Load management decision-making method

Abstract

The invention discloses a load management decision-making method, which comprises the following steps of: inputting user load management related parameters, establishing load management decision multi-dimensional evaluation indexes, and comprehensively reflecting each attention dimension in a load management decision-making process; converting a load management decision multi-dimensional evaluation index into decision constraints of a power grid company and a user side, constructing an economic function representing the power grid company and the user, and establishing a load management decision model considering the minimum social loss; and solving the load management decision model to obtain an optimized user main body result participating in load management. According to the method, the constraints of economy, low carbon, sociality and the like are taken into consideration, the economy and carbon emission of a load management scheme are effectively improved while the regulation and control requirements of a power grid company are met, the redundant investment of load side resources is reduced, and the satisfaction degree and recognition degree of a user side on a load management decision scheme are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of demand response, and particularly relates to a load management decision-making method. Background Art

[0002] With the expansion of the scale of new energy, especially distributed photovoltaics, the mismatch between power generation and consumption times in many places has occurred, and the load peak-valley difference has gradually widened. Especially during the peak summer period, the air-conditioning load rises rapidly, and the power supply guarantee pressure further increases during peak power consumption periods such as noon and evening. The importance of demand-side resources participating in regulation is highlighted.

[0003] Currently, load management schemes mainly consider the constraints of load regulation volume. The schemes disclosed in the prior art are as follows: Application No.: 202310716384.6, which discloses an auxiliary decision-making method for load control in a new power system, provides an auxiliary decision-making method for load control in a new power system, aiming to solve the problem that it is difficult to emergently control the regional load caused by insufficient new energy output in the new power system. By determining the users participating in load control and constructing a historical database of relevant load curves, the auxiliary decision-making of load control is realized; Application No.: 202010812163.5, which discloses an energy optimization method for industrial demand response aggregators based on multi-agent, proposes an energy optimization method for industrial demand response aggregators based on multi-agent. By dividing the market agents into three layers: the supply side, the middle side, and the demand side, establishing a power consumption optimization model, and scheduling the industrial operation of electrical equipment, demand-side response is realized. The existing schemes do not comprehensively consider the deep participation of the user side in load management. For example, key factors such as user regulation credibility, regulation willingness, and carbon emission contribution are not fully incorporated into the evaluation, which may lead to poor effects in the actual implementation of load management schemes, making it difficult to effectively motivate users to actively participate, and also unable to balance economy and low carbon.

[0004] In response to the above problems, this application introduces indicators such as user regulation credibility, regulation effect, regulation willingness, and regulation carbon reduction amount to more comprehensively describe the behavioral characteristics of the user side in load management. At the same time, this application constructs a load management optimization model with the goal of minimizing social losses, taking into account economy and low carbon while meeting the grid regulation requirements, thereby improving the overall optimization effect of the load management scheme. Summary of the Invention

[0005] The purpose of the present invention is to propose a load management decision-making method, which introduces indicators such as user regulation credibility, regulation effect, regulation willingness, and regulation carbon reduction amount, constructs a load management decision-making model with the goal of minimizing social losses, and screens out load-side entities that meet economy and low carbon while meeting the grid regulation requirements, thereby improving the overall optimization effect of the load management scheme.

[0006] To achieve the above-mentioned invention purpose, the present invention adopts the following technical solutions:

[0007] A load management decision-making method, the method comprising the following steps:

[0008] S1. Input parameters related to user load management, and establish multi-dimensional evaluation indexes for load management decision-making, including user regulation credibility, proportion of non-impact regulation measures, user regulation willingness, and carbon reduction amount of regulation,

[0009] S2. Convert the multi-dimensional evaluation indexes for load management decision-making into decision-making constraints for the power grid company and the user side, construct an economic function representing the power grid company and the user, and establish a load management decision-making model considering the minimum social loss. The constraint conditions include load regulation amount constraint, user regulation credibility constraint, user regulation willingness constraint, unit output value loss constraint, and carbon reduction amount constraint;

[0010] S3. Solve the load management decision-making model to obtain the optimized results of user entities participating in load management.

[0011] Among them, the multi-dimensional evaluation indexes for load management decision-making are specifically:

[0012] Starting from the time perspective, user regulation credibility refers to the ratio of the duration during which the actual regulated load of the user is less than or equal to the target regulated load in a certain period to the total duration during the load management implementation process.

