User multi-target demand response strategy generation method based on dynamic electric carbon factors

Abstract

The invention relates to the technical field of electric power operation and maintenance, and particularly discloses a user multi-target demand response strategy generation method based on a dynamic electric carbon factor, and the method comprises the steps: building a plurality of different demand response scene models according to the user load characteristics in different seasons; constructing a constraint condition of each demand response scene model, and introducing a corresponding penalty function for each constraint condition; and integrating a multi-target sparrow optimization algorithm into the demand response scene model to optimize the demand response scene model. According to the user multi-target demand response strategy generation method based on the dynamic electric carbon factor, multi-target optimization of load balance, cost reduction and carbon emission reduction of a power system can be realized.

Description

Technical Field

[0001] The present invention relates to the field of electric power operation and maintenance technology, and in particular to a method for generating a user multi-objective demand response strategy based on a dynamic electric carbon factor. Background Art

[0002] In the context of the current low-carbon transformation of the power system, the application of demand response strategies is becoming more and more widespread. The demand response strategy mainly encourages users to reduce electricity consumption during peak load periods of the power grid to achieve load balance and carbon emission reduction goals of the power system. In recent years, the time-of-use electricity price mechanism has been widely used in demand response strategies. It sets different electricity prices during peak and off-peak periods to encourage users to use electricity during periods with lower electricity prices, thereby reducing electricity costs. In addition, some power systems have also introduced an electricity carbon factor to measure the carbon emissions generated by unit electricity consumption, thereby quantifying users' carbon emissions.

[0003] Among the existing demand response strategies, time-of-use electricity prices and demand response incentives have been widely adopted by power companies to effectively guide users to reduce electricity consumption during peak load periods. Such demand response technologies help the power system balance the load, especially during peak demand periods, by changing the user's electricity consumption time to reduce the peak and fill the valley, thereby alleviating the pressure on electricity supply and demand to a certain extent. At the same time, the time-of-use electricity price mechanism provides users with incentives to use lower electricity prices during off-peak periods, reducing users' electricity expenditures. However, this time-of-use electricity price strategy mainly focuses on load balancing and economic optimization, and rarely incorporates carbon emission reduction into the decision-making process. Especially under the requirements of low-carbon transformation, it is difficult to promote effective carbon emission optimization with electricity price signals alone.

[0004] In order to further support low-carbon development, the power systems in some regions have introduced electricity carbon factors, attempting to influence users' electricity choices by marking the carbon emissions per unit of electricity in different time periods. However, in the existing demand response strategies, the application of electricity carbon factors has not yet formed a unified standard and is often used as an additional monitoring parameter. The electricity carbon factor is currently used to provide carbon emission information rather than as a key decision-making indicator for demand response. This makes the role of the electricity carbon factor in regulating user electricity consumption behavior still relatively limited.

[0005] In addition, existing demand response strategies usually fail to systematically combine electricity prices with electricity carbon factors. Although some technical solutions have proposed demand response scenario models based on electricity carbon factors, most of them are static models that fail to fully consider the dynamic changes of electricity carbon factors. For example, when the output of renewable energy such as wind power and photovoltaic power fluctuates greatly, the electricity carbon factor will fluctuate accordingly, but the existing models cannot reflect the changes in the electricity carbon factor in real time, thus affecting the efficiency of carbon emission reduction.

[0006] Existing demand response strategies have many problems and defects in practical applications, including the following aspects:

[0007] 1. Lack of a multi-objective optimization model that comprehensively considers the impact of electricity prices and electricity carbon factors on demand response: Existing demand response scenario models mainly rely on the time-of-use electricity price mechanism to guide loads, while the use of electricity carbon factors is relatively simple. Most models only focus on the guiding role of electricity price signals when designing, but fail to combine electricity prices with electricity carbon factors at the same time to achieve dual optimization of economic costs and carbon emissions when users respond. Since carbon emissions fluctuate significantly in different time periods, it is difficult to effectively guide users to use electricity during low-carbon periods relying solely on time-of-use electricity prices, which affects the contribution of demand response to low-carbon transformation. Therefore, current technology has not yet established a complete, multi-objective demand response scenario model, and cannot meet the requirements for the development of low-carbon power systems.

