Power load dispatching optimization method based on demand response of smart power grid

Abstract

The invention discloses a power load dispatching optimization method based on demand response of a smart power grid, which comprises the following steps of: S1, judging a load management level, and predicting a power load condition of the next day according to historical power load data so as to obtain a total response load value # imgabs0 # in a response time period; and S3, demand response load scheduling is carried out. According to the power load dispatching optimization method based on the demand response of the smart power grid, reasonable management of the load in the power demand response can be realized, stable power supply of the smart power grid is ensured, the power supply pressure is relieved, and power resources are reasonably utilized.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power load dispatching, and in particular to an electric power load dispatching optimization method based on smart grid demand response. Background Art

[0002] Electricity load demand response is a strategy for electricity load management that guides users to actively adjust their own electricity load when the grid load is high, low, or when the grid security is threatened. Its goal is to encourage users to flexibly change their electricity consumption patterns according to changes in electricity supply and market prices, thereby helping the grid to achieve supply and demand balance. This mechanism enables users to optimize their electricity use behavior based on price signals and incentive policies, thereby ensuring the stable operation of the power system and efficient use of resources. How to reasonably design load management scheduling strategies during demand response is a key difficulty in current research. Summary of the invention

[0003] The purpose of the present invention is to provide a method for optimizing power load scheduling based on smart grid demand response, which can achieve reasonable management of loads in power demand response, ensure that the smart grid can provide stable power supply, alleviate power supply pressure and reasonably utilize power resources.

[0004] To achieve the above object, the present invention provides a method for optimizing power load dispatching based on smart grid demand response, comprising the following steps:

[0005] Step S1: Determine the load management level and predict the next day's load situation based on historical load data, so as to obtain the total response load value of the response time period.

[0006] Step S2: Obtain user responsive load data: Users participating in demand response need to sign a demand response subsidy agreement with the power load manager and report the maximum responsive load value. and the minimum responsive load value are the maximum responsive load value and the minimum responsive load value of the i-th user respectively. The power load management party establishes a user responsive load database through the protocol and calculates the responsive load value of the user according to the protocol and the responsive load value of the user. Response load value The load value actually responded by the i-th user when participating in the response for the jth time, and the relevant subsidies are given to the users participating in the response;

[0007] Step S3: Demand response load dispatch: based on the total response load value corresponding to the determined load management level and the user's maximum responsive load value and the minimum responsive load value The scheduling optimization plan is obtained through the demand response scheduling optimization algorithm.

[0008] Preferably, step S1 specifically includes: evaluating and analyzing whether demand response is needed the next day based on the predicted load curve for the next day, and setting the total response load value in the peak or trough time period.

[0009] Preferably, step S3 includes the following sub-steps:

[0010] S301, demand response scheduling optimization algorithm;

[0011] S302, scheduling plan evaluation;

[0012] S303, scheduling notification.

[0013] Preferably, step S301 is specifically as follows:

[0014] The global optimal fitness function for the jth demand response is as follows:

[0015]

[0016] Among them, T j represents the time period length of the jth demand response event, that is, the total number of time points involved in the response, T j represents the entire time range of the jth response, which is 96 time points in one day; N j represents the total number of users who participated in the jth response event; x i,j is a binary variable, indicating whether user i participates in load response in the jth response event. If x i,j =1, user i participates in the jth response; if x i,j =0, the user did not participate in the response; the parameter λ is used to control the weight of each part of the optimization objective, which is used in the formula to balance the optimization between load forecasting and user response costs; C represents the subsidy obtained by the user according to the agreement in the demand response event. The subsidy is used as a reward from the power supplier to the user for adjusting the electricity consumption behavior, and is used to encourage the user to actively participate in the demand response; is the total load value at time t of the next day predicted by the jth demand response; is the response load of user i at time point t in the jth response; is the average load value of the entire predicted time period T; k is the number of iterations.

[0017] Preferably, the individual optimal fitness function of user i's j-th demand response is as follows:

[0018]

[0019] Among them, F i,j (k) is the current load response value of user i The calculated evaluation index is used to measure the performance of the current solution in the optimization goal.

[0020] Preferably, the constraints of the demand response power dispatch optimization algorithm are as follows:

[0021]

[0022]

[0023] Among them, formula (3) is the range of load values ​​that each user can participate in the response, and formula (4) is the total load balance constraint, which is used to ensure that the target total response load can be met during the demand response process.

[0024] Preferably, a particle swarm optimization algorithm is used in the scheduling scheme evaluation in step S302, and particles and particle swarms are represented as follows:

[0025]

[0026]

[0027] Among them, X i,j (k) is the decision of user i in the kth iteration in the jth response event; is the response load value of user i at time point t in the jth response event; S j (k) is the particle swarm at the kth iteration in the jth response event.

