Considering the complementary load scheduling methods, systems, and media of proactive peak shaving and demand response.

Abstract

This application provides a method, system, and medium for load dispatching that considers both proactive peak shaving and demand response complementarity. The method includes the following specific steps: S1: Analyzing the peak shaving compensation and allocation methods of various generating units at the power generation level, considering the proactive constraints of peak shaving for various units, and establishing a model to incentivize various units to participate in peak shaving through peak shaving revenue; S2: Adopting price-based demand response on the load side to establish a model that guides users to actively participate in load adjustment; S3: Then, using the minimization of system operating costs and the minimization of wind and solar curtailment as optimization objectives, constructing a day-ahead optimal dispatching model for the new energy system that considers the proactive peak shaving of thermal power and demand response on the load side using the simulated annealing-particle swarm optimization algorithm; S4: Finally, conducting multi-scenario analysis using the improved IEEE 30-bus system as an example and verifying its feasibility. This application effectively promotes the consumption of renewable energy, improves the overall peak shaving efficiency of the system, and provides an effective method for coordinated dispatching decisions of multi-energy systems.

Description

Technical Field

[0001] This application relates to the field of distributed energy operation, specifically a method, system, and medium for load scheduling that considers active peak shaving and demand response complementary loads. Background Technology

[0002] In recent years, China has vigorously developed new energy power generation, promoting the transformation and development of its energy structure. At the same time, the increase in uncertain power sources such as wind and solar power has further increased the pressure on the power grid for peak shaving. The existing peak shaving model mainly relies on conventional peak shaving by thermal power units. When the load peak-valley difference is large, deep peak shaving is needed to meet the needs of new energy absorption and balance load fluctuations. Relying solely on the existing system regulation capacity is insufficient to meet the system's peak shaving requirements. Therefore, it is necessary to establish a complementary and coordinated mechanism for the operation of multiple energy sources, fully explore the flexible regulation capacity of the power system, smooth peaks and fill valleys, and improve the level of new energy absorption. Currently, most work focuses on using conventional peak shaving by thermal power and energy storage devices for peak shaving and valley filling, smoothing load curve fluctuations to achieve optimized scheduling. However, the capacity of energy storage and conventional peak shaving by thermal power have certain constraints on renewable energy absorption, limiting the application effect. Collaborative consideration between power sources and loads can further explore the system's peak shaving capacity. Summary of the Invention

[0003] The purpose of this application is to provide a method, system, and medium for load scheduling that considers active peak shaving and demand response complementarity, effectively promoting the consumption of renewable energy, improving the overall peak shaving efficiency of the system, and providing an effective method for coordinated scheduling decisions of multi-energy systems.

[0004] To achieve the above objectives, this application provides the following technical solution:

[0005] In a first aspect, embodiments of this application provide a load scheduling method that considers complementary active peak shaving and demand response, including the following specific steps:

[0006] S1: In the analysis of peak shaving compensation and allocation methods of various generating units, considering the constraints on the initiative of various generating units in peak shaving, a model is established to incentivize various generating units to participate in peak shaving through peak shaving revenue;

[0007] S2: On the load side, adopt price-based demand response to establish a model that guides users to actively participate in load adjustment.

[0008] S3: Then, with the optimization objectives of minimizing system operating costs and minimizing wind and solar curtailment, a day-ahead optimization scheduling model for new energy systems is constructed using the simulated annealing-particle swarm adaptive algorithm, taking into account the initiative of thermal power peak shaving and the demand response of the load side.

[0009] S4: Finally, we will take the improved IEEE 30-node system as an example to conduct multi-scenario analysis and verify its feasibility.

[0010] In S1, considering the proactive constraints of peak shaving for various generating units, the ultimate goal of peak shaving is to maximize the overall system benefit. g I w I pv I s These represent the number of thermal power, wind power, photovoltaic power, and energy storage participating in grid dispatch, respectively.

[0011] Firstly, regarding the peak-shaving compensation model for thermal power units, for a given scheduling time, if a thermal power unit takes measures to reduce its output for peak shaving, the resulting loss needs to be compensated. The expression is as follows:

[0012] (1)

[0013] in, This refers to the peak-shaving compensation cost for conventional thermal power units. This is the standard peak-shaving compensation price for thermal power plants; This refers to the paid peak-shaving power of conventional thermal power plants.

[0014] Total peak-shaving compensation expenditure at time t Represented as:

[0015] (2)

[0016] Peak-shaving compensation costs are shared by thermal power units, wind power, and photovoltaic power. The cost allocation for wind power and photovoltaic power is based on the proportion of total power generation on the deep peak-shaving trading day, while the cost allocation for thermal power units is based on their on-grid electricity price. The peak-shaving cost allocation model is as follows:

[0017] (3)

[0018] (4)

[0019] (5)

[0020] in, , and These represent the peak-shaving cost allocation for each conventional thermal power unit, wind farm, and photovoltaic power station at time t. Let t be the on-grid power of conventional thermal power unit i. Let t be the grid-connected power of the wind farm; For a given photovoltaic (PV) power generation at time t, if the compensation provided by non-peak-shaving entities exceeds the unit's own costs, the unit will be more motivated to participate. For wind farms and PV power plants, deep peak shaving can effectively increase their market share. As long as their shared costs are lower than the revenue from their increased power generation, wind farms and PV power plants are willing to actively participate in peak-shaving transactions.

