An optimization operation method for distribution and sales power companies considering user-side interaction

Abstract

The present invention relates to the technical field of power sales market, and provides a method for optimizing the operation of a power distribution company considering user-side interaction, wherein the method comprises: S1, establishing a source-load model of a power distribution company (PTL) according to the operation structure of the power distribution company (PTL), and using demand response technology to uniformly dispatch flexible loads through a load agent (LA); S2, establishing a multi-stakeholder game behavior model based on the source-load model obtained in step S1 and the load agent participating in the dispatching process; S3, proposing a bargaining rejection function for the multi-stakeholder game behavior based on the Weber-Fechner law, and using a genetic algorithm to solve the Nash equilibrium point. Through the technical solution of the present invention, the enthusiasm of demand response is fully utilized, and the local consumption of renewable energy is basically realized, so that the interests of multiple stakeholders are the highest, and the power distribution dispatching result is also optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of power sales market, and in particular to an optimization operation method of a power distribution and sales company considering user-side interaction. Background Art

[0002] The power distribution and sales company (PTL) is mainly responsible for the construction and operation management of the incremental distribution network, providing power supply services to regional users, and plays a bridging role between the upper power grid and users. Domestic power distribution and sales companies have just begun to develop, and their development model is not yet mature. There is little research on the optimization of power distribution and sales companies. The current technical methods mainly focus on the business model and marketing strategy of power sales companies.

[0003] Some of the existing technologies have discussed the possible impact of the opening of power sales on the demand response side, and proposed relevant operational mechanism suggestions for the participation of domestic demand-side resources in the power market under the background of the new power reform from multiple perspectives. Some use "aggregation agents" to integrate the flexible load resources in the area under the jurisdiction of the agent in order to respond to the operation and dispatch needs of the distribution system, and advocate the construction of a three-layer active distribution system structure and operation system consisting of power sales companies-aggregation agents-flexible users. Some are based on the actual implementation of the current domestic new power reform, and draw on the operating experience and lessons of foreign power sales companies to try to build an operating mode of power sales companies that adapt to the development trend of my country's power industry, including customized personalized services for different users, providing network services, energy-saving measures and shared power services, etc. Finally, based on the surplus capacity of power sales companies, the author thinks and explores how to choose the most appropriate operating mode according to the type of power sales entity. In order to increase users' enthusiasm for responding to electricity market prices and incentives, some have created a power sales company power purchase and sales model that takes DR into consideration. The upper model aims to maximize the operating income of the power sales entity, while the lower model stands from the perspective of the end user, takes its own utility as the optimization goal, and quotes the bilateral contract breach penalty mechanism to establish a mathematical model for DR users. Some have also proposed an optimization scheduling model for power sales companies, whose goal is to maximize the day-ahead revenue, combining DG and controllable loads into the optimization operation of the power sales company, taking into account controllable reactive power, and using classic examples to verify and analyze the methods and models proposed in the article. In the existing technology, there is little involvement and discussion on the optimization operation of distribution and sales companies.

[0004] Faced with fierce market competition, distribution and sales companies are responsible for the construction and operation of distribution networks and for supplying electricity to end users. The development of DG and controllable loads has given distribution and sales companies more power supply options in addition to purchasing electricity from the power market. How to meet load demand and improve their own operating economy in a market competition environment is an urgent problem that distribution and sales companies need to solve. For distribution and sales companies that have the right to operate distribution networks, it is not realistic to conduct unified dispatching through distribution and sales companies in the face of the access of DG and a large number of controllable loads, and it is difficult to give full play to the enthusiasm of demand response. Summary of the invention

[0005] In view of this, the present invention aims to propose an optimization operation method for distribution and sales power companies taking into account user-side interaction, which can give full play to the enthusiasm of demand response and basically realize the local consumption of renewable energy, so that the interests of multiple stakeholders are maximized.

[0006] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0007] An optimized operation method of a distribution and sales power company considering user-side interaction comprises the following steps:

[0008] S1, establish the source-load model of the power distribution company (PTL) according to the operation structure of the power distribution company (PTL), and use the demand response technology to uniformly dispatch the flexible load through the load agent (LA);

[0009] S2, based on the source-load model obtained in step S1 and the load agent participating in the dispatching process, a multi-stakeholder game behavior model is established;

[0010] S3, a bargaining rejection function is proposed for the game behavior of multiple stakeholders based on the Weber-Fechner law, and a genetic algorithm is used to solve the Nash equilibrium point.

[0011] Furthermore, the source-load model in step S1 includes a renewable energy (RES) model and a demand response (DR) model.