[0028] Preferably, the core of the demand response power dispatch optimization algorithm lies in the speed and position update of each particle. The speed of the particle is used to adjust the search direction of the solution. The speed update formula is as follows:

[0029]

[0030] Among them, v i,t,j (k) is the adjustment step of the load response value at the kth iteration, which affects the update of the load response value at the next iteration of the algorithm; is the inertia weight, which is used to control the moving inertia of the particle in the current speed direction; c1 and c2 are the individual and global learning factors, respectively, which are used to indicate the degree of pursuit of the particle to its own optimal solution and the global optimal solution; γ1 and γ2 are random numbers between [0,1], which increase the randomness of the search; P best,i，t,j (k) and g best,t，j (k) are the individual optimal solution and the global optimal solution of user i in the kth iteration, respectively.

[0031] Preferably, the dispatching scheme evaluation also includes an iterative optimization stage, and the load response value update iterative formula is as follows:

[0032]

[0033] in, is the load response value of user i at time point t in the j-th response at the k+1-th iteration.

[0034] Preferably, step S303 is specifically as follows:

[0035] According to the scheduling plan obtained by the above-mentioned demand response scheduling optimization algorithm, the load is reduced during peak hours, or the load is increased during trough hours, and the specific load response value of each participating user is determined based on these responses, and the relevant users are notified to participate in the response by phone or text message.

[0036] Therefore, the present invention adopts the above-mentioned power load scheduling optimization method based on smart grid demand response, applies the particle swarm optimization algorithm (PSO) to the demand response scenario of the smart grid, and realizes demand-responsive power load scheduling management through the power scheduling optimization algorithm based on the predicted load curve of the next day and the established user-responsive load database, thereby ensuring the stable operation of the smart grid.

[0037] In addition, this method not only considers the efficiency of demand response, but also the economic compensation obtained by users for participating in demand response. The obtained scheduling scheme can not only efficiently realize the "peak shaving and valley filling" of power load, but also achieve low-cost operation, reduce the economic pressure of load management, and does not affect the economic benefits of users participating in the response, thus achieving a win-win situation.

[0038] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is an overall flow chart of an embodiment of a method for optimizing power load dispatching based on smart grid demand response of the present invention;

[0040] Figure 2 It is a flow chart of an electric power load dispatch optimization algorithm based on smart grid demand response of the present invention. DETAILED DESCRIPTION

[0041] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0042] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0043] Embodiment 1

[0044] like Figure 1 As shown, the present invention provides a method for optimizing power load scheduling based on smart grid demand response, comprising the following steps:

[0045] Step S1: Determine the load management level and predict the next day's load situation based on historical load data, so as to obtain the total response load value of the response time period. Specifically: Based on the predicted load curve for the next day, evaluate and analyze whether demand response is needed the next day, and set the total response load value during the peak or trough period.

[0046] Step S2: Obtain user responsive load data: Users participating in demand response need to sign a demand response subsidy agreement with the power load manager and report the maximum responsive load value. and the minimum responsive load value are the maximum responsive load value and the minimum responsive load value of the i-th user respectively. The power load management party establishes a user responsive load database through the protocol and calculates the responsive load value of the user according to the protocol and the responsive load value of the user. Response load value The load value actually responded by the i-th user when participating in the response for the jth time, and the relevant subsidies are given to the users participating in the response;

[0047] Step S3: Demand response load dispatch: based on the total response load value corresponding to the determined load management level and the user's maximum responsive load value and the minimum responsive load value The scheduling optimization plan is obtained through the demand response scheduling optimization algorithm.

[0048] Step S3 includes the following sub-steps:

[0049] S301, demand response scheduling optimization algorithm; the specific algorithm is:

[0050] The global optimal fitness function for the jth demand response is as follows:

[0051]

[0052] Among them, T j represents the time period length of the jth demand response event, that is, the total number of time points involved in the response, T j represents the entire time range of the jth response, which is 96 time points in one day; N j represents the total number of users who participated in the jth response event; x i,jis a binary variable, indicating whether user i participates in load response in the jth response event. If x i,j =1, user i participates in the jth response; if x i,j =0, the user did not participate in the response; the parameter λ is used to control the weight of each part of the optimization objective, which is used in the formula to balance the optimization between load forecasting and user response costs; C represents the subsidy obtained by the user according to the agreement in the demand response event. The subsidy is used as a reward from the power supplier to the user for adjusting the electricity consumption behavior, and is used to encourage the user to actively participate in the demand response; is the total load value at time t of the next day predicted by the jth demand response; is the response load of user i at time point t in the jth response; is the average load value of the entire predicted time period T; k is the number of iterations.

