefit is realized. Finally, medium and small-sized users can participate in demand response when entering the market, and the flexibility of the system is further enhanced.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a diagram of an aggregator deployment framework with distributed new energy resources;
FIG. 2 is a flowchart of aggregator-oriented multi-slot economic dispatch;
FIG. 3 is a topology of aggregation provider subordinate nodes and a power grid;
FIG. 4 is a data diagram of system power generation and utilization;
FIG. 5 is a comparison of electricity prices;
FIG. 6 is a simulation diagram of the original electricity consumption situation of each aggregator;
fig. 7 is a simulation diagram of power consumption after scheduling by each aggregator.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
The invention defines the demand response flexibility of the user side as: "the ability of the user to change the normal electricity consumption mode according to the user's own situation in response to the price of the power grid or the excitation signal". The flexibility of demand response is complementary to the flexibility of the power system, the flexibility of a user side is accurately measured, the frequency modulation and peak shaving capacity and the new energy consumption capacity of the system can be effectively improved, and the power utilization optimization and the comprehensive optimization configuration of system resources are facilitated to be finally realized.
The main factors influencing the user demand response capability include power utilization level, load variation, power utilization tendency, power utilization stability, other factors, and the like, and the factors can be characterized by extracting corresponding characteristic indexes of a load curve, as shown in table 1.
TABLE 1 Classification of demand response feature sets
The demand response flexibility of the user, which can be obtained based on the demand response characteristic index, is as follows:
Figure BDA0002236499800000072
wherein DPcResponse flexibility for aggregator demand, FiAnd τiThe ith characteristic index and the corresponding weight are respectively.
The demand response flexibility of the aggregator is:
Figure BDA0002236499800000073
wherein DPcaIn order for the aggregator to demand flexibility of response,ncifor the ith class of user number, DPciDemand response flexibility for class i users, nLAIs the total number of users under the aggregator. Compared with price type demand response, the incentive type demand response establishes a contract in advance, and the user response will and timeliness are strong, so that the scheduling strategy considered by the invention is based on the incentive type demand response.
Referring to fig. 1 to 7, a load aggregator is a main research object, and scheduling is performed by using new energy incentive electricity prices and self flexibility, so that self income is maximized. The smart grid dispatching framework containing the distributed new energy is shown in fig. 1 and comprises the following parts:
① the main body of the power grid dispatching layer is the system operator who sends dispatching capacity and task through the information collecting system, the power grid obtains the power by the traditional machine set and the distributed new energy power generation in the area or the power transmission through the interconnected power grid.
② and a aggregator decision layer, wherein the aggregator realizes interaction between the power grid and users by making a scheduling protocol, distributes and reports schedulable capacity meeting the power grid requirement to the power grid scheduling layer, and issues a scheduling plan to the load response layer.
③ load response layer, which is to collect power consumption information and receive dispatching signal via the intelligent control terminal to execute the dispatching task of the aggregator according to the agreement with the aggregator and combining the habit and demand.
As shown in fig. 2, the multi-period scheduling of the present embodiment includes two processes of day-ahead scheduling and real-time scheduling. The day-ahead scheduling adopts day-ahead clearing, a power generation plan and an electricity price signal containing new energy are issued by a power grid, an aggregator is used as a receiver of the consumption amount and the electricity price of the new energy, a scheduling plan is arranged through a contract signed with a user, the new energy is consumed, meanwhile, rewards from the power grid are obtained, and a load scheduling command is issued to a load response layer; the real-time scheduling is finished every hour, and the aggregator arranges a real-time scheduling plan by considering default conditions of users on the basis of receiving real-time power generation conditions and real-time electricity prices of the power grid, realizes power balance by purchasing and selling electricity from the power grid, and charges the electricity for the users to obtain benefits.
The user responds to the scheduling instructions by controlling the controllable load. The controllable load is used as a standby resource of the system, and system power fluctuation caused by new energy grid connection is balanced through demand response. Different residential users have different numbers, types and usage characteristics of household appliances, and it is difficult for an aggregator to analyze the usage of each household appliance one by one. Therefore, the aggregator needs to classify different electrical appliances according to the electricity usage habits of residents, so as to facilitate user characteristic analysis and dispatching instruction transmission.