[0012] Furthermore, the step S1 specifically includes the following sub-steps:

[0013] S11, based on the operating structure of distribution companies, it is assumed that renewable energy (RES) is owned by the distribution companies;

[0014] S12, establish a renewable energy (RES) model, use wind power generation and photovoltaic power generation to represent RES, and model them separately

[0015] (1) Wind turbine output power model

[0016] Wind power output changes with wind speed. Whether the unit is in power generation state and the size of wind power output depend on the wind speed. The mathematical expression is as follows:

[0017]

[0018] Where: P WT (t) is the fan output at time t; v t is the wind speed at time t; v ci 、v co 、v r are the cut-in, cut-out and rated wind speed of the fan respectively; A, B, C are the parameters of the fan power curve; P r is the rated power of the fan;

[0019] (2) Photovoltaic power generation system output power model

[0020] Assuming that the area of ​​the solar panel is S, and the light intensity that the solar panel can receive at a certain moment is I(t), then the output power P of the photovoltaic power generation system is PV (t) is:

[0021]

[0022] Where: η C is the conversion efficiency of the solar panel; K C is the threshold constant. When the incident light intensity is less than K C When the incident light intensity is greater than K, the output power has a quadratic relationship with I(t). C When , the output power is linearly related to I(t);

[0023] S13, establishing a demand response (DR) model, and obtaining the degree of enthusiasm of the demand response based on the relationship between the actual power consumption of the user and the marginal utility;

[0024] S14, based on the renewable energy (RES) model and the demand response (DR) model, the flexible loads are uniformly dispatched through the load agent (LA).

[0025] Furthermore, the demand response (DR) model adopts a rectangular utility function model.

[0026] Furthermore, the step S2 specifically includes the following sub-steps:

[0027] S21, assuming that the power distribution company and the load agent are participants in the game process, and the decisions they make are rational, and they play the game with the goal of maximizing their own benefits;

[0028] S22, determine the payoff function of the game participants

[0029] (1) Profit function of power distribution company (PTL)

[0030] PTL is the power supplier. Its strategy portfolio includes the main grid power exchange power and DR electricity price. The profit function of PTL is the net profit of the distribution and sales company, as shown in the following formula:

[0031] R PTL =I PTL -C PTL

[0032] I PTL It is the income of the power distribution company, which consists of two parts: the income from the power sales of PTL to the main grid and the income from the power sales to the incremental distribution network load. The calculation formula is as follows:

[0033]

[0034] Where: P t R , are the power consumption and electricity price of the uncontrollable load at time t respectively; NDR is the total number of load agents; are the load size and DR electricity price of the dth load agent at time t; P t gs , are the amount of electricity sold by PTL to the main grid and the electricity price at time t;

[0035] C PTL is the operating cost of the power distribution company, and the calculation formula is as follows:

[0036]

[0037] Where: P t gb , are the amount of electricity purchased by PTL from the main grid at time t and the electricity price;

[0038] (2) Load Agent (LA) Profit Function

[0039] The load agent can change the electricity consumption of flexible users through demand response technology to negotiate with the distribution and sales company. Its profit function is the difference between revenue and expenditure. Among them, the revenue of LA is measured by the utility function, and the expenditure is the electricity fee paid by the users under its jurisdiction. The specific calculation formula is as follows:

[0040]

[0041] Where: R LA (d) represents the net profit of the dth load agent; The d-th load agent at time t has a flexible user power consumption of The utility function value when ;

[0042] S23, determine the game constraints, the specific constraints are as follows:

[0043] (1) Active power balance constraints

[0044]

[0045] In the formula: NWT and NWT are the total number of wind turbines and photovoltaics respectively; is the actual output of the kth wind turbine at time t; is the actual output of the mth photovoltaic unit at time t;

[0046] (2) Main network switching power constraints

[0047]

[0048] Where: They represent the maximum downstream and upstream power of the distribution and sales power company to the upper grid respectively;

[0049] (3) Demand Response (DR) Price Constraints

[0050] The specific constraints of demand response (DR) electricity prices are as follows:

[0051]

[0052] Where: is the maximum DR electricity price defined by the policy. To improve DR enthusiasm, it is assumed that P t ZR is the total load capacity before demand response at time t; P t RES is the amount of electricity generated by renewable energy at time t;

[0053] S24, establish a multi-stakeholder game behavior model based on the game participants' profit functions and game constraints.

[0054] Furthermore, the multi-stakeholder game behavior model is a full-information dynamic game model of an incremental distribution network with the participation of multiple stakeholders.