[0053] Fitness function F j (k) Aims to transform the peak shaving and valley filling problem into an actual load curve within the usage period that is as close as possible to a smooth average load curve.

[0054] The individual optimal fitness function of user i's j-th demand response is as follows:

[0055]

[0056] Among them, F i,j (k) is the current load response value of user i The calculated evaluation index is used to measure the performance of the current solution in the optimization goal.

[0057] The constraints of the demand response power dispatch optimization algorithm are as follows:

[0058]

[0059]

[0060] Among them, formula (3) is the range of load values ​​that each user can participate in the response, and formula (4) is the total load balance constraint, which is used to ensure that the target total response load can be met during the demand response process.

[0061] Electricity suppliers generally use smart meters to record users' instantaneous load every 15 minutes, thus forming a 96-point load curve for a day.

[0062] Step S302: Particle swarm optimization algorithm is used in scheduling scheme evaluation. Particles and particle swarms are represented as follows:

[0063]

[0064]

[0065]

[0066] Among them, X i,j (k) is the decision of user i in the kth iteration in the jth response event; is the response load value of user i at time point t in the jth response event; S j (k) is the particle swarm at the kth iteration in the jth response event.

[0067] The core of the demand response power dispatch optimization algorithm lies in the speed and position update of each particle. The speed of the particle is used to adjust the search direction of the solution. The speed update formula is as follows:

[0068]

[0069] Among them, v i,t,j (k) is the adjustment step of the load response value at the kth iteration, which affects the update of the load response value at the next iteration of the algorithm. is the inertia weight, which is used to control the moving inertia of the particle in the current speed direction; c1 and c2 are the individual and global learning factors, respectively, which are used to indicate the degree of pursuit of the particle to its own optimal solution and the global optimal solution; γ1 and γ2 are random numbers between [0,1], which increase the randomness of the search; P best,i，t，j (k) and g best,t，j (k) are the individual optimal solution and the global optimal solution of user i in the kth iteration, respectively.

[0070] The flow chart of the power load dispatch optimization algorithm based on smart grid demand response in the present invention is as follows: Figure 2 shown.

[0071] Particle initialization stage

[0072] The initial particle position formula is as follows:

[0073]

[0074] Among them, in the particle initialization stage, a particle swarm S is randomly generated j (0), initial load response value for each user A value is randomly generated within the constraints of the above-mentioned demand response power dispatch optimization algorithm, and σ is a random number in the interval price of [0, 1].

[0075] The formula for initial particle velocity is as follows:

[0076] v i，t，j (0) = γ·(P max，i -Pmin，i ) (11);

[0077] The initial velocity of the particle v i，t，j (0) is also randomly generated, and the initial fitness function F of each particle is calculated j (0), and set the initial load response value of the particle to the individual optimal solution P best，i，t，j (0), find the global optimal solution g of the group best，t，j (0).

[0078] Iterative optimization phase

[0079] The load response value update iterative formula is as follows:

[0080]

[0081] in, is the load response value of user i at time point t in the j-th response at the k+1-th iteration.

[0082] The update rule of the individual optimal response load value is as follows:

[0083] F best，t，j (k) = min(F i，j (0), F i，j (1), ..., F i，j (k)) (13);

[0084]

[0085] Among them, F best，i，j (k) is the optimal target value of user i at the kth iteration, F i，j (k) is the individual fitness function value of user i in the jth response event at the kth iteration, P best，i，t，j (k+1) is the optimal load response value of user i in the k+1th iteration in the dth response event.

[0086] The update rule of the global optimal response load value is as follows:

[0087] F global，j (k) = min(F j (0), F j (1), ..., F j (k)) (15);

[0088]

[0089] Among them, F global，j (k) is the optimal target value of user i at the kth iteration, F j(k) is the individual fitness function value of user i in the jth response event at the kth iteration, g best,t，j (k+1) is the optimal load response value of user i in the jth response event at the k+1th iteration

[0090] Iteration termination condition

[0091] The iteration is terminated based on the relative convergence ε of the global fitness function, and the formula is as follows:

[0092]

[0093] By setting the threshold ε, the relative change is used to judge the degree of convergence. When the relative change is less than the threshold ε in N iterations, it means that the algorithm has basically converged and the iteration can be stopped to obtain the global optimal solution, that is, the scheduling optimization plan for the jth response event. The plan has the user information participating in this response event and the load value that each user needs to respond to.

[0094] Step S303: Scheduling notification, specifically:

[0095] According to the scheduling plan obtained by the above-mentioned demand response scheduling optimization algorithm, the load is reduced during peak hours, or the load is increased during trough hours, and the specific load response value of each user participating in the response is determined based on these responses, and the relevant users are notified to participate in the response through telephone, text messages, etc.

