Virtual power plant optimal scheduling method for power grid peak regulation service

Abstract

The invention discloses a virtual power plant optimal scheduling method for power grid peak regulation service. The method comprises the following steps: step 1, constructing a virtual power plant peak regulation response model; step 2, considering the peak regulation response model of the virtual power plant obtained in the step 1, taking the maximum profit of the virtual power plant as an objective function, and setting constraint conditions of the objective function, thereby constructing a virtual power plant optimal scheduling model oriented to the power grid peak regulation service; 3, virtual power plant peak regulation punishment rewards are introduced; and step 4, solving the virtual power plant optimal scheduling model which introduces the virtual power plant peak regulation penalty reward and faces the power grid peak regulation service, and realizing virtual power plant optimal scheduling according to a solving result. According to the invention, through reasonable benefit distribution, fairness and high efficiency of resource utilization are ensured, and active participation of users and operators is stimulated.

Description

Technical Field

[0001] The present invention relates to a virtual power plant optimization dispatching method, in particular to a virtual power plant optimization dispatching method for power grid peak load regulation services. Background Art

[0002] In modern power systems, with the large-scale access of renewable energy and the gradual opening of the power market, the demand for grid peak-shaving services is growing. Peak-shaving services refer to services that balance supply and demand by adjusting power generation or load during peak electricity demand periods to maintain stable grid operation. Traditional peak-shaving methods mainly rely on the start-stop or regulation of large power plants, but with the increase in distributed energy resources (such as solar energy, wind energy, energy storage systems, etc.), virtual power plants, as an emerging energy management model, provide new solutions for grid peak-shaving.

[0003] Virtual power plants integrate dispersed distributed energy resources, energy storage equipment, demand response resources, etc. through advanced communication technology and intelligent control systems to form a dispatchable energy supply network, thereby improving energy utilization efficiency and enhancing the flexibility and reliability of the power grid. In the operation of virtual power plants, the response parameters of users participating in peak load regulation are crucial. These parameters reflect the changes in users' electricity consumption behavior under different price incentives and are the basis for building peak load response models. Traditional linear models are often difficult to accurately describe this complex relationship, and quadratic functions are widely used in the construction of such models because they can better fit nonlinear relationships.

[0004] The benefit distribution mechanism of virtual power plants needs to consider the fairness and efficiency of resource utilization. The imperfection of the benefit distribution mechanism often leads to some potential problems, such as unfair resource utilization, market distortion and imbalance of power balance. The introduction of a penalty reward mechanism has become the key to solving these problems. However, the design and implementation of the penalty mechanism needs to fully consider the interests of all parties and ensure fairness and rationality. Only on the basis of combining the penalty mechanism with the reward mechanism and matching the system with technology, can virtual power plants achieve their maximum benefits and bring more sustainable development opportunities to the power market. Summary of the invention

[0005] The present invention provides a virtual power plant optimization scheduling method for power grid peak load regulation service to solve the problem that the virtual power plant scheduling method in the prior art uses a linear model to hardly describe the response parameters of users participating in peak load regulation and has no reasonable penalty reward mechanism.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A virtual power plant optimization scheduling method for power grid peak load regulation service comprises the following steps:

[0008] Step 1: Establish the relationship between price incentive and user load reduction, and the relationship between comfort cost and user load reduction, thereby obtaining the relationship between user load reduction and price incentive; and establish a virtual power plant peak load response model consisting of the relationship between user load reduction and price incentive;

[0009] Step 2: Considering the virtual power plant peak load response model obtained in step 1, taking the maximum profit of the virtual power plant as the objective function, and setting constraints of the objective function, thereby constructing a virtual power plant optimization scheduling model for power grid peak load service;

[0010] Step 3: Introduce the virtual power plant peak load regulation penalty reward into the virtual power plant optimization dispatching model for power grid peak load regulation service established in step 2;

[0011] Step 4: Solve the virtual power plant optimization scheduling model for grid peak-shaving services that introduces virtual power plant peak-shaving penalty rewards in step 3, and implement virtual power plant optimization scheduling based on the solution results.