[0055] Furthermore, in step S24, when P t ZR ≥P t RES RES can fully absorb the demand, and there is no need to increase load power consumption by using demand response, so there is no need to engage in game; When , for PTL, the surplus electricity of RES except for the supply of conventional load electricity can be completely absorbed by two ways: feeding it back to the main grid and increasing the electricity consumption of flexible load through DR measures. Therefore, the bargaining lower limit of DR electricity price should be set as the electricity price sold by PTL to the main grid, otherwise the game will fall into a stalemate; when When the remaining power of RES exceeds the upper limit of what PTL can supply to the main grid, if DR is not performed, it will cause wind and solar power abandonment. At this time, LA has certain advantages. The lower limit of DR electricity price is determined according to the benefits brought by PTL supplying the maximum upstream capacity to the main grid. That is, at this time

[0056] Furthermore, the step S3 specifically includes the following sub-steps:

[0057] S31, based on the Weber-Fechner law, proposes a bargaining rejection function

[0058] The bargaining rejection function is used to measure the probability of a decision-maker rejecting a strategy proposed by a bargaining participant. From the perspective of a decision-maker, when the reduction in his own income is less than the minimum perceptible difference (continuous difference threshold), this strategy can be accepted; when the reduction in income continues to increase, the probability of rejecting the strategy proposed by the bargaining participant increases, and eventually reaches saturation. The specific function expression is as follows:

[0059]

[0060] Where: P ij (I) is the probability that the strategy proposed by bargaining participant j is rejected by decision-making participant i; I M is the just noticeable difference,

[0061] S32, using genetic algorithm to solve the Nash equilibrium point of the game process.

[0062] Furthermore, the game process based on the genetic algorithm in step S32 is:

[0063] (1) Input system parameters and given wind power, photovoltaic, and load forecast curves. Set PTL as the bargaining participant and LA as the decision-making participant;

[0064] (2) Determine P t RES ≤P t ZR Is it true: if it is true, then the game ends directly without proceeding; if it is not true, then go to step S33;

[0065] (3)Judgment Is it true? If true, the bargaining range of DR electricity price is set as Go to step S34; if not established (i.e. ), the bargaining range of DR electricity price is set as Go to step S34;

[0066] (4) Initialize the population (DR electricity, reverse power), the number of iterations n = 1, and the bargaining participants propose the initial DR electricity price;

[0067] (5) Select the current bargaining participant's income as the fitness value and calculate the fitness;

[0068] (6) Calculate individual fitness and update the optimal solution;

[0069] (7) Update the population (selection, crossover, mutation), n = n + 1; return to S35 until n = Nmax is reached, output the optimal solution, determine the transmission power of the tie line, and the DR power;

[0070] (8) Calculate the rejection probability of the decision-making participants to the current operation strategy according to the bargaining rejection function;

[0071] (9) Determine through sampling whether the decision-making participant rejects the strategy proposed by the current bargaining participant. If so, the current bargaining participant re-proposes the DR electricity price, initializes the population, and returns to step S35;

[0072] (10) Compare the optimization result of this time with the previous one. When the profit deviation between the two is less than ε, output the optimal solution and end. When it is greater than ε, use the optimization result of this time as the initial feasible solution, replace the identities of the decision-making participants and the bargaining participants, and return to step S35. When the profit deviation of the strategies proposed by the two parties is less than ε, it is considered that the current strategy set is the optimal response of each participant to the strategies of other participants, that is, Nash equilibrium is achieved.

[0073] Compared with the prior art, the beneficial effects of the method for optimizing operation of a distribution and sales power company considering user-side interaction of the present invention are mainly manifested in:

[0074] 1) Dynamic game bargaining effectively improves the participation of load agents in the entire dispatching process, enhances the flexibility of dispatching, and thus further increases the consumption of renewable energy.

[0075] 2) Dynamic game can not only respond to the national call and reduce the phenomenon of abandoned wind and solar power, but also improve the economic efficiency of various stakeholders and enhance the overall social benefits.

[0076] 3) The dynamic game proposed in the present invention gives equal bargaining power to all participants, reduces the gap in benefits among all parties, and improves the fairness of the game to a certain extent.

[0077] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0079] Figure 1 A schematic diagram of the operation structure of a power distribution company according to an implementation of the method for optimizing the operation of a power distribution company taking into account user-side interaction of the present invention;

[0080] Figure 2 It is a rectangular utility function model diagram in the present invention;

[0081] Figure 3 It is a bargaining rejection function diagram in the present invention;

[0082] Figure 4 It is a game flow chart based on genetic algorithm of the present invention;

[0083] Figure 5 It is a scheduling result diagram in the example scenario 1 of the present invention;

[0084] Figure 6 It is a scheduling result diagram in the example scenario 2 of the present invention;

[0085] Figure 7 This is a diagram of the scheduling results in the example scenario 3 of the present invention. DETAILED DESCRIPTION

[0086] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0087] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0088] It should be noted that the terms "first", "second", "third", etc. in the specification and claims of the present invention are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0089] According to one aspect of the present invention, a method for optimizing operation of a distribution and sales power company considering user-side interaction is provided, such as Figures 1 to 4 As shown, the optimization operation method of the power distribution company considering the user-side interaction includes the following steps:

[0090] S1, establish the source-load model of the power distribution company (PTL) according to the operation structure of the power distribution company (PTL), and use the demand response technology to uniformly dispatch the flexible load through the load agent (LA);

[0091] S2, based on the source-load model obtained in step S1 and the load agent participating in the dispatching process, a multi-stakeholder game behavior model is established;

[0092] S3, a bargaining rejection function is proposed for the game behavior of multiple stakeholders based on the Weber-Fechner law, and a genetic algorithm is used to solve the Nash equilibrium point.