[0013]

[0014] In the formula: is the total duration during which the actual regulated load is less than or equal to the target regulated load in a certain period during the load management implementation process; is the actual regulated load of the nth user at the tth moment, is the target regulated load of the nth user at the tth moment, N is the number of users participating in load management, and T is the total duration of load management,

[0015]

[0016] In the formula, u n is the regulation credibility of the nth user at the tth moment.

[0017] Starting from the electricity quantity perspective, user regulation effect refers to the degree of realization of the user's regulation target during the load management implementation process, which is expressed by the ratio of the actual regulated quantity of the user to the target regulated quantity.

[0018]

[0019] In the formula: v n is the regulation effect of the nth user, It is the actual regulated load amount of the user during the implementation of the load management plan. It is the target regulated load amount of the user.

[0020] The proportion of non - impact regulation measures refers to the ratio of the reduced load regulation amount due to early maintenance and other work during the implementation of load management to the regulation amount of this user. Non - impact regulation measures will not affect the electricity charges receivable by the power grid company. If the proportion of non - impact regulation measures is high, it means that the impact of this user's participation in this load management on the electricity charge loss of the power grid company is small.

[0021] The user's regulation willingness refers to the degree of enthusiasm and willingness of the user to participate in load management events, which can be obtained through user research scoring or user's initiative to report.

[0022] The carbon emission reduction amount brought about by regulation refers to the reduction of the corresponding carbon emissions by reducing the user's electricity consumption through the implementation of load regulation measures. Its value is the product of the carbon emission factor per unit of electricity and the load regulation amount.

[0023]

[0024] In the formula: E n is the carbon emission reduction amount of the user, and e n is the carbon emission factor per unit of electricity of the nth user.

[0025] Among them, the objective function of the load management decision - making model is:

[0026] Construct an economic function representing the power grid company and the user, which is characterized by Objective 1 and Objective 2 respectively.

[0027] Objective 1: Based on the economic needs of the power grid company, measure the load regulation loss of the power grid company by the electricity charge loss, and use it as the economic function of the power grid company.

[0028]

[0029] In the formula, F1 is the level of electricity charge loss; μ n is a 0 - 1 Boolean variable. When it is 1, it means that user n is selected; when it is 0, it means that user n is not selected. is the regulation capacity of the nth user at time t; τ is the ratio between the load management capacity determined by the power grid and the actual load management gap; P total,tar is the actual load management gap; ρ t is the electricity price at time t. is the load amount of the nth user at time t that does not affect the load management plan such as early maintenance.

[0030] Objective 2: Based on the economic needs of the user, measure the user's load regulation loss by the difference between the output value loss and the carbon emission reduction benefit, and use it as the economic function of the user.

[0031]

[0032] In the formula, F2 is the loss level of load regulation; C n is the output value loss of the nth user; is the carbon emission reduction benefit of the nth user; is the output value loss per unit load of the nth user; ρ carb is the unit carbon price; α is the conversion coefficient of carbon emission factor,

[0033] Convert the above objective into the objective of minimizing social loss:

[0034]

[0035] The above formula is the objective function after multi-objective optimization, is the expected electricity charge loss of the power company, is the expected regulation loss of users, and λ1, λ2 are weight coefficients, and λ1 + λ2 = 1.

[0036] Among them, the constraint conditions of the load management decision model include:

[0037] The constraint conditions include load regulation quantity constraint, user regulation credibility constraint, user regulation willingness constraint, output value loss per unit constraint, and carbon emission reduction quantity constraint;

[0038] 1), Load regulation quantity constraint;

[0039]

[0040] 2), User regulation credibility constraint;

[0041]

[0042] In the formula: u min refers to the minimum value of user regulation credibility set by the power grid company,

[0043] 3), Load regulation effect constraint;

[0044]

[0045] In the formula: v min refers to the minimum value of user regulation effect set by the power grid company,

[0046] 4), User regulation willingness constraint;

[0047]

[0048] In the formula: y n is the regulation willingness of the nth user, y minIt refers to the minimum value of the user's regulation willingness set by the power grid company.

[0049] 5), Constraint on the loss per unit output value; From the perspective of the whole society, it is expected that the loss of output value caused by the load regulation on the user side is as small as possible.

[0050]

[0051] In the formula: It refers to the maximum value of the loss per unit output value set by the power grid company.

[0052] 6), Constraint on the carbon reduction amount;

[0053]

[0054] In the formula: E min Is the minimum value of the carbon reduction amount set by the power grid company.