[0012] In a further step 1, a quadratic function is used to represent the relationship between the comfort cost and the user load reduction.

[0013] In a further step 1, a quadratic function is used to represent the relationship between the amount of user load reduction and the change in price incentives.

[0014] In the further step 2, the objective function is constructed based on the peak-shaving income of the virtual power plant, the cost of new energy, the cost of mobilizing the demand-side load to participate in the peak-shaving obtained according to the virtual power plant peak-shaving response model, the energy storage cost of the virtual power plant, and the carbon trading cost.

[0015] In the further step 2, the set constraints include power balance constraints, network security constraints, photovoltaic power generation operation constraints, wind power operation constraints, user constraints and energy storage constraints.

[0016] In the further step 3, the introduced virtual power plant peak-shaving penalty rewards include the deviation penalty between the wind turbine and photovoltaic units and the load peak-shaving output, as well as the reward for the emission reduction benefits of wind power and photovoltaic units generating clean energy.

[0017] In the further step 4, the CPLEX solver is used to solve the virtual power plant optimization scheduling model for grid peak load regulation service which introduces the virtual power plant peak load regulation penalty reward.

[0018] The present invention realizes the combination of accurate modeling of user peak-shaving response and optimized scheduling strategy through the constructed virtual power plant optimization scheduling model for power grid peak-shaving services, improves the efficiency of virtual power plants in power grid peak-shaving services, and not only enhances the stability of the power grid, but also ensures the flexibility and adaptability of scheduling decisions. The present invention accurately obtains the response parameters of users participating in peak-shaving by combining price elasticity and comfort cost, and uses quadratic functions to describe the nonlinear relationship between incentives and load reduction. The constructed virtual power plant peak-shaving response model improves prediction accuracy while considering the complexity of user behavior. The present invention introduces a virtual power plant peak-shaving penalty reward mechanism, which ensures the fairness and efficiency of resource utilization through rational benefit distribution, and encourages the active participation of users and operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0021] like Figure 1 As shown, this embodiment discloses a virtual power plant optimization scheduling method for power grid peak load regulation service, including the following steps:

[0022] Step 1: Establish the relationship between price incentives and user load reduction, and the relationship between comfort costs and user load reduction, thereby obtaining the relationship between user load reduction and price incentives; and establish a virtual power plant peak-shaving response model consisting of the relationship between user load reduction and price incentives.

[0023] In this embodiment, elastic demand is used to express the impact of changes in price incentives on user load shedding, thereby establishing the relationship between price elasticity, price incentives and user load shedding as shown in the following formula:

[0024]

[0025] Where: E i is the price elasticity; I is the price incentive given by the system to stimulate users to participate in peak load regulation, ΔI represents the change in price incentive; r is the load reduction of users, Δr represents the change in load reduction of users.

[0026] In this embodiment, the comfort cost is used to simulate the reaction behavior when facing incentives, reflecting that the user's participation in peak load regulation is a process of using comfort to exchange for benefits, that is, the relationship between the comfort cost and the load reduction amount used for peak load regulation is described by a quadratic function, as shown in the following formula:

[0027]

[0028] Where: C is the comfort cost, α COS , β COS are the parameters of the comfort cost function.

[0029] Therefore, the response parameters of users participating in peak load regulation are shown as follows:

[0030]

[0031] Where: B r Response parameters for users participating in peak load regulation.

[0032] In this embodiment, considering the limitations of the traditional price elasticity mechanism, a quadratic function is used to establish the relationship between the user load reduction and the price incentive, where the relationship between the uncertain area incentive and the load reduction is expressed as a classic quadratic function, as shown in the following formula:

[0033] I=a0r 2 +a1r+a2

[0034] a0=x0

[0035]

[0036] Wherein: a0, a1, a2 are parameters, which are calculated from x0, x1, x2, x3, x4. x0 represents the degree of change of the load reduction amount of users participating in peak load regulation with the change of incentives. x1 represents the incentive when the incentive and the user's comfort loss are exactly offset. x2 represents the user's load reduction amount when the incentive and the user's comfort loss are exactly offset. x3 represents the incentive corresponding to the maximum value of the user's load reduction amount. x4 represents the maximum value of the user's load reduction amount.