[0093] In the above, in the operating structure of the power distribution company (PTL), the power distribution company (PTL) is mainly responsible for the construction and operation management of the incremental distribution network, providing power supply services to regional users, and plays the role of a bridge between the upper power grid and users. The dispatchable resources of the power distribution company include renewable energy source (RES) and the exchange power with the large power grid. In the embodiment of the present invention, it is assumed that RES is owned by the power distribution company. The load agent (LA) can aggregate a large number of controllable flexible load resources and participate in market competition on behalf of these flexible loads. Uncontrollable loads (UL) with fixed electricity prices do not participate in the bargaining game.

[0094] In the embodiment of the present invention, demand response technology and renewable energy are incorporated into the optimized operation of the power distribution company, flexible loads are uniformly dispatched through load agents, and the requirements of the power distribution company and load agents for economic benefits are comprehensively considered. A dynamic game model of multiple stakeholders is established, and a bargaining rejection function is proposed based on the Weber-Fechner law to characterize the dynamic game behavior of each participant in the game process. A genetic algorithm is used to solve the problem and obtain the Nash equilibrium point. The optimized operation method of the power distribution company considering user-side interaction of the present invention conducts bargaining games between the power distribution company and the load agent through a dynamic game model, further improving the degree of participation of demand response technology in the entire scheduling process, and ultimately basically realizing the local consumption of renewable energy. The profit of the power distribution company, the profit of the load agent and the total social benefit are all the highest.

[0095] Furthermore, the source-load model in step S1 includes a renewable energy (RES) model and a demand response (DR) model.

[0096] Furthermore, the step S1 specifically includes the following sub-steps:

[0097] S11, based on the operating structure of distribution companies, it is assumed that renewable energy (RES) is owned by the distribution companies;

[0098] S12, establish a renewable energy (RES) model, use wind power generation and photovoltaic power generation to represent RES, and model them separately

[0099] (1) Wind turbine output power model

[0100] Wind power output changes with wind speed. Whether the unit is in power generation state and the size of wind power output depend on the wind speed. The mathematical expression is as follows:

[0101]

[0102] Where: P WT (t) is the fan output at time t; v t is the wind speed at time t; v ci 、v co 、v r are the cut-in, cut-out and rated wind speed of the fan respectively; A, B, C are the parameters of the fan power curve; P r is the rated power of the fan;

[0103] (2) Photovoltaic power generation system output power model

[0104] Assuming that the area of ​​the solar panel is S, and the light intensity that the solar panel can receive at a certain moment is I(t), then the output power P of the photovoltaic power generation system is PV (t) is:

[0105]

[0106] Where: η C is the conversion efficiency of the solar panel; K C is the threshold constant. When the incident light intensity is less than K C When the incident light intensity is greater than K, the output power has a quadratic relationship with I(t). C When , the output power is linearly related to I(t);

[0107] S13, establishing a demand response (DR) model, and obtaining the degree of enthusiasm of the demand response based on the relationship between the actual power consumption of the user and the marginal utility;

[0108] S14, based on the renewable energy (RES) model and the demand response (DR) model, the flexible loads are uniformly dispatched through the load agent (LA).

[0109] Furthermore, the demand response (DR) model adopts a rectangular utility function model.

[0110] In the above, the source-load model is a model that takes into account the flexible load of users and the access of renewable energy to the distribution and sales of electricity, in which the load has the ability to improve the distribution and sales company's ability to cope with power fluctuations, and compared with energy storage, demand response can effectively reduce investment and operating costs, and has good economic efficiency. Currently, the commonly used demand response models are mainly divided into the following three categories: electricity price elasticity matrix model, consumer psychology model, and utility function model. In order to more intuitively represent the economic benefits brought by user electricity consumption to load agents, in the preferred case of the present invention, a rectangular utility function model is used as the demand response model.

[0111] With the development of demand response and the further improvement of market mechanisms, for load agents and power distribution companies, in order to determine the best electricity consumption strategy for each participant in order to balance and optimize the interests of all parties, a multi-stakeholder game behavior model is established to analyze the behavior of each stakeholder.