In the day-ahead scheduling, an aggregator reports a schedulable capacity plan to a power grid according to the flexibility of the aggregator. The income of the day-ahead scheduling of the aggregator comes from the profit of the flexibility of selling the load to the power grid after deducting the penalty which can not accept the new energy, as shown in the formula (3). The aggregator revenue function consists of two parts: the gains obtained by consuming the new energy and the penalty due to not consuming the new energy for the last scheduling period are:
Figure BDA0002236499800000081
wherein p isi_incentiveFor new energy incentive price, p, within scheduling period TpenaltyIn order to penalize the price,
Figure BDA0002236499800000082
for the demand response flexibility of the aggregator n,
Figure BDA0002236499800000083
the default amount of power generated for the last scheduling period of the aggregator. The excitation electrovalence of the new energy is shown as the formula (4):
Figure BDA0002236499800000085
wherein alpha is a correction factor larger than 0, beta is a constant term and is the most acceptable to the power gridHigh electricity prices, the present embodiment is set to twice the highest real-time electricity prices. Equation (5) is the update strategy of the correction factor, qpvFor the new energy output, the new energy of this embodiment is the photovoltaic output.
Meanwhile, the electricity consumption reported by the aggregator must not exceed the contract quantity reported by the user, so there are the following constraints:
Pil_t≤Pilr_t(6)
Pddl_t≤Pddlr_t(7)
Pil_t,Pddl_t≥0 (8)
wherein, Pilr_tAnd Pddlr_tInterruptible load and translatable load power consumption, P, reported separately for the useril_tAnd Pddl_tRespectively, interruptible load and translatable load reported to the power grid by the aggregator.
The aggregator-oriented day-ahead economic dispatch is a non-cooperative gaming problem, i.e., a complete information static limited gaming problem in which all game participants simultaneously select their own action strategies. The specific description is as follows:
(1) aggregator day-ahead capacity reporting game model
Definition G ═ S1,...,SN；U1,...,UNThe method is a non-cooperative game model for the economic dispatch of the day ahead of the aggregator, and consists of participants N, a strategy S and a payment function U, wherein:
① participants, N aggregators within a region participating in economic dispatch day ahead may be viewed as a set of participants, where the aggregator i e Γ ═ 1, 2.
② strategy, each aggregator reports the electricity consumption of controllable load according to its own demand, namely, load interruption and load translation,
Figure BDA0002236499800000092
③ Payment function the payment function is expressed as follows:
Figure BDA0002236499800000091
wherein q is-n＝{q1,...,qn-1,qn+1,...,qNAnd the aggregate of the reported electric quantity in the scheduling time interval of all the aggregators except the aggregation provider n is represented.
For each aggregator, it is desirable to maximize its revenue. Therefore, the aggregators participate in the game by changing the reported power consumption in each time interval, and finally obtain the self optimal power consumption reporting strategy.
(2) Nash equilibrium of aggregator day-ahead capacity reporting game
If each participant i selects strategy SiWhen any participant cannot obtain more profits by changing the strategy under the condition that other participants keep the strategy, the nash equilibrium of the game is called to be achieved, and the current strategy set { S }1*,...,SN*Is the Nash equilibrium solution.
According to Nash equilibrium existence theorem 2, the game model of the invention is proved to have Nash equilibrium, and the equivalent transformation is carried out on the payment function as follows:
Figure BDA0002236499800000101
to q isnAnd (3) calculating a second partial derivative to obtain:
Figure BDA0002236499800000102
therefore, un(qn,q-n) Is qnThe non-cooperative game has nash equilibrium.
Best reporting plans for all aggregators q1*,q2*,...,qN*And the result is the Nash equilibrium solution of the current capacity reporting game. At this time, when any one of the aggregators in the participators adopts the optimal strategy, the other aggregators n cannot increase the profit by changing the existing strategy and omitting to establish a new strategy, namely:
Figure BDA0002236499800000104
wherein q is-n*＝{q1*,...,qn-1*,qn+1*,...,qN*And expressing the optimal strategy combination of other aggregators in the game except the aggregator n.
The optimal planned power consumption reported by the aggregator in the day-ahead scheduling is transmitted to the real-time scheduling, the aggregator readjusts the power utilization plan by considering the user default and the uncertainty of the new energy output, and the maximization of the real-time scheduling profit is realized.