[0008] 2. Difficult to adapt to the dynamic changes of the electricity carbon factor: The electricity carbon factor will fluctuate due to changes in multiple factors such as the proportion of renewable energy generation in the power system, load demand, and climate conditions, and this fluctuation may be large in a short period of time. However, most existing demand response strategies are based on static electricity carbon factor assumptions, fail to flexibly adjust the response strategy, and ignore the reality of the dynamic changes of the electricity carbon factor.

[0009] 3. It is impossible to minimize costs and carbon emissions while ensuring the constraints of user load curves: In demand response practice, users' electricity demand has a certain rigidity and cannot be adjusted freely relying entirely on electricity prices or carbon factors. For example, industrial and commercial users usually have stricter production plans, and their load curves are relatively stable, making it difficult to significantly change their electricity consumption behavior. In addition, some users have higher requirements for comfort, such as residents' air-conditioning loads, which have certain temperature control requirements. Therefore, in demand response, the constraints of user load curves make it difficult for existing strategies to effectively balance costs and carbon emissions, especially in scenarios where the carbon factor changes greatly, the model's adaptability is poor.

[0010] The existing demand response technology has the following main problems:

[0011] 1. Lack of consideration of carbon emission factors: In the context of low-carbon transformation of the power system, it is no longer sufficient to rely solely on electricity prices to stimulate demand response. Traditional demand response scenario models mainly consider changes in electricity prices. Users choose to use electricity during periods with lower electricity prices based on the time-of-use electricity price curve to reduce electricity expenses. Although this model has achieved the effect of optimizing electricity costs to a certain extent, in the context of increasingly clear and strict low-carbon development goals, simply considering electricity prices ignores the user's carbon emission costs and does not fully incentivize users to reduce electricity consumption during carbon-intensive periods or switch to low-carbon periods. Therefore, carbon emission factors need to be considered as a key factor in demand response strategies to further promote the low-carbon transformation of demand response.

[0012] 2. Multi-scenario modeling that does not cover the characteristics of different seasons and time periods: In demand response optimization, the failure to cover multi-scenario modeling with characteristics of different seasons and time periods limits the flexibility and effectiveness of the response model in practical applications. Load characteristics fluctuate significantly with changes in seasons and time periods. For example, air conditioning and heating demand is higher in summer and winter, while electricity load is relatively low in spring and autumn. In addition, different peak and valley periods, electricity price curves, and user behavior habits also have different effects on load response. Therefore, in order to more accurately control costs and carbon emissions, the demand response scenario model needs to be able to adapt to multiple scenarios and flexibly adjust the response strategy. Load characteristics change with the seasons. In the context of low-carbon transformation, it has become a key step for the demand response scenario model to introduce multi-scenario modeling to cover the needs of different seasons and time periods. By flexibly adapting to load characteristics, peak and valley periods, electricity price curves, and changes in user behavior, multi-scenario modeling provides strong support for the economy and low carbon nature of the power grid system and promotes the sustainable development of the power industry.

[0013] 3. Ignoring the dynamics of grid command response: In demand response practice, the grid will issue power control commands in different time periods to cope with load fluctuations or ensure the stability of the grid. However, existing demand response scenario models usually fail to fully consider the dynamics of these grid commands, and lack real-time analysis and flexible adjustment of changes in electricity consumption before and after the response. This limitation of ignoring dynamic regulation makes it difficult for demand response strategies to effectively meet the needs of real-time grid regulation in practical applications, which may lead to delayed response or unstable grid operation. The demand response scenario model needs to flexibly adjust load demand before and after the response, establish a real-time feedback and optimization mechanism, and perform hierarchical regulation during the load recovery process. These improvements will greatly improve the adaptability and flexibility of the demand response scenario model, not only meeting the real-time regulation needs of the grid, but also providing strong support for the low-carbon development of the power system.

[0014] In response to the above problems, the present invention is a method for generating a user multi-objective demand response strategy based on a dynamic electricity-carbon factor, which aims to achieve multi-objective optimization of power system load balancing, cost reduction and carbon emission reduction.