[0055] Solve the load management decision-making model to obtain the optimized results of the user entities participating in load management:

[0056] First, read the data, read the 7 index data of the resource entities and the hourly regulation capacity data of the users from two Excel files respectively; then define the number of resource entities, define the decision variables to represent whether to select a certain resource entity; then define the objective function and constraint conditions, aiming to minimize the social loss, with the load regulation amount, user regulation credibility, user regulation willingness, loss per unit output value and carbon reduction amount as constraints, and use the CPLEX solver to solve this mixed-integer programming problem to obtain the optimized results of the user entities participating in load management. Through YALMIP as the optimization framework, transform the decision variables, objective function and constraint conditions into a standard mathematical model, and optimize through the solver to output the optimized results of the user entities participating in load management.

[0057] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the load management decision-making method described above.

[0058] A computer-readable storage medium stores computer instructions thereon. When the computer instructions are executed by a processor, they implement the load management decision-making method described above.

[0059] Compared with the prior art, the beneficial effects of the present invention:

[0060] The present invention establishes a multi-dimensional evaluation index system for load management decisions by inputting user load management related parameters, and takes into account multiple factors such as economy, low carbon, and regulation willingness, so that load management decisions are more comprehensive and scientific, and can effectively balance the regulation needs of power grid companies and users, improve users' recognition of load management solutions, and avoid the one-sided problems caused by single-target optimization; in the optimization goals, the economy, low carbon, and social losses are fully considered, and the user entities suitable for participating in load management are accurately identified, which effectively reduces the redundant resource investment caused by unreasonable load management strategies, improves the regulation efficiency of power grid companies on load resources, reduces overall social costs, and enhances the safe and stable operation capabilities of the power grid.

[0061] In terms of indicator selection, comprehensive evaluation and precise optimization are carried out. By constructing multi-dimensional evaluation indicators, multiple factors such as economy, low carbon, and user regulation effects are taken into consideration, so that the load management plan can be optimized more accurately and avoid the one-sided problems caused by single-target optimization.

[0062] In terms of load demand, it dynamically adapts to changes in load demand. The present invention can dynamically adjust the load management scheme according to the real-time changes in the load demand of the power grid, improve the flexibility and adaptability of the scheme, and make the power grid regulation more efficient.

[0063] In terms of the impact on the power grid, multiple factors such as economy, low carbon, and user control effects are taken into consideration. By constructing multi-dimensional evaluation indicators, the load management plan is more accurately optimized, the load management decision is more scientific and reasonable, and the power grid company's control efficiency of load resources is improved, avoiding the one-sided problems caused by single-target optimization.

[0064] In terms of the impact on users, the user participation and satisfaction are enhanced. The proposed method fully considers the economic benefits and participation willingness of the user side, improves the user's recognition of the load management plan, and promotes users to actively respond to load management events.

[0065] In terms of social impact, the model is optimized with the goal of minimizing social losses, effectively reducing redundant resource inputs caused by unreasonable load management strategies, improving resource utilization, and reducing overall social costs.

[0066] In terms of environmental impact, we promote low-carbon and sustainable development. In the optimization process, we combine the constraints of carbon reduction indicators to guide the load side to adopt more environmentally friendly and low-carbon energy use methods, providing strong support for building a green and low-carbon energy system.

[0067] In summary, the present invention not only improves the power grid regulation capability, but also achieves the coordinated optimization of economic benefits, environmental benefits and social benefits, and provides innovative ideas and efficient solutions for future load management. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is the flow chart of the method of the present invention,

[0069] Figure 2 is the schematic diagram of the participation in orderly power consumption under the optimized model,

[0070] Figure 3 is the schematic diagram of the participation in orderly power consumption under the traditional model. Specific embodiments

[0071] The present invention will be further described below in conjunction with specific embodiments.

[0072] Embodiment 1: A load management decision-making method, the method comprising the following steps

[0073] S1: Taking an orderly power consumption event as an example, the average electricity price on the user side is about 0.8 yuan / kWh, the number of users N is 100; the load management gap is about 1500 kW, the duration is 24 hours, and the ratio τ between the load management capacity determined by the power grid decision and the actual load management gap is taken as 1.3, that is, the target load management gap is 1950 kW; determined by the historical regulation event experience value; the minimum value u of the user regulation credibility min is taken as 0.7; the minimum limit v of the regulation effect min is taken as 0.8; the minimum user participation willingness y allowed by the power grid side min is taken as 0.25; the maximum value C of the unit output value loss max is taken as 1.1; the minimum value E of the carbon reduction amount min is taken as 5 tons; the unit carbon price ρ carb is taken as 75 yuan / ton.