In the real-time scheduling, the user executes the power utilization plan according to a contract agreed in advance, the power utilization control right is handed to the aggregator, and the aggregator collects the power fee to the user according to the real-time power price and the power consumption to obtain income. However, there is uncertainty in real-time scheduling due to both grid and customer aspects. Because the new energy power generation has uncertainty, the planned power generation amount released by the power grid in the day ahead is not always equal to the actual new energy output. The power grid considers uncertainty of new energy output, real-time new energy output is issued to the aggregator, and the aggregator needs to adjust the power utilization plan according to the new energy output prediction issued during real-time scheduling of the power grid. On the user side, a breach may be caused for two reasons:
① the lack of agreement is subjective for reasons such as comfort or electricity cost savings.
② since real-time scheduling is a discrete state control and the load has certain physical properties, it is an objective cause of the breach.
In summary, the planned power consumption of the user is not always equal to the actual power consumption, and for the power consumption of the user beyond the plan, the aggregator needs to purchase additional power from the power grid to meet the user demand, so it is necessary to consider the impact of the user default on the real-time scheduling. When the user default electric quantity xi follows the truncation normal distribution, xi is in xil,ξu]The probability density function of (d) is shown as:
Figure BDA0002236499800000103
wherein phi and phi respectively represent the probability density function of standard normal distribution and the cumulative distribution function thereof, xi and muξ、σξThe independent variable, mean and standard deviation of the density function are indicated, respectively.
The default power expectations of the available users are as follows:
setting an upper limit xi of the cutoff distributionuQ' and a lower limit ξ l0, the default electric quantity Q of the user is 0, Q']Within the range. By changing the mean and variance of the truncated distributions, the differential impact of different user default distributions on scheduling can be studied. Since this is not the main subject of the present invention, the problem is reduced to μξ＝0，σξK is a coefficient representing the difference between different users, and is in a value range of [0,1]. The default power expectations thus simplified are:
Figure BDA0002236499800000112
according to the user default expectation, the default rate of the aggregators can be:
Figure BDA0002236499800000113
on the basis of considering the user default, the aggregator schedules with the maximum profit target. The aggregator's revenue is shown below:
Figure BDA0002236499800000114
wherein,
Figure BDA0002236499800000115
is a decision variable, which is the real-time electricity consumption, p, of the users belonging to the aggregator i at the moment tr_tIn order to achieve a real-time electricity rate,
Figure BDA0002236499800000116
the method is characterized in that the aggregator needs to purchase electric quantity from a power grid due to default of users belonging to the aggregator i at the moment t, and q is providedp_t＞0，pp_tThe price of electricity is bought and sold according to the power grid protocol; the objective function shows that the income of the real-time scheduling of the aggregator comes from income obtained by paying the electric charge by the user, and the rest part of the income after the expense of purchasing the electricity from the power grid is deducted.
The following constraints need to be considered in real-time scheduling: the method comprises the following steps of user power consumption upper and lower limit constraint, supply and demand balance constraint, node power flow equation constraint, node voltage constraint, node phase angle constraint and family load modeling constraint.
By using the real-time example model, on the basis of the IEEE33 node power distribution system shown in fig. 3, four aggregators with different flexibilities are added for scheduling, and the aggregators flexibility is shown in table 2. The initial electricity usage of each aggregator is shown in figure 6.
TABLE 2 flexibility of aggregators
Aggregators 1 2 3 4
Flexibility 0.55 0.52 0.41 0.48
Distributed photovoltaics are connected to the nodes 3 and 5, and the predicted output, real-time output and the total electricity utilization condition of the aggregator before the photovoltaic day are shown in fig. 4. The results show that based on the scheduling model, each aggregator competes for the consumption of new energy to maximize its own benefits. As can be seen from fig. 5, since different aggregators actively participate in new energy consumption according to their flexibility, the game model encourages higher electricity prices and higher aggregators earnings. Fig. 7 shows the power consumption of each aggregator after scheduling, and comparing with fig. 6, each aggregator significantly increases the power consumption in the time period when the new energy output accounts for a high ratio, and the new energy consumption capability and stability of the system are increased.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.