[0015] The above information is presented as background information only to assist with understanding the present disclosure and no determination or admission is made as to whether any of the above may be used as prior art with respect to the present disclosure. Summary of the invention

[0016] The present invention provides a method for generating a user multi-objective demand response strategy based on a dynamic electricity-carbon factor, which can achieve multi-objective optimization of power system load balancing, cost reduction and carbon emission reduction.

[0017] To achieve the above object, the present invention provides a method for generating a user multi-objective demand response strategy based on a dynamic electricity carbon factor, comprising:

[0018] S10: Seasonal load response modeling: Establish several different demand response scenario models based on the user load characteristics in different seasons;

[0019] S20: constructing constraint conditions for each of the demand response scenario models, and introducing a corresponding penalty function for each of the constraint conditions;

[0020] S30: Integrate the multi-objective sparrow optimization algorithm into the demand response scenario model to optimize the demand response scenario model.

[0021] Optionally, the S10 includes:

[0022] Without considering the power grid power command response, and the user's overall power consumption does not change before and after the response, the demand response scenario model of scenario 1 is constructed;

[0023] Without considering the power grid power command response, and the user's overall power consumption may change before and after the response, the demand response scenario model of scenario 2 is constructed;

[0024] Considering the power command response of the power grid, and the overall power consumption of the user does not change before and after the response, the demand response scenario model of scenario 3 is constructed;

[0025] Considering the power command response of the power grid and the fact that the user's overall power consumption may change before and after the response, a demand response scenario model for scenario 4 is constructed.

[0026] Optionally, the demand response scenario model for scenario 1 is:

[0027]

[0028] in: is the power consumption after the user load demand response in the corresponding period t, is the original power consumption of the user load in period t; is the electricity price in period t; is the user load switch control strategy corresponding to time period t; is the carbon price in period t; is the value of the dynamic electric carbon factor in period t; is the maximum reduction ratio of the park load in period t; It represents the maximum power consumption of the user load; T is the number of power consumption periods.

[0029] Optionally, the demand response scenario model for scenario 2 is:

[0030]

[0031] in: is the price penalty coefficient, and its unit is the same as the unit of electricity price; Part I Represents the total electricity cost; the second part It represents the total load reduction cost.

[0032] Optionally, the demand response scenario model for scenario 3 is:

[0033]

[0034] Where: T C a set of response time periods specified for the power grid; Indicates the power consumption instruction sent by the power grid.

[0035] Optionally, the demand response scenario model for scenario 4 is:

[0036]

[0037] Optionally, the constraints include power constraints, load balance constraints, carbon emission constraints, electricity cost constraints, and switch state constraints.

[0038] Optionally, the S20 includes:

[0039] Constraint 1: Power Constraint

[0040]

[0041]

[0042] Constraint 2: Load balancing constraint

[0043]

[0044]

[0045] Constraint 3: Carbon emission constraints

[0046]

[0047]

[0048] Constraint 4: Electricity cost constraint

[0049]

[0050]

[0051] Constraint 5: Switch state constraint

[0052]

[0053] in, Indicates that the total load demand is met at each moment, and Indicates that carbon emissions and electricity costs do not exceed the maximum allowed values, - It represents the penalty coefficient corresponding to each constraint condition in turn; the switch state is 0 for off and 1 for on.

[0054] Optionally, the S30 includes:

[0055] S301: Population initialization

[0056] Initialize a set of solutions, define each solution as a vector containing the user's start state and power; for each sparrow , define the states it contains as:

[0057]

[0058] S302: Fitness evaluation

[0059] S3021: In each iteration, each individual Calculate the fitness function value; the fitness function consists of two objective functions , The composition is as follows:

[0060]

[0061]

[0062] S3022: Calculate each solution The objective function value is to minimize the objective function :

[0063]

[0064] S303: Selecting a Leader

[0065] To select the leader, the concept of Pareto optimal solution is used; for two solutions and ,definition:

[0066] if ,but Better than ;

[0067] if Better than , then is regarded as a non-inferior solution; through this method, a set of Pareto optimal frontier solutions is found, which is called the leader;

[0068] S304: Location Update

[0069] In the sparrow optimization algorithm, the sparrow will update its position according to the position of the leader and other solutions; the specific update rules are as follows:

[0070]

[0071] In the formula, represents the solution of the i-th sparrow in the t+1 generation, represents the leader solution in the current iteration (i.e., one of the Pareto optimal solutions), is another randomly selected solution to increase the diversity of solutions, r1 and r2 are random numbers in the interval [0,1], which control the degree to which the sparrow learns from the leader solution and the random solution respectively;

[0072] S305: Iteration and Convergence

[0073] After multiple iterations, the population will gradually converge to the Pareto optimal solution, and the optimization of the demand response scenario model can be completed. At this time, the demand response scenario model can be used to automatically dispatch the power system to achieve multi-objective optimization of power system load balance, cost reduction and carbon emission reduction; set the maximum number of iterations T max As the convergence criterion, the final solution is in the form of: .

[0074] Compared with the prior art, the present invention has the following beneficial effects: providing a method for generating a user multi-objective demand response strategy based on a dynamic electricity carbon factor,

[0075] 1. Comprehensive consideration of dynamic electricity-carbon factor: The present invention adopts dynamic electricity-carbon factor, and considers the influence of time-of-use electricity price and dynamic electricity-carbon factor in demand response optimization, so that the generated demand response strategy can reduce both electricity cost and carbon emission. Different from the prior art, the dynamic electricity-carbon factor can fluctuate over time and more truly reflect the carbon emission intensity in different time periods.

[0076] 2. User modeling and constraints based on load curves: The present invention uses user load curves as optimization constraints to ensure that the optimized demand response strategy meets the actual power demand of users. This user modeling method based on load curves enhances the applicability of demand response strategies.

[0077] 3. Application of multi-objective optimization algorithm The present invention uses a multi-objective optimization algorithm to generate and optimize the demand response strategy. The generated strategy can minimize the user's electricity cost and carbon emissions under the combined influence of time-of-use electricity prices and electricity carbon factors. This patent intends to protect the application of this specific multi-objective optimization algorithm under the framework of dynamic electricity carbon factors and time-of-use electricity prices.

[0078] The present invention has other features and advantages, which will be apparent from or will be described in detail in the accompanying drawings and the following detailed description incorporated herein, which together serve to explain the specific principles of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0080] Figure 1 is a flow chart of a method for generating a user multi-objective demand response strategy based on a dynamic electric carbon factor provided in an embodiment of the present invention;

[0081] Figure 2 It is an algorithm flow chart of the sparrow optimization algorithm provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0082] In order to explain in detail the possible application scenarios, technical principles, specific schemes that can be implemented, and the purposes and effects that can be achieved, the following is a detailed description of the specific embodiments listed in conjunction with the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0083] Reference to "embodiment" herein means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present application. The term "embodiment" appearing in various places in the specification does not necessarily refer to the same embodiment, nor does it particularly limit its independence or association with other embodiments. In principle, in the present application, as long as there is no technical contradiction or conflict, the various technical features mentioned in the embodiments can be combined in any way to form a corresponding implementable technical solution.

[0084] Unless otherwise defined, the technical terms used in this document have the same meanings as those generally understood by those skilled in the art to which this application belongs; the use of relevant terms in this document is only for describing specific embodiments and is not intended to limit this application.

[0085] In the description of this application, the term "and / or" is an expression used to describe the logical relationship between objects, indicating that three relationships may exist, for example, A and / or B, which means: A exists, B exists, and A and B exist at the same time. In addition, the character " / " in this article generally indicates that the objects before and after are in an "or" logical relationship.

[0086] In the present application, terms such as “first” and “second” are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship of quantity, priority or sequence between these entities or operations.

[0087] Without further limitations, in this application, the words "include", "comprises", "has" or other similar expressions used in the sentences are intended to cover non-exclusive inclusion. These expressions do not exclude the presence of additional elements in the process, method or product including the elements, so that the process, method or product including a series of elements may include not only those limited elements, but also other elements not explicitly listed, or also include elements inherent to such process, method or product.

[0088] Similar to the understanding in the Examination Guidelines, in this application, expressions such as "greater than", "less than", "exceed" and the like are understood to exclude the number itself; expressions such as "above", "below", "within" and the like are understood to include the number itself. In addition, in the description of the embodiments of this application, "multiple" means more than two (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups", "multiple times", etc., unless otherwise clearly and specifically limited.