[0037] Finally, in this embodiment, a virtual power plant peak load response model is constructed based on the relationship between user load reduction and price incentives.

[0038] Step 2: Consider the virtual power plant peak-shaving response model obtained in step 1, take the maximum profit of the virtual power plant as the objective function, and establish constraints of the objective function, thereby constructing a virtual power plant optimization scheduling model for power grid peak-shaving services.

[0039] In this embodiment, the peak load revenue F based on the virtual power plant sell 、The cost of new energy n , the cost F required to mobilize the demand-side load to participate in peak load regulation according to the virtual power plant peak load regulation response model l 、Energy storage cost of virtual power plant F s , Carbon trading costs F c , taking the maximum profit of the virtual power plant as the objective function, the objective function of the virtual power plant optimization scheduling model for power grid peak load regulation service is constructed, and the objective function is shown in the following formula:

[0040]

[0041] Where: F sell is the peak load benefit of the virtual power plant, F n is the cost of new energy, F l The cost required to mobilize the demand side load to participate in peak load regulation, F s is the energy storage cost of the virtual power plant, F c is the carbon trading cost; F total is the total profit of the virtual power plant.

[0042] The peak load benefit F of the virtual power plant in the objective function sell As shown below:

[0043]

[0044] Where: peak,t is the price given by the dispatch center to encourage peak load regulation in different time periods, P peak,t is the amount of electricity used by the virtual power plant to load the peak, P peak,t,pre is the predicted reported peak load, π ω is the probability under the ω scenario, and Δt is the duration.

[0045] The cost of new energy in the objective function F n As shown below:

[0046]

[0047] Where: t is the unit operating cost of the new energy unit, F g,t 、F f,t are the cost of abandoned solar power and the cost of abandoned wind power, θ g,t ,θ f,t are the penalty costs for abandoning solar and wind power per unit power at time t, P g,rea,t , P f,rea,t The amount of wind power and photovoltaic power output participating in peak load regulation, F g,t 、F f,t are the cost of abandoned solar power and the cost of abandoned wind power respectively; P f,a,tw and P g,a,tw They are wind power and photovoltaic power abandonment respectively.

[0048] The cost F required to mobilize the demand side load to participate in peak load regulation in the objective function l As shown below:

[0049]

[0050] Where: I tωThe price incentive given by the system to stimulate users to participate in peak load regulation is obtained from the virtual power plant peak load regulation response model in step 1, P cor,tω To motivate the corresponding users to contribute, P loss,tω is the amount of power loss under the ω scenario. L,tω is the penalty factor for load power failure.

[0051] The energy storage cost F of the virtual power plant in the objective function s As shown below:

[0052] F s =a x (P charge +P discharge )+b x

[0053] Where: a x 、b x is the cost coefficient of energy storage equipment, P charge , P discharge are the charging power and discharging power of the energy storage device respectively.

[0054] Carbon trading cost F in the objective function c As shown below:

[0055]

[0056] Where: tax , buy , sell are the tax required when the carbon emission quota is exceeded, the price required to purchase the carbon emission quota, and the price when the excess carbon emission quota is sold. μ is a 0-1 variable. When C em >C st When μ is 1, when C em <C st When μ is 0, C em , C st , C buy They are the carbon emissions of the virtual power plant, the free carbon emissions obtained and the purchased carbon emissions.

[0057] In this embodiment, the constraints of the objective function include power balance constraints, network security constraints, photovoltaic power generation operation constraints, wind power operation constraints, user constraints, and energy storage constraints.

[0058] The power balance constraint is as follows:

[0059]

[0060] Where P L,t is the load reduction amount of user participation response, P storeContribute to energy storage equipment.

[0061] The network security constraints are as follows:

[0062] -P max ≤P l,t ≤P max

[0063] Where P l,t is the network flow power, P max It is the maximum value of the flow power that the network can withstand.