Claims (8)

1. A load aggregator economic dispatching method considering demand response flexibility and uncertainty is characterized in that the method utilizes new energy to stimulate electricity price and self flexibility to carry out dispatching arrangement, and the self income is maximized; the method specifically comprises the following three parts:
1) and (3) a power grid dispatching layer: the main body of the power grid dispatching layer is a system operator, and dispatching capacity and tasks are issued through the information acquisition system; the way for the power grid to obtain power is as follows: generating power by traditional units and distributed new energy in the region or transmitting the power through a power grid connected with the traditional units and the distributed new energy;
2) and (3) an aggregator decision layer: the aggregator realizes the interaction between the power grid and users by formulating a scheduling protocol, distributes and reports schedulable capacity meeting the requirements of the power grid to a power grid scheduling layer, and issues a scheduling plan to a load response layer;
3) a load response layer: according to an agreement signed with the aggregator, by combining own habits and requirements, the intelligent control terminal collects power utilization information and receives a scheduling signal, and the scheduling task of the aggregator is executed.
2. The economic dispatching method of the load aggregator considering flexibility and uncertainty of demand response according to claim 1, wherein said dispatching method adopts multi-period dispatching, including day-ahead dispatching and real-time dispatching;
1) the day-ahead scheduling adopts day-ahead clearing, a power generation plan and an electricity price signal containing new energy are issued by a power grid, an aggregator is used as a receiver of the consumption amount and the electricity price of the new energy, a scheduling plan is arranged through a contract signed with a user, the new energy is consumed, meanwhile, rewards from the power grid are obtained, and a load scheduling command is issued to a load response layer;
2) the real-time scheduling is finished every hour, and the aggregator arranges a real-time scheduling plan by considering default conditions of users on the basis of receiving real-time power generation conditions and real-time electricity prices of the power grid, realizes power balance by purchasing and selling electricity from the power grid, and charges the electricity for the users to obtain benefits.
3. The load aggregator economic dispatching method considering demand response flexibility and uncertainty as claimed in claim 2, wherein in a day-ahead dispatch, an aggregator reports a dispatchable capacity plan to a power grid according to its own flexibility; the income of the day-ahead scheduling of the aggregator comes from the profit of selling the flexibility of the load to the power grid after the penalty incapable of accepting new energy is deducted; that is, the aggregator revenue function consists of two parts: the profit obtained by consuming the new energy and the punishment caused by not consuming the new energy in the last scheduling period are calculated according to the following formula:
Figure FDA0002236499790000011
wherein p isi_incentiveFor new energy incentive price, p, within scheduling period TpenaltyIn order to penalize the price,
Figure FDA0002236499790000012
for the demand response flexibility of the aggregator n,
Figure FDA0002236499790000013
default electric quantity generated for the last scheduling period of the aggregator;
the incentive price calculation formula for consuming new energy is as follows:
wherein alpha is a correction factor larger than 0, beta is a constant term and is the highest power price acceptable by the power grid; n represents the number of aggregators;
the update strategy of the correction factor is as follows:
Figure FDA0002236499790000021
wherein q ispvThe new energy is used.
4. The load aggregator economic dispatch method of claim 3, wherein the aggregate reported electricity consumption does not exceed the user reported contract amount, with the following constraints:
Pil_t≤Pilr_t
Pddl_t≤Pddlr_t
Pil_t,Pddl_t≥0
wherein, Pilr_tAnd Pddlr_tInterruptible load and translatable load power consumption, P, reported separately for the useril_tAnd Pddl_tRespectively, interruptible load and translatable load reported to the power grid by the aggregator.