[0089] In the description of the embodiments of the present application, space-related expressions used, such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "vertical", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicate the orientation or position relationship based on the orientation or position relationship shown in the specific embodiments or drawings, and are only for the convenience of describing the specific embodiments of the present application or facilitating the reader's understanding, and do not indicate or imply that the referred device or component must have a specific position, a specific orientation, or be constructed or operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0090] Unless otherwise expressly specified or limited, in the description of the embodiments of the present application, the terms such as "install", "connect", "connect", "fix", "set", etc. used should be understood in a broad sense. For example, the "connection" can be a fixed connection, a detachable connection, or an integrated setting; it can be a mechanical connection, an electrical connection, or a communication connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. For technicians in the technical field to which the present application belongs, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0091] Please refer to the attached Figure 1 , is a flow chart of a method for generating a user multi-objective demand response strategy based on a dynamic electricity-carbon factor provided by an embodiment of the present invention. The method is applicable to scenarios where a demand response strategy is required to optimize power system operation, reduce costs, and reduce carbon emissions. The method is executed by a demand response strategy generation system, which can be implemented by software and / or hardware. The method specifically includes the following steps:

[0092] S10: Seasonal load response modeling: Establish several different demand response scenario models based on the user load characteristics in different seasons;

[0093] In each season, the operational requirements of user load automatic demand response can be divided into the following four scenarios.

[0094] Scenario 1: The power grid power command response is not considered, and the user's overall power consumption does not change before and after the response. The demand response scenario model for this situation can be described as follows:

[0095] (1)

[0096] in: is the power consumption after the user load demand response in the corresponding period t, is the original power consumption of the user load in period t; is the electricity price in period t; is the user load switch control strategy corresponding to time period t; is the carbon price in period t; is the value of the dynamic electric carbon factor in period t; is the maximum reduction ratio of the park load in period t; It represents the maximum power consumption of the user load; T is the number of power consumption periods.

[0097] The response power consumption curve of the final output user load; The final output of the carbon emission curve of the user load response based on the dynamic carbon factor (in the following three scenarios , The meaning is the same and will not be explained later)

[0098] Scenario 2: The power grid power command response is not considered, and the user's overall power consumption may change before and after the response. The demand response scenario model for this situation can be described as follows:

[0099] (2)

[0100] in: is the price penalty coefficient, and its unit is the same as the unit of electricity price; Part I Represents the total electricity cost; the second part It represents the total load reduction cost.

[0101] Scenario 3: Considering the power grid power command response, and the user's overall power consumption does not change before and after the response. The demand response scenario model of this situation can be described as follows:

[0102] (3)

[0103] Where: T C A set of response periods specified for the power grid, such as a set of peak period periods; Indicates the power consumption instruction sent by the power grid.

[0104] Scenario 4: Considering the power grid power command response, the user's overall power consumption may change before and after the response. The demand response scenario model for this situation can be described as follows:

[0105] (4)

[0106] S20: constructing constraint conditions for each of the demand response scenario models, and introducing a corresponding penalty function for each of the constraint conditions;

[0107] Constraint 1: Power Constraint

[0108] (5)

[0109] (6)

[0110] Constraint 2: Load balancing constraint

[0111] (7)

[0112] (8)

[0113] Constraint 3: Carbon emission constraints

[0114] (9)

[0115] (10)

[0116] Constraint 4: Electricity cost constraint

[0117] (11)

[0118] (12)

[0119] Constraint 5: Switch state constraint

[0120] (13)

[0121] in, Indicates that the total load demand is met at each moment, and Indicates that carbon emissions and electricity costs do not exceed the maximum allowed values, - It represents the penalty coefficient corresponding to each constraint condition in turn; the switch state is 0 for off and 1 for on.