[0112] Furthermore, the establishment of the multi-stakeholder game behavior model in step S2 specifically includes the following:

[0113] Sub-steps:

[0114] S21, assuming that the power distribution company and the load agent are participants in the game process, and the decisions they make are rational, and they play the game with the goal of maximizing their own benefits;

[0115] S22, determine the payoff function of the game participants

[0116] (1) Profit function of power distribution company (PTL)

[0117] PTL is the power supplier. Its strategy portfolio includes the main grid power exchange power and DR electricity price. The profit function of PTL is the net profit of the distribution and sales company, as shown in the following formula:

[0118] R PTL =I PTL -C PTL

[0119] I PTL It is the income of the power distribution company, which consists of two parts: the income from the power sales of PTL to the main grid and the income from the power sales to the incremental distribution network load. The calculation formula is as follows:

[0120]

[0121] Where: P t R , are the power consumption and electricity price of the uncontrollable load at time t respectively; NDR is the total number of load agents; are the load size and DR electricity price of the dth load agent at time t; P t gs , are the amount of electricity sold by PTL to the main grid and the electricity price at time t;

[0122] C PTL is the operating cost of the power distribution company, and the calculation formula is as follows:

[0123]

[0124] Where: P t gb , are the amount of electricity purchased by PTL from the main grid at time t and the electricity price;

[0125] (2) Load Agent (LA) Profit Function

[0126] The load agent can change the electricity consumption of flexible users through demand response technology to negotiate with the distribution and sales company. Its profit function is the difference between revenue and expenditure. Among them, the revenue of LA is measured by the utility function, and the expenditure is the electricity fee paid by the users under its jurisdiction. The specific calculation formula is as follows:

[0127]

[0128] Where: R LA (d) represents the net profit of the dth load agent; The d-th load agent at time t has a flexible user power consumption of The utility function value when ;

[0129] S23, determine the game constraints, the specific constraints are as follows:

[0130] (1) Active power balance constraints

[0131]

[0132] In the formula: NWT and NWT are the total number of wind turbines and photovoltaics respectively; is the actual output of the kth wind turbine at time t; is the actual output of the mth photovoltaic unit at time t.

[0133] (2) Main network switching power constraints

[0134]

[0135] Where: They respectively represent the maximum downstream and upstream power of the distribution and sales power company to the superior power grid.

[0136] (3) Demand Response (DR) Price Constraints

[0137] The specific constraints of demand response (DR) electricity prices are as follows:

[0138]

[0139] Where: is the maximum DR electricity price defined by the policy. To improve DR enthusiasm, it is assumed that P t ZR is the total load capacity before demand response at time t; P t RES is the amount of electricity generated by renewable energy at time t;

[0140] S24, establish a multi-stakeholder game behavior model based on the game participants' profit functions and game constraints.

[0141] Furthermore, in order to ensure that all participants in the game have access to all strategies and benefit information of all other participants, information disclosure and transparency can further guarantee fairness in the game process. The multi-stakeholder game behavior model is a full information dynamic game model of the incremental distribution network with the participation of multiple stakeholders.

[0142] Furthermore, in step S24, when P t ZR ≥P t RES RES can fully absorb the demand, and there is no need to increase load power consumption by using demand response, so there is no need to engage in game; When , for PTL, the surplus electricity of RES except for the supply of conventional load electricity can be completely absorbed by two ways: feeding it back to the main grid and increasing the electricity consumption of flexible load through DR measures. Therefore, the bargaining lower limit of DR electricity price should be set as the electricity price sold by PTL to the main grid, otherwise the game will fall into a stalemate; when When the remaining power of RES exceeds the upper limit of what PTL can supply to the main grid, if DR is not performed, it will cause wind and solar power abandonment. At this time, LA has certain advantages. The lower limit of DR electricity price is determined according to the benefits brought by PTL supplying the maximum upstream capacity to the main grid. That is, at this time

[0143] Furthermore, the step S3 specifically includes the following sub-steps:

[0144] S31, based on the Weber-Fechner law, proposes a bargaining rejection function

[0145] In the embodiment of the present invention, the game is played in the form of a complete information dynamic game. During the whole game, each participant has all the characteristics, strategies and benefits of all other participants. The two parties will take turns to propose their own operation plan strategies and report the demand response electricity price. For the convenience of description, it is stipulated here that the party that proposes a new demand response electricity price and operation strategy is the bargaining participant, and the party that decides whether to accept it is the decision-making participant.