5. The economic dispatch method of a load aggregator with flexibility and uncertainty in demand response considered as claimed in claim 3, wherein the aggregator-oriented economic dispatch is a non-cooperative game problem, i.e. a complete information static limited game problem, wherein all game players simultaneously select their own action strategy; the method specifically comprises the following steps:
(1) aggregator day-ahead capacity reporting game model
Definition G ═ S1,...,SN；U1,...,UNThe method is a non-cooperative game model for the economic dispatch of the day ahead of the aggregator, and consists of participants N, a strategy S and a payment function U, wherein:
① participants, N aggregators in the region participating in economic dispatch day before are regarded as a set of participants, wherein the aggregators i e r ∈ Γ ═ {1, 2.., N };
② strategy, each aggregator reports the electricity consumption of controllable load according to its own demand, namely, load interruption and load translation,
③ Payment function:
Figure FDA0002236499790000023
wherein q is-n＝{q1,...,qn-1,qn+1,...,qNRepresenting the set of reported electric quantity of all aggregators except the aggregation quotient n in a scheduling time interval;
the aggregator participates in the game by changing the reported power consumption in each time interval, and finally obtains the self optimal power consumption reporting strategy;
(2) nash equilibrium of aggregator day-ahead capacity reporting game
Best reporting plans for all aggregators q1*,q2*,...,qN*The Nash equilibrium solution of the current capacity reporting game is obtained; at this time, when any one of the aggregators in the participators adopts the optimal strategy, the other aggregators n cannot increase the profit by changing the existing strategy and omitting to establish a new strategy, namely:
Figure FDA0002236499790000031
wherein q is-n*＝{q1*,...,qn-1*,qn+1*,...,qN*Representing the optimal strategy combination of other aggregators in the game except the aggregator n;
the optimal planned power consumption reported by the aggregator in the day-ahead scheduling is transmitted to the real-time scheduling, the aggregator readjusts the power utilization plan by considering the user default and the uncertainty of the new energy output, and the maximization of the real-time scheduling profit is realized.
6. The economic dispatch method of a load aggregator with flexibility and uncertainty in demand response considered in claim 2, wherein in real-time dispatch, the user will execute a power utilization plan according to a contract agreed in advance, and the power utilization control right is handed over to the aggregator, which charges the user with electricity according to the real-time electricity price and the power utilization amount to obtain income; the method comprises the steps that uncertainty of new energy output is taken into account by a power grid, real-time new energy output is issued to an aggregator, and the aggregator needs to adjust a power utilization plan according to new energy output prediction issued during real-time scheduling of the power grid;
for users with power consumption beyond plan, the aggregator needs to purchase additional power from the grid to meet the user demand, taking into account the impact of the user default on real-time scheduling.
7. The load aggregator economic dispatching method considering demand response flexibility and uncertainty as claimed in claim 6, wherein said considering the impact of user default on real-time dispatching is specifically: when the user default electric quantity xi follows the truncation normal distribution, xi is in xil,ξu]The probability density function of (d) is shown as:
Figure FDA0002236499790000032
wherein,
Figure FDA0002236499790000033
phi represents the probability density function of standard normal distribution and its cumulative distribution function, xi, muξ、σξRespectively representing independent variable, mean value and standard deviation of the density function;
the default power expectations of users are:
wherein k is a coefficient representing the difference of different users;
according to the default expectation of the user, the default rate of the aggregator is as follows:
Figure FDA0002236499790000041
on the basis of considering the default of the user, the aggregator schedules with the maximum income target; the revenue objective function for an aggregator is shown below:
Figure FDA0002236499790000042
wherein,
Figure FDA0002236499790000043
is a decision variable, which is the real-time electricity consumption, p, of the users belonging to the aggregator i at the moment tr_tIn order to achieve a real-time electricity rate,
Figure FDA0002236499790000044
the method is characterized in that the aggregator needs to purchase electric quantity from a power grid due to default of users belonging to the aggregator i at the moment t, and q is providedp_t＞0，pp_tThe price of electricity is bought and sold according to the power grid protocol; the objective function shows that the income of the real-time scheduling of the aggregator comes from income obtained by paying the electric charge by the user, and the rest part of the income after the expense of purchasing the electricity from the power grid is deducted.
8. The method of claim 7, wherein the real-time scheduling considers the following constraints: the method comprises the following steps of user power consumption upper and lower limit constraint, supply and demand balance constraint, node power flow equation constraint, node voltage constraint, node phase angle constraint and family load modeling constraint.
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CN116565882B (en) * 2023-06-29 2023-09-19 北京大学 Distributed demand response method, device, system and medium
CN116565882A (en) * 2023-06-29 2023-08-08 北京大学 Distributed demand response method, device, system and medium
CN117117861B (en) * 2023-10-24 2024-01-26 北京交通大学 Optimal scheduling system for virtual energy storage of electric automobile to participate in various auxiliary services of power grid
CN117117861A (en) * 2023-10-24 2023-11-24 北京交通大学 Optimal scheduling system for virtual energy storage of electric automobile to participate in various auxiliary services of power grid
CN117829450A (en) * 2023-11-23 2024-04-05 国网天津静海供电有限公司 Collaborative planning method for power distribution network-data center and model solving method thereof
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