[0122] S30: Figure 2 The multi-objective sparrow optimization algorithm shown is integrated into the demand response scenario model to optimize the demand response scenario model;

[0123] S301: Population initialization

[0124] Initialize a set of solutions, and define each solution as a vector containing the user's start state and power. , define the states it contains as:

[0125] (14)

[0126] S302: Fitness evaluation

[0127] S3021: In each iteration, each individual Calculate the fitness function value. The fitness function consists of two objective functions: , The composition is as follows:

[0128] (15)

[0129] (16)

[0130] S3022: Calculate each solution The objective function value is to minimize the objective function :

[0131] (17)

[0132] S303: Selecting a Leader

[0133] To select the leader, the Pareto optimal solution concept is used. and ,definition:

[0134] if (18),

[0135] but Better than ,if Better than , then By this method, a set of Pareto optimal frontier solutions can be found, which are called leaders.

[0136] S304: Location Update

[0137] In the sparrow optimization algorithm, the sparrow will update its position according to the position of the leader and other solutions. The specific update rules are as follows:

[0138] (19)

[0139] In the formula, represents the solution of the i-th sparrow in the t+1th generation, represents the leader solution in the current iteration (i.e., one of the Pareto optimal solutions), is another randomly selected solution to increase the diversity of solutions. r1 and r2 are random numbers in the interval [0,1], which control the degree to which the sparrow learns from the leader solution and the random solution, respectively.

[0140] S305: Iteration and Convergence

[0141] After multiple iterations, the population will gradually converge to the Pareto optimal solution, and the optimization of the demand response scenario model can be completed. At this time, the demand response scenario model can be used to automatically dispatch the power system to achieve multi-objective optimization of power system load balance, cost reduction and carbon emission reduction. Set the maximum number of iterations T max As the convergence criterion, the final solution is in the form of:

[0142] (20)

[0143] When the multi-objective optimization algorithm reaches the Pareto optimal solution, it can be considered that the demand response scenario model has been optimized through these optimal solutions. The following is a detailed explanation of this process:

[0144] (1) Demand response scenario model optimization:

[0145] The demand response scenario model is a simulation tool that can predict users' electricity consumption behavior and the response of the power system based on different electricity market demand and supply conditions.

[0146] By applying a multi-objective optimization algorithm, we find a set of optimal solutions (Pareto optimal solution set) that represent the best strategy combination when considering multiple objectives such as load balancing, cost reduction, and carbon emission reduction.

[0147] (2) Complete optimization:

[0148] When the algorithm converges to the Pareto optimal solution, it means that we have found a series of strategies that can balance multiple objectives in a given demand response scenario.

[0149] These strategies take into account factors such as electricity price changes, user demand, and grid conditions, so they are optimized results for specific demand response scenarios.

[0150] (3) Model application and automatic scheduling:

[0151] Once the optimal solutions are obtained, they can be integrated into demand response scenario models to guide actual power system dispatch.

[0152] By implementing these optimal strategies, the power system can be automatically dispatched to achieve the following goals:

[0153] Load balancing: By adjusting user electricity consumption behavior, reducing the load during peak hours and increasing electricity consumption during off-peak hours, the grid load is balanced.

[0154] Cost reduction: Utilize the time-of-use electricity price mechanism to reduce users' electricity costs by increasing electricity consumption when electricity prices are low and reducing electricity consumption when electricity prices are high.

[0155] Carbon emission reduction: By optimizing electricity consumption strategies, electricity consumption during high carbon emission periods can be reduced, thereby reducing overall carbon emissions.

[0156] In summary, when the multi-objective optimization algorithm completes its task and reaches the Pareto optimal solution, these solutions can be directly used in the demand response scenario model to achieve multi-objective optimization of the power system. These optimized models and strategies can be used in actual power system dispatch to achieve the goals of load balancing, cost reduction and carbon emission reduction.

[0157] The method for generating a user multi-objective demand response strategy based on a dynamic electricity-carbon factor provided in this embodiment can achieve multi-objective optimization of power system load balancing, cost reduction, and carbon emission reduction. Specifically:

[0158] (1) Dynamic electricity carbon factor and time-of-use electricity price framework:

[0159] Dynamic Electricity Carbon Factor: This scheme takes into account the changes in the carbon emission intensity of electricity generation over time and grid conditions. By dynamically adjusting the electricity carbon factor, the environmental impact of electricity consumption can be more accurately reflected, thereby giving priority to low-carbon electricity resources during the optimization process.