[0146] Different from static games, in dynamic games, there is a sequence of gaming behaviors, and the party that acts later can observe the behavior of the first actor. For the first actor, they can take the lead in proposing a strategy that is beneficial to themselves, which is a first-mover advantage. For the party that acts later, they have more information and can make targeted choices in a timely manner, which has a little advantage of being a latecomer. Therefore, in the dynamic game process, it is not certain whether it is more advantageous to act first or later. In order to ensure that the game can proceed smoothly and improve the fairness of the game process, participants are generally required to make certain concessions on the premise of ensuring that their own interests are not seriously damaged. In order to visualize the concession behavior of the participants and enable each interest subject to balance their own and overall interests in the decision-making process, in the preferred case of the present invention, a bargaining rejection function is proposed:

[0147] The bargaining rejection function is based on the Weber-Fechner law and is used to measure the probability of a decision-maker rejecting a strategy proposed by a bargaining participant. From the perspective of a decision-maker, when the reduction in his or her own benefits is less than the least noticeable difference (continuous difference threshold), this strategy can be accepted; when the reduction in benefits continues to increase, the probability of rejecting the strategy proposed by the bargaining participant increases, and eventually reaches saturation. The specific function expression is as follows:

[0148]

[0149] Where: P ij (I) is the probability that the strategy proposed by bargaining participant j is rejected by decision-making participant i; I M is the just noticeable difference,

[0150] S32, for the bargaining game problem between the above-mentioned power distribution company and the load agent, a genetic algorithm is used to solve the Nash equilibrium point of the game process.

[0151] Furthermore, the game process based on the genetic algorithm in step S32 is:

[0152] (1) Input system parameters and given wind power, photovoltaic, and load forecast curves. Set PTL as the bargaining participant and LA as the decision-making participant;

[0153] (2) Determine P t RES ≤P t ZR Is it true: if it is true, then the game ends directly without proceeding; if it is not true, then go to step S33;

[0154] (3)Judgment Is it true? If true, the bargaining range of DR electricity price is set as Go to step S34; if not established (i.e. ), the bargaining range of DR electricity price is set as Go to step S34;

[0155] (4) Initialize the population (DR electricity, reverse power), the number of iterations n = 1, and the bargaining participants propose the initial DR electricity price;

[0156] (5) Select the current bargaining participant's income as the fitness value and calculate the fitness;

[0157] (6) Calculate individual fitness and update the optimal solution;

[0158] (7) Update the population (selection, crossover, mutation), n = n + 1; return to S35 until n = Nmax is reached, output the optimal solution, determine the transmission power of the tie line, and the DR power;

[0159] (8) Calculate the rejection probability of the decision-making participants to the current operation strategy according to the bargaining rejection function;

[0160] (9) Determine through sampling whether the decision-making participant rejects the strategy proposed by the current bargaining participant. If so, the current bargaining participant re-proposes the DR electricity price, initializes the population, and returns to step S35;

[0161] (10) Compare the optimization result of this time with the previous one. When the profit deviation between the two is less than ε, output the optimal solution and end. When it is greater than ε, use the optimization result of this time as the initial feasible solution, replace the identities of the decision-making participants and the bargaining participants, and return to step S35. When the profit deviation of the strategies proposed by the two parties is less than ε, it is considered that the current strategy set is the optimal response of each participant to the strategies of other participants, that is, Nash equilibrium is achieved.

[0162] In order to more conveniently understand the optimized operation method of the distribution and sales power company considering the user-side interaction of the present invention, the following is combined with examples, and the scenario is discussed and analyzed to verify the effectiveness of this method. All simulations are completed by MATLAB programming based on a desktop computer with a 3.2GHz InterCore i5 processor and 8G memory. Among them, the DR utility function parameters are shown in Table 1, and the other system parameters are shown in Table 2.

[0163] Table 1 Demand response utility function parameters

[0164]

[0165] Table 2 Simulation example parameters

[0166]

[0167]

[0168] In order to characterize the differences in electricity consumption willingness and benefits during peak and valley periods, the basic marginal benefits of the utility function during peak and valley periods are multiplied by coefficients of 1.3 and 0.7 respectively. The specific division of peak and valley periods is shown in Table 3.

[0169] Table 3 Load power consumption time division

[0170]

[0171] The embodiment of the present invention introduces three operation scenarios for comparison: Scenario 1, the power distribution company sells electricity at a fixed price for all loads, and does not perform demand response load scheduling, nor does it have a dynamic game process; Scenario 2, introduces demand response technology (using a rectangular utility function), the power distribution company sets a demand response price, and the load agent responds according to the demand response price, but does not have bargaining power; Scenario 3, the power distribution company and the load agent use the dynamic game model proposed in the present invention to conduct a bargaining game. Among them, there is no game process in scenarios 1 and 2, and dynamic game must be conducted in scenario 3. The economic benefits of the various stakeholders in the three scenarios are shown in Table 4.