[0064] The photovoltaic power generation operation constraints are as follows:

[0065] 0≤P g,pre,t -P g,rea,t ≤P g,rea,t

[0066] Where P g,pre,t Represents photovoltaic power generation power.

[0067] The wind power operation constraints are as follows:

[0068] 0≤P f,pre,t -P f,rea,t ≤P f,rea,t

[0069] Where P f,pre,t Indicates wind power generation power.

[0070] User constraints are as follows:

[0071] k2≤P L,t ≤k4

[0072] Where k2 and k4 are the upper and lower limits of the user load reduction response area respectively.

[0073] The energy storage constraint is as follows:

[0074]

[0075] Where P charge,max , P charge,min are the maximum and minimum values ​​of the charging power of the energy storage device, P discharge,min , P discharge,max are the maximum and minimum values ​​of the discharge power of the energy storage device, P charge and P discharge are the charging power and discharging power of the energy storage device respectively.

[0076] Step 3: Introduce virtual power plant peak-shaving penalty rewards into the virtual power plant optimization scheduling model for grid peak-shaving services established in step 2. The introduced virtual power plant peak-shaving penalty rewards include deviation penalties between wind turbines and photovoltaic units and load peak-shaving output, as well as rewards for the emission reduction benefits of clean energy generated by wind power and photovoltaic units.

[0077] Specifically, in this embodiment, a deviation penalty is set for the wind turbine photovoltaic unit and the load peak load output, the deviation threshold is set to 30%, and the penalty fee is used to reward the wind turbine photovoltaic unit and the load whose deviation is within the threshold. The deviation penalty for the wind turbine photovoltaic unit and the load peak load output is shown in the following formula:

[0078] Punish1=a i I total 70%P peak,i,pre >Peak,i

[0079]

[0080] In the formula: Punish1, Award1, Punish total1 are the deviation penalty for wind power and photovoltaic units and load peak load output, the reward for wind power and photovoltaic units and load within 30% deviation, and the total amount of deviation penalty; P peak,i,pre is the peak load reported by the virtual power plant, Peak,i is the actual peak load of the virtual power plant, P peak,k is the output of wind power and photovoltaic units; I total is the total revenue of the virtual power plant, a i is the benefit allocation factor, α j is the deviation value of the jth device, m is the number of devices with a deviation value of 30%; P peak,j Indicates the power contributed by each part of the virtual power plant to peak load regulation.

[0081] In this embodiment, a reward is set for the emission reduction benefits of wind power and photovoltaic units generating clean energy, and the carbon emission costs are shared by energy storage equipment and users. The reward for the emission reduction benefits of wind power and photovoltaic units generating clean energy is shown in the following formula:

[0082]

[0083] Where: Punish2, Award2, Punish total2 The carbon emissions that energy storage equipment and users are responsible for, the rewards given to wind and photovoltaic units according to their output ratio, and the total cost of this part; P peak,i For energy storage or user output, Com CO2 The cost of carbon emissions.

[0084] Step 4: Use the CPLEX solver to solve the virtual power plant optimization scheduling model for grid peak-shaving services that introduces virtual power plant peak-shaving penalty rewards in step 3, and implement virtual power plant optimization scheduling based on the solution results.

[0085] The following uses a virtual power plant consisting of a wind turbine, a photovoltaic unit, an energy storage device and loads in the distribution network as an example to further illustrate the effect of the optimization scheduling method of this embodiment.