[0160] Time-of-use electricity price: The electricity price changes according to the supply and demand situation in the time period. By encouraging electricity consumption when the electricity price is low and reducing electricity consumption when the electricity price is high, it can effectively guide user behavior and reduce the peak load of the power system.

[0161] (2) Multi-objective optimization algorithm:

[0162] Multi-objective optimization: This solution uses a multi-objective optimization algorithm to find the best balance between multiple conflicting objectives, for example, reducing carbon emissions while reducing costs, rather than optimizing a single objective.

[0163] Sparrow Optimization Algorithm: This is an optimization algorithm based on swarm intelligence, which simulates the foraging behavior and anti-predation behavior of sparrows and can find high-quality solutions in complex search spaces.

[0164] (3) User demand response strategy:

[0165] Demand response: By incentivizing users to reduce electricity consumption during peak hours or increase electricity consumption during off-peak hours, it can effectively balance the load on the power grid.

[0166] Strategy generation: The scheme generates multi-objective demand response strategies based on user needs. These strategies take into account user comfort, cost sensitivity, and environmental awareness, allowing users to participate in demand response without sacrificing too much convenience.

[0167] (4) Constraints and penalty functions:

[0168] Constraints: The operational constraints of the power system, such as grid stability and equipment capacity, are taken into account in the scheme to ensure that the generated strategy is feasible.

[0169] Penalty function: For solutions that do not meet the constraints, the fitness is reduced through the penalty function, thereby guiding the algorithm to search for the optimal solution that meets all constraints.

[0170] In summary, the reason why the scheme in the briefing document can achieve multi-objective optimization is that it comprehensively considers the dynamic characteristics of the power system, the adjustability of user behavior, and the advancement of the optimization algorithm. Through these means, the scheme can effectively reduce costs and carbon emissions while ensuring the stable operation of the power system and achieve optimal resource allocation.

[0171] The method for generating a user multi-objective demand response strategy based on a dynamic electricity carbon factor provided in this embodiment has the following advantages:

[0172] 1. Comprehensive consideration of dynamic electricity-carbon factor: The present invention adopts dynamic electricity-carbon factor, and considers the influence of time-of-use electricity price and dynamic electricity-carbon factor in demand response optimization, so that the generated demand response strategy can reduce both electricity cost and carbon emission. Different from the prior art, the dynamic electricity-carbon factor can fluctuate over time and more truly reflect the carbon emission intensity in different time periods.

[0173] 2. User modeling and constraints based on load curves: The present invention uses user load curves as optimization constraints to ensure that the optimized demand response strategy meets the actual power demand of users. This user modeling method based on load curves enhances the applicability of demand response strategies.

[0174] 3. Application of multi-objective optimization algorithm The present invention uses a multi-objective optimization algorithm to generate and optimize the demand response strategy. The generated strategy can minimize the user's electricity cost and carbon emissions under the combined influence of time-of-use electricity prices and electricity carbon factors. This patent intends to protect the application of this specific multi-objective optimization algorithm under the framework of dynamic electricity carbon factors and time-of-use electricity prices.

[0175] Finally, it should be noted that although the above embodiments have been described in the specification and drawings of this application, this does not limit the scope of patent protection of this application. All technical solutions generated by replacing or modifying equivalent structures or equivalent processes based on the essential concept of this application using the contents recorded in the specification and drawings of this application, as well as directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, are included in the scope of patent protection of this application.

Claims

1. A method for generating a user multi-objective demand response strategy based on a dynamic electricity carbon factor, characterized in that: include: S10: Seasonal load response modeling: Establish several different demand response scenario models based on the user load characteristics in different seasons; S20: constructing constraint conditions for each of the demand response scenario models, and introducing a corresponding penalty function for each of the constraint conditions; S30: Integrate the multi-objective sparrow optimization algorithm into the demand response scenario model to optimize the demand response scenario model.