[0172] Table 4 Comparison of economic benefits in different scenarios

[0173]

[0174] From the perspective of economic benefits, scenario 1 has the lowest profits among all scenarios, whether it is the profit of the distribution power company, the profit of the load agent or the total social profit. This is because the electricity price for all power loads in scenario 1 is unified and fixed, lacks the support of demand response technology, cannot effectively absorb the excessive wind power output at night, and there is a serious phenomenon of wind abandonment; scenario 2 takes demand response into consideration on the basis of scenario 1, and the RES absorption capacity is significantly improved, and the profits of all parties are improved to a certain extent. However, in this scenario, the load agent can only respond according to the demand response electricity price and does not have bargaining power. There is a lack of game bargaining process. The demand response electricity price unilaterally formulated by the distribution power company may not achieve a good demand response effect, which ultimately leads to the fact that the profits of both the distribution power company and the load agent are not as good as scenario 3; in scenario 3, the dynamic game model of the present invention is used between the distribution power company and the load agent to conduct bargaining games, which further improves the degree of participation of demand response technology in the entire scheduling process, and finally basically realizes the local absorption of renewable energy, and the profit of the distribution power company, the profit of the load agent and the total social benefit are the highest.

[0175] From the perspective of system peak-to-valley difference and renewable energy abandonment, scenario 1 has the largest peak-to-valley difference of 3.09MW due to the lack of consideration of demand response, and the amount of renewable energy abandoned reaches 11.36% of the available power generation, with the least ideal overall scheduling effect; scenario 2 takes demand response into consideration, and the load peak-to-valley difference is reduced to 2.53MW, which is better than scenario 1, but the lack of bargaining power of load agents leads to limited enthusiasm for demand response, and the amount of renewable energy abandoned is 4.65% of the available power generation; scenario 3 further considers the dynamic game between distribution and sales companies and load agents, which increases the enthusiasm of load agents to participate in scheduling, and the amount of renewable energy abandoned is only 2.19% of the available power generation. The peak-to-valley difference is the same as scenario 2, which is 2.53MW. Scenario 3 has basically achieved local consumption of renewable energy, effectively reducing the adverse impact of renewable energy uncertainty on the main grid, and the scheduling result is the best among all scenarios.

[0176] The technical solution of the present invention gives full play to the initiative of demand response and basically realizes the local consumption of renewable energy, so that the interests of multiple stakeholders are maximized and the power distribution and dispatching results are optimized.