[0074] The user information participating in the load management event is as follows:

[0075] Table 1 Orderly power consumption user information

[0076]

[0077]

[0078]

[0079]

[0080] S2: The objective function of the load management decision-making model is:

[0081] Objective 1: Based on the economic needs of the power grid company, the load regulation loss of the power grid company is measured by the electricity fee loss and used as the economic function of the power grid company.

[0082]

[0083] where F1 is the level of electricity cost loss; μ n is a 0-1 Boolean variable, being 1 indicates that user n is selected, and being 0 indicates that user n is not selected; is the regulation capacity of the nth user at time t; τ is the ratio between the load management capacity of the power grid decision and the actual load management gap; P total,tar is the actual load management gap; ρ t is the electricity price at time t; is the load volume of the nth user at time t that does not affect the load management plan, such as early maintenance.

[0084] Objective 2: Based on the economic needs of users, the loss of user load regulation is characterized by the difference between the output value loss and the carbon reduction benefit, and is used as the user economic function.

[0085]

[0086] where F2 is the level of load regulation loss; C n is the output value loss of the nth user; is the carbon reduction benefit of the nth user; is the output value loss per unit load of the nth user; ρ carb is the unit carbon price; α is the conversion coefficient of the carbon emission factor.

[0087] The above objectives are transformed into the objective of minimizing social loss:

[0088]

[0089] The above formula is the objective function after multi-objective optimization, is the expected electricity cost loss of the power company, is the expected regulation loss of the user, and λ1, λ2 are weight coefficients, and λ1 + λ2 = 1.

[0090] S3: The constraint conditions of the load regulation model include:

[0091] 1), Actual load regulation quantity constraint;

[0092]

[0093] 2), User regulation credibility constraint;

[0094]

[0095] 3), User regulation effect constraint;

[0096]

[0097] 3), User participation willingness constraint;

[0098]

[0099] 4), Unit output value loss constraint;

[0100] μ n C n ≤1.1, n ∈ N

[0101] This reflects the potential load regulation loss level of users, and it is expected that the output value per kilowatt-hour is as small as possible;

[0102] 5), Carbon emission reduction constraint;

[0103]

[0104] S4. Solve the load management decision model to obtain the optimized results of the user entities participating in load management.

[0105] Figure 2 It is the orderly power consumption participation situation of users under the optimized model, that is, the load regulation amount of users at each moment is greater than the set regulation amount gap requirement. The users participating in regulation are No. 8, 17, 20, 33, 36, 38, 42, 45, 50, 63, 72, 73, 76, 79, 80, 82, 84, 86, 87, 89, 92, 99, a total of 22 households.

[0106] Assume that the traditional model refers to making decisions on user entities participating in the order of user sorting, and compare the proposed model with the traditional model. Figure 3 It is the orderly power consumption participation situation when setting the load demand of users under the traditional model, that is, the load regulation amount of users at each moment is greater than the set regulation amount gap requirement. The users participating in regulation are No. 1 - 25, a total of 25 households.

[0107] Table 2 Economic comparison of two models

[0108] Traditional model Optimized model Objective 1: Grid loss / yuan 14162.02 5617.88 Objective 2: User loss / yuan 56618.38 42459.12

[0109] As can be seen from Table 2, in terms of economy, because the traditional scheme does not consider factors such as the proportion of non - impact measures such as advance maintenance, user regulation effect, and user regulation willingness, the quality of users participating in the load management scheme is uneven. The grid loss of the traditional model obtained from the simulation experiment is 14162.02 yuan, while the grid loss of the optimized model is 5617.88 yuan, with a promotion ratio of 60.33%; the economic loss of users in the traditional model is 56618.38 yuan, and the economic loss of users after optimization is reduced to 42459.12 yuan, with a promotion ratio of 25.01%.

[0110] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A load management decision method, characterized in that: The method comprises the following steps: S1. Input the relevant parameters of user load management and establish multi-dimensional evaluation indicators for load management decision-making, including user regulation credibility, proportion of non-impact regulation measures, user regulation willingness, and regulation carbon reduction. S2. Convert the multi-dimensional evaluation indicators of load management decisions into decision constraints on the power grid company and the user side, construct an economic function that characterizes the power grid company and the user, and establish a load management decision model that takes into account the minimum social loss. The constraints include load control quantity constraints, user control credibility constraints, user control willingness constraints, unit output value loss constraints, and carbon reduction constraints; S3. Solve the load management decision model to obtain the optimized user subject results participating in load management.