2. The method for generating a user multi-objective demand response strategy based on a dynamic electricity-carbon factor according to claim 1 is characterized in that: The S10 includes: Without considering the power grid power command response, and the user's overall power consumption does not change before and after the response, the demand response scenario model of scenario 1 is constructed; Without considering the power grid power command response, and the user's overall power consumption may change before and after the response, the demand response scenario model of scenario 2 is constructed; Considering the power command response of the power grid, and the overall power consumption of the user does not change before and after the response, the demand response scenario model of scenario 3 is constructed; Considering the power command response of the power grid and the fact that the user's overall power consumption may change before and after the response, a demand response scenario model for scenario 4 is constructed.

3. The method for generating a user multi-objective demand response strategy based on a dynamic electricity carbon factor according to claim 2 is characterized in that: The demand response scenario model for scenario 1 is: ； in: is the power consumption after the user load demand response in the corresponding period t, is the original power consumption of the user load in period t; is the electricity price in period t; is the user load switch control strategy corresponding to time period t; is the carbon price in period t; is the value of the dynamic electric carbon factor in period t; is the maximum reduction ratio of the park load in period t; It represents the maximum power consumption of the user load; T is the number of power consumption periods.

4. The method for generating a user multi-objective demand response strategy based on a dynamic electricity-carbon factor according to claim 3 is characterized in that: The demand response scenario model for scenario 2 is: ； in: is the price penalty coefficient, and its unit is the same as the unit of electricity price; Part I Represents the total electricity cost; the second part It represents the total load reduction cost.

5. The method for generating a user multi-objective demand response strategy based on a dynamic electricity-carbon factor according to claim 4 is characterized in that: The demand response scenario model for scenario 3 is: ； Where: T C a set of response time periods specified for the power grid; Indicates the power consumption instruction sent by the power grid.

6. The method for generating a user multi-objective demand response strategy based on a dynamic electricity-carbon factor according to claim 5 is characterized in that: The demand response scenario model for scenario 4 is: 。 7. The method for generating a user multi-objective demand response strategy based on a dynamic electricity carbon factor according to claim 6 is characterized in that: The constraints include power constraints, load balance constraints, carbon emission constraints, electricity cost constraints, and switch state constraints.

8. The method for generating a user multi-objective demand response strategy based on a dynamic electricity-carbon factor according to claim 7 is characterized in that: The S20 includes: Constraint 1: Power Constraint ； )； Constraint 2: Load balancing constraint ； ； Constraint 3: Carbon emission constraints ； ； Constraint 4: Electricity cost constraint ； ； Constraint 5: Switch state constraint ； in, Indicates that the total load demand is met at each moment, and Indicates that carbon emissions and electricity costs do not exceed the maximum allowed values, - It represents the penalty coefficient corresponding to each constraint condition in turn; the switch state is 0 for off and 1 for on.

9. The method for generating a user multi-objective demand response strategy based on a dynamic electricity-carbon factor according to claim 8 is characterized in that: The S30 includes: S301: Population initialization Initialize a set of solutions, define each solution as a vector containing the user's start state and power; for each sparrow , define the states it contains as: ； S302: Fitness evaluation S3021: In each iteration, each individual Calculate the fitness function value; the fitness function consists of two objective functions , The composition is as follows: ； ； S3022: Calculate each solution The objective function value is to minimize the objective function : ； S303: Selecting a Leader To select the leader, the concept of Pareto optimal solution is used; for two solutions and ,definition: if ,but Better than ; if Better than , then is considered as a non-inferior solution; through this method, a set of Pareto optimal frontier solutions is found, which is called the leader; S304: Location Update In the sparrow optimization algorithm, the sparrow will update its position according to the position of the leader and other solutions; the specific update rules are as follows: ； In the formula, represents the solution of the i-th sparrow in the t+1 generation, represents the leader solution in the current iteration (i.e., one of the Pareto optimal solutions), is another randomly selected solution to increase the diversity of solutions, r1 and r2 are random numbers in the interval [0,1], which control the degree to which the sparrow learns from the leader solution and the random solution respectively; S305: Iteration and Convergence After multiple iterations, the population will gradually converge to the Pareto optimal solution, and the optimization of the demand response scenario model can be completed. At this time, the demand response scenario model can be used to automatically dispatch the power system to achieve multi-objective optimization of power system load balance, cost reduction and carbon emission reduction; set the maximum number of iterations T max As the convergence criterion, the final solution is in the form of: .

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