[0177] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing the operation of a distribution and sales power company considering user-side interaction, characterized in that: The following steps are involved: S1, establish the source-load model of the power distribution company PTL according to its operation structure, and use demand response technology to uniformly dispatch flexible loads through the load agent LA; S2, based on the source-load model obtained in step S1 and the load agent participating in the dispatching process, a multi-stakeholder game behavior model is established; S3, proposes a bargaining rejection function based on the Weber-Fechner law for the game behavior of multiple stakeholders, and uses a genetic algorithm to solve the Nash equilibrium point; The step S2 specifically includes the following sub-steps: S21, assuming that the power distribution company and the load agent are participants in the game process, and the decisions they make are rational, and they play the game with the goal of maximizing their own benefits; S22, determine the payoff function of the game participants (1) PTL profit function of power distribution companies PTL is the power supplier. Its strategy portfolio includes the main grid power exchange power and DR electricity price. The profit function of PTL is the net profit of the distribution and sales company, as shown in the following formula: R PTL =I PTL -C PTL I PTL It is the income of the power distribution company, which consists of two parts: the income from the power sales of PTL to the main grid and the income from the power sales to the incremental distribution network load. The calculation formula is as follows: Where: are the power consumption and electricity price of the uncontrollable load at time t respectively; NDR is the total number of load agents; are the load size and DR electricity price of the dth load agent at time t respectively; are the amount of electricity sold by PTL to the main grid and the electricity price at time t; C PTL is the operating cost of the power distribution company, and the calculation formula is as follows: Where: are the amount of electricity purchased by PTL from the main grid at time t and the electricity price; (2) Load agent LA profit function The load agent can change the electricity consumption of flexible users through demand response technology to negotiate with the distribution and sales company. Its profit function is the difference between revenue and expenditure. Among them, the revenue of LA is measured by the utility function, and the expenditure is the electricity fee paid by the users under its jurisdiction. The specific calculation formula is as follows: Where: R LA (d) represents the net profit of the dth load agent; The d-th load agent at time t has a flexible user power consumption of The utility function value when ; S23, determine the game constraints, the specific constraints are as follows: (1) Active power balance constraints In the formula: NWT and NWT are the total number of wind turbines and photovoltaics respectively; is the actual output of the kth wind turbine at time t; is the actual output of the mth photovoltaic unit at time t; (2) Main network switching power constraints Where: They represent the maximum downstream and upstream power of the distribution and sales power company to the upper grid respectively; (3) Demand response DR price constraints The specific constraints of demand response DR electricity prices are as follows: Where: is the maximum DR electricity price defined by the policy. To improve DR enthusiasm, it is assumed that is the total load capacity before demand response at time t; is the amount of electricity generated by renewable energy at time t; S24, establish a multi-stakeholder game behavior model based on the game participants’ payoff functions and game constraints; In step S24, when RES can fully absorb the demand, and there is no need to increase load power consumption by using demand response, so there is no need to engage in game; When , for PTL, the surplus electricity of RES except for the supply of conventional load electricity can be completely absorbed by two ways: feeding it back to the main grid and increasing the electricity consumption of flexible load through DR measures. Therefore, the bargaining lower limit of DR electricity price should be set as the electricity price sold by PTL to the main grid, otherwise the game will fall into a stalemate; when When the remaining power of RES exceeds the upper limit of what PTL can supply to the main grid, if DR is not performed, it will cause wind and solar power abandonment. At this time, LA has certain advantages. The lower limit of DR electricity price is determined according to the benefits brought by PTL supplying the maximum upstream capacity to the main grid. That is, at this time The source-load model in step S1 includes a renewable energy RES model and a demand response DR model; The step S1 specifically includes the following sub-steps: S11, based on the operating structure of the distribution company, it is assumed that the renewable energy RES is owned by the distribution company; S12, establish a renewable energy RES model, take wind power generation and photovoltaic power generation as RES, and model them respectively (1) Wind turbine output power model Wind power output changes with wind speed. Whether the unit is in power generation state and the size of wind power output depend on the wind speed. The mathematical expression is as follows: Where: P WT (t) is the fan output at time t; v t is the wind speed at time t; v ci 、v co 、v r are the cut-in, cut-out and rated wind speed of the fan respectively; A, B, C are the parameters of the fan power curve; P r is the rated power of the fan; (2) Photovoltaic power generation system output power model Assuming that the area of ​​the solar panel is S, and the light intensity that the solar panel can receive at a certain moment is I(t), then the output power P of the photovoltaic power generation system is PV (t) is: Where: η C is the conversion efficiency of the solar panel; K C is the threshold constant. When the incident light intensity is less than K C When the incident light intensity is greater than K, the output power has a quadratic relationship with I(t). C When , the output power is linearly related to I(t); S13, establishing a demand response DR model, and obtaining the degree of enthusiasm of demand response based on the relationship between the actual power consumption of users and marginal utility; S14, based on the renewable energy RES model and the demand response DR model, the flexible load is uniformly dispatched through the load agent LA; The step S3 specifically includes the following sub-steps: S31, based on the Weber-Fechner law, proposes a bargaining rejection function The bargaining rejection function is used to measure the probability of a decision-maker rejecting a strategy proposed by a bargaining participant. From the perspective of a decision-maker, when the reduction in its own benefits is less than the least noticeable difference, that is, less than the continuous difference threshold, this strategy can be accepted; when the reduction in benefits continues to increase, the probability of rejecting the strategy proposed by the bargaining participant increases accordingly, and eventually reaches saturation. The specific function expression is as follows: Where: Pij(I) is the probability that the strategy proposed by bargaining participant j is rejected by decision participant i; I M is the just noticeable difference, S32, using genetic algorithm to solve the Nash equilibrium point of the game process; The game process based on the genetic algorithm in step S32 is: (1) Input system parameters, given wind power, photovoltaic, and load forecast curves, set PTL as the bargaining participant and LA as the decision-making participant; (2)Judgment Is it true: if it is true, then the game ends directly without proceeding; if it is not true, then go to step S33; (3)Judgment Is it true? If so, the bargaining range of DR electricity price is set as Go to step S34; if not, that is, When the bargaining range of DR electricity price is set to Go to step S34; (4) Initialize the population, including DR electricity and reverse power, the number of iterations n = 1, and the bargaining participants propose the initial DR electricity price; (5) Select the current bargaining participant's income as the fitness value and calculate the fitness; (6) Calculate individual fitness and update the optimal solution; (7) updating the population, including selection, crossover, and mutation operations, n = n + 1; returning to S35 until n = Nmax is reached, outputting the optimal solution, and determining the transmission power of the tie line and the DR power; (8) Calculate the rejection probability of the decision-making participants to the current operation strategy according to the bargaining rejection function; (9) Determine through sampling whether the decision-making participant rejects the strategy proposed by the current bargaining participant. If so, the current bargaining participant re-proposes the DR electricity price, initializes the population, and returns to step S35; (10) Compare the optimization result of this time with the previous time. When the profit deviation between the two is less than ε, output the optimal solution and end. When it is greater than ε, use the optimization result of this time as the initial feasible solution, replace the identities of the decision-making participants and the bargaining participants, and return to step S35. When the profit deviation of the strategies proposed by the two parties is less than ε, it is considered that the current strategy set is the optimal response of each participant to the strategies of other participants, that is, Nash equilibrium is achieved.

2. The method for optimizing operation of a distribution and sales power company considering user-side interaction according to claim 1, characterized in that: The demand response DR model adopts a rectangular utility function model.

3. The method for optimizing operation of a distribution and sales power company considering user-side interaction according to claim 1, characterized in that: The multi-stakeholder game behavior model is a full-information dynamic game model of an incremental distribution network with the participation of multiple stakeholders.

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