[0096] It is worth noting that the contents not elaborated in detail in the present invention are all prior art and are well known to those skilled in the art.

[0097] Therefore, the present invention adopts the above-mentioned power load scheduling optimization method based on smart grid demand response, which can achieve reasonable management of loads in power demand response, ensure that the smart grid can provide stable power supply, alleviate power supply pressure and reasonably utilize power resources.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for optimizing power load dispatching based on smart grid demand response, characterized in that: The following steps are involved: Step S1: Determine the load management level and predict the next day's load situation based on historical load data, so as to obtain the total response load value of the response time period. Step S2: Obtain user responsive load data: Users participating in demand response need to sign a demand response subsidy agreement with the power load manager and report the maximum responsive load value. and the minimum responsive load value are the maximum responsive load value and the minimum responsive load value of the i-th user respectively. The power load management party establishes a user responsive load database through the protocol and calculates the responsive load value of the user according to the protocol and the responsive load value of the user. Response load value The load value actually responded by the i-th user when participating in the response for the jth time, and the relevant subsidies are given to the users participating in the response; Step S3: Demand response load dispatch: based on the total response load value corresponding to the determined load management level and the user's maximum responsive load value and the minimum responsive load value The scheduling optimization plan is obtained through the demand response scheduling optimization algorithm.

2. The method for optimizing power load dispatching based on smart grid demand response according to claim 1, characterized in that: Step S1 is as follows: according to the predicted load curve for the next day, evaluate and analyze whether demand response is needed the next day, and set the total response load value in the peak or trough time period.

3. The method for optimizing power load dispatching based on smart grid demand response according to claim 2, characterized in that: Step S3 includes the following sub-steps: S301, demand response scheduling optimization algorithm; S302, scheduling plan evaluation; S303, scheduling notification.

4. The method for optimizing power load dispatching based on smart grid demand response according to claim 3 is characterized in that: Step S301 is specifically as follows: The global optimal fitness function of the j-th demand response is shown in the following formula: Among them, T j N represents the length of the time period of the jth demand response event, that is, the total number of time points involved in the response; j represents the total number of users who participated in the jth response event; x i,j is a binary variable, indicating whether user i participates in load response in the jth response event; parameter λ is used to control the weight of each part in the optimization objective; C represents the subsidy obtained by the user according to the agreement in the demand response event; is the total load value at time t of the next day predicted by the jth demand response; is the response load of user i at time point t in the jth response; is the average load value of the entire predicted time period T; k is the number of iterations.

5. The method for optimizing power load dispatching based on smart grid demand response according to claim 4, characterized in that: The individual optimal fitness function of user i's j-th demand response is shown in the following formula: Among them, F i,j (k) is the current load response value of user i Calculated evaluation index.

6. The method for optimizing power load dispatching based on smart grid demand response according to claim 5, characterized in that: The constraints of the demand response power dispatch optimization algorithm are as follows: Among them, formula (3) is the range of load values ​​for each user to participate in the response, and formula (4) is the total load balance constraint.

7. The method for optimizing power load dispatching based on smart grid demand response according to claim 6, characterized in that: In step S302, a particle swarm optimization algorithm is used in the scheduling scheme evaluation. The representation of particles and particle swarms is shown in the following formula: Among them, X i,j (k) is the decision of user i in the kth iteration in the jth response event; is the response load value of user i at time point t in the jth response event; S j (k) is the particle swarm at the kth iteration in the jth response event.

8. The method for optimizing power load dispatching based on smart grid demand response according to claim 7, characterized in that: The core of the demand response scheduling optimization algorithm lies in the speed and position update of each particle. The particle speed update formula is as follows: Among them, v i,t,j (k) is the adjustment step of the load response value at the kth iteration, which affects the update of the load response value at the next iteration of the algorithm; is the inertia weight; c1, c2 are individual and global learning factors respectively; γ1, γ2 are random numbers between [0, 1]; P best,i,t,j (k) and g best,t,j (k) are the individual optimal solution and the global optimal solution of user i in the kth iteration, respectively.

9. The method for optimizing power load dispatching based on smart grid demand response according to claim 8, characterized in that: The dispatch scheme evaluation also includes an iterative optimization phase, in which the load response value update iterative formula is as follows: in, is the load response value of user i at time point t in the j-th response at the k+1-th iteration.

10. The method for optimizing power load dispatching based on smart grid demand response according to claim 9, characterized in that: Step S303 is specifically as follows: According to the scheduling plan obtained by the above-mentioned demand response scheduling optimization algorithm, the load is reduced during peak hours, or the load is increased during trough hours, and the specific load response value of each participating user is determined based on these responses, and the relevant users are notified to participate in the response by phone or text message.

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