[0086] Combined with the deviation penalty and deviation rate between wind power and photovoltaic units and load peak output, the deviation rate above 30% will not be settled, and this part will be allocated to units and users with a deviation rate within 30% according to labor and deviation rate. The sharing of carbon emission costs is mainly borne by users and energy storage equipment. Wind power and photovoltaic units are used as new energy power generation, and the shared costs are rewarded to new energy units, as shown in Table 1:

[0087] Table 1 Distribution

[0088]

[0089] In order to illustrate the advantages of the virtual power plant optimization scheduling method for power grid peak load regulation service described in this embodiment, the traditional method of direct allocation without considering the risk and reward and punishment mechanism is compared with the method proposed in this embodiment. The results of the two methods are shown in Table 2:

[0090] Table 2 Comparison of different methods

[0091] Direct distribution / kW The method proposed in this embodiment / kW Load User 250.6305 359.202 Wind power 743.6689 753.9788 Photovoltaic 233.2573 428.210 Energy Storage 1128.524 687.798

[0092] According to the results in Table 2, according to the method proposed in this embodiment, the internal benefit distribution of the virtual power plant is no longer determined solely by the output, but is allocated from multiple aspects, which can further improve the peak-shaving quality and to a certain extent reward the part with small deviation and large output, thereby motivating each part to be more active in high-quality peak-shaving.

[0093] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. The embodiments described in the present invention are merely descriptions of the preferred embodiments of the present invention, and do not limit the concept and scope of the present invention. The various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction, and such combinations should also be regarded as the contents disclosed in the present disclosure as long as they do not violate the concept of the present invention. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0094] The present invention is not limited to the specific details of the above-mentioned embodiments. Within the technical concept of the present invention and without departing from the design concept of the present invention, various modifications and improvements made to the technical solution of the present invention by technical personnel in this field should fall within the protection scope of the present invention. The technical contents for which protection is sought in the present invention have been fully recorded in the claims.

Claims

1. A virtual power plant optimization scheduling method for power grid peak load regulation service, characterized in that: The following steps are involved: Step 1: Establish the relationship between price incentive and user load reduction, and the relationship between comfort cost and user load reduction, thereby obtaining the relationship between user load reduction and price incentive; and establish a virtual power plant peak load response model consisting of the relationship between user load reduction and price incentive; Step 2: Considering the virtual power plant peak load response model obtained in step 1, taking the maximum profit of the virtual power plant as the objective function, and setting constraints of the objective function, thereby constructing a virtual power plant optimization scheduling model for power grid peak load service; Step 3: Introduce the virtual power plant peak load regulation penalty reward into the virtual power plant optimization dispatching model for power grid peak load regulation service established in step 2; Step 4: Solve the virtual power plant optimization scheduling model for grid peak-shaving services that introduces virtual power plant peak-shaving penalty rewards in step 3, and implement virtual power plant optimization scheduling based on the solution results.

2. A virtual power plant optimization scheduling method for power grid peak load regulation service according to claim 1, characterized in that: In step 1, a quadratic function is used to represent the relationship between comfort cost and user load reduction.

3. The method for optimizing and dispatching a virtual power plant for power grid peak load regulation according to claim 1, characterized in that: In step 1, a quadratic function is used to represent the relationship between the amount of user load reduction and the change in price incentives.

4. The virtual power plant optimization scheduling method for power grid peak load regulation service according to claim 1 is characterized in that: In step 2, the objective function is constructed based on the peak-shaving revenue of the virtual power plant, the cost of new energy, the cost of mobilizing the demand-side load to participate in peak-shaving obtained according to the virtual power plant peak-shaving response model, the energy storage cost of the virtual power plant, and the carbon trading cost.

5. The method for optimizing and dispatching a virtual power plant for power grid peak load regulation service according to claim 1, characterized in that: In step 2, the set constraints include power balance constraints, network security constraints, photovoltaic power generation operation constraints, wind power operation constraints, user constraints and energy storage constraints.

6. The virtual power plant optimization scheduling method for power grid peak load regulation service according to claim 1 is characterized in that: In step 3, the virtual power plant peak-shaving penalty reward introduced includes the deviation penalty between the wind turbine and photovoltaic units and the load peak-shaving output, as well as the reward for the emission reduction benefits of wind power and photovoltaic units generating clean energy.

7. The method for optimizing and dispatching a virtual power plant for power grid peak load regulation service according to claim 1, characterized in that: In step 4, the CPLEX solver is used to solve the virtual power plant optimization scheduling model for grid peak load regulation service with the introduction of virtual power plant peak load regulation penalty reward.

Images