2. A load management decision method according to claim 1, characterized in that: The load management decision multi-dimensional evaluation index is specifically: From the perspective of time, the user control credibility refers to the ratio of the duration during which the user's actual control load is less than or equal to the target control load during the period to the total duration. Where: The total duration during which the actual regulated load is less than or equal to the target regulated load during the load management implementation period; is the actual control load of the nth user at time t, is the target load regulation of the nth user at time t, N is the number of users participating in load management, T is the total duration of load management, In the formula, u n is the control credibility of the nth user at time t, The user control effect is based on the power consumption, which refers to the degree to which the user achieves its control target during the implementation of load management, and is expressed as the ratio of the user's actual control amount to the target control amount. Where: v n is the control effect of the nth user, To help users to actually control the load during the implementation of the load management plan. Adjust the load according to the user's goals, The carbon reduction due to regulation refers to the reduction of user electricity consumption through the implementation of load regulation measures, thereby reducing the corresponding carbon emissions. Its value is the product of the unit electricity carbon emission factor and the load regulation amount. Where: E n Reduce carbon emissions for users, e n is the carbon emission factor per unit electricity of the nth user.

3. A load management decision method according to claim 1, characterized in that: The objective function of the load management decision model is: Construct an economic function that characterizes the power grid company and the user, which is represented by Objective 1 and Objective 2 respectively.

4. A load management decision method according to claim 3, characterized in that: Objective 1: Based on the economic needs of the power grid company, the power grid company's load regulation loss is measured by the electricity fee loss, and used as the economic function of the power grid company. Where F1 is the electricity loss level; μ n is a 0-1 Boolean variable, 1 means user n is selected, 0 means user n is not selected; is the control capacity of the nth user at time t; τ is the ratio between the load management capacity of the power grid decision and the actual load management gap; P total,tar Management gap for actual load; ρ t is the electricity price at time t; Early maintenance for the nth user at time t does not affect the load of the load management plan.

5. A load management decision method according to claim 3, characterized in that: Objective 2: Based on the economic needs of users, the difference between output value loss and carbon reduction benefits is used to represent the user load regulation loss, and used as the user economic function. Where, F2 is the load regulation loss level; C n is the output value loss of the nth user; Carbon reduction benefit for the nth user; is the unit load output value loss of the nth user; ρ carb is the unit carbon price; α is the carbon emission factor conversion coefficient, Transform the above goals into the goal of minimizing social losses: The above formula is the objective function after multi-objective optimization. For the power company, the expected loss of electricity bills, is the user's expected control loss, λ1 and λ2 are weight coefficients, and λ1+λ2=1.

6. A load management decision method according to claim 1, characterized in that: The load management decision model constraints include: The constraints include load control quantity constraint, user control credibility constraint, user control willingness constraint, unit output value loss constraint and carbon reduction constraint; 1) Load control quantity constraints; 2) User-controlled credibility constraints; Where: u min It refers to the minimum value of user regulation credibility set by the power grid company. 3) Constraints on load regulation effect; Where: v min It refers to the minimum value of user regulation effect set by the power grid company. 4) Constraints on user regulation willingness; Where: y n is the regulation intention of the nth user, y min It refers to the minimum value of user regulation willingness set by the power grid company. 5) Constraints on unit output value loss: From the perspective of the whole society, it is expected that the output value loss caused by user-side load regulation is as small as possible. Where: It refers to the maximum loss per unit output value set by the power grid company. 6) Carbon reduction constraints; Where: E min The minimum carbon reduction amount set for power grid companies.

7. A load management decision method according to claim 1, characterized in that: The load management decision model is solved to obtain the results of the user subjects participating in load management after optimization: First, the seven indicator data of the resource subject and the hourly regulation capacity data of the user are input; then the number of resource subjects is defined, and the decision variables are defined to indicate whether a certain resource subject is selected; then the objective function and constraints are defined, with the goal of minimizing social losses and the load regulation amount, user regulation credibility, user regulation willingness, unit output value loss and carbon reduction as constraints. The CPLEX solver is used to solve this mixed integer programming problem, and the user subject results participating in load management after optimization are obtained. YALMIP is used as the optimization framework to transform the decision variables, objective functions and constraints into a standard mathematical model, which is optimized through the solver to output the optimized user subject results participating in load management.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the load management decision method as described in any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by a processor, the load management decision method according to any one of claims 1 to 7 is implemented.

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