[0021] The active constraints on peak shaving for various generating units are as follows:

[0022] (1) Peak-shaving revenue of thermal power units:

[0023] (6)

[0024] (7)

[0025] in, For the peak-shaving cost of the generating unit; and These are the unit loss costs and coal costs during peak shaving periods, respectively. The coal-saving benefits resulting from reduced power generation by thermal power units; N represents the reduced power generation of thermal power units due to peak shaving. T For one scheduling cycle,

[0026] (2) Wind power revenue:

[0027] (8)

[0028] in, The benchmark on-grid tariff for wind power, This indicates a punishment for abandoning the wind; This represents the amount of wind power curtailed at wind farm j during time period t.

[0029] (3) Revenue from thermal power:

[0030] (9)

[0031] in, The benchmark feed-in tariff for photovoltaic power. This indicates a punishment for abandoning light; This represents the amount of wind power curtailed at photovoltaic power station k during time period t.

[0032] When wind power revenue Less than 0 or photovoltaic power plant revenue When the value is less than 0, it means that the cost of wind farms or photovoltaic power plants participating in peak shaving has exceeded the benefits they receive from deep peak shaving. In this case, wind farms or photovoltaic power plants will withdraw from peak shaving to ensure their profitability. When the peak shaving revenue of thermal power units... When the value is less than 0, it means that the peak-shaving cost of thermal power units participating in deep peak shaving is greater than the peak-shaving compensation, and thermal power units will be the first to withdraw from peak shaving.

[0033] In S2, the price elasticity matrix is ​​used to describe the price-based demand response model. From an economic perspective, relative changes in electricity prices will cause corresponding changes in customer electricity demand. The degree of this change is represented by the price elasticity coefficient ε.

[0034] (10)

[0035] Where Δq and Δp are the changes in electricity consumption q and electricity price p per unit time, respectively.

[0036] To balance system economics and renewable energy consumption levels, and to incorporate both system economics and wind / solar curtailment into the objective function, the two objective functions are scalarized. A linear weighting method is then used to transform the two functions with different dimensions, resulting in the following objective function:

[0037] (11)

[0038] Where w1 and w2 represent the weighting coefficients of economic efficiency and wind / solar curtailment, respectively, and w1 + w2 = 1; f1 represents the operating cost of the proposed model; d0 is the optimization result of the original scheduling model with the objective function of minimizing operating cost; f2 is the amount of wind / solar curtailment in the proposed model; θ0 is the amount of wind / solar curtailment in the original scheduling model with the objective function of minimizing wind / solar curtailment.

[0039] The system operating cost C0 consists of the operating cost of thermal power units, the operating cost of energy storage systems, the cost of wind and solar curtailment penalties, and the reserve cost.

[0040] (12)

[0041] (13)

[0042] (14)

[0043] (15)

[0044] (16)

[0045] Where C is the operating cost of the thermal power unit, C sc For the operating cost of the energy storage system; C c The cost of penalties for curtailing wind and solar power; C b For system backup costs; c soc P represents the operating cost coefficient of the energy storage system. c, t and P d, tThe charging and discharging power of the energy storage system; and The cost of penalizing the abandonment of wind and solar power; , The predicted output of wind power and photovoltaic power generation at time t are respectively; k b The price to provide a backup for the system; P L, t For system load power,

[0046] The operating cost of thermal power units includes the following components:

[0047] (1) Coal consumption cost of thermal power units

[0048] (17)

[0049] Among them, a i b i and c i P is the consumption coefficient of thermal power unit i; i, t For the output of thermal power units,

[0050] (2) Loss cost of deep peak shaving units

[0051] (18)

[0052] Where α is the thermal power unit operation impact coefficient; C unit , i N represents the purchase cost of the i-th thermal power unit; f This represents the number of rotor cracking cycles determined by the rotor low-cycle fatigue curve.

[0053] (3) Fuel cost of deep peak shaving units

[0054] Combustion-supporting fuels incur additional peak-shaving fuel costs, which are as follows:

[0055] (19)

[0056] Among them, Q o , t R represents the amount of fuel oil input during the deep peak-shaving phase of a thermal power unit at time t; o For the current season's oil price,

[0057] In summary, the operating cost of thermal power units can be divided into segments based on their operating conditions as follows:

[0058] (20)

[0059] in, and These represent the theoretical minimum and maximum output of the thermal power unit, respectively; P b For the unit's oil depth, peak shaving and fuel stability load limit; P aThe limit for peak shaving and fuel stability load of the unit without oil.

[0060] (4) Renewable energy absorption capacity

[0061] Renewable energy absorption capacity is represented by the total amount of wind and solar curtailment (f2) during the dispatch cycle. The greater the amount of wind and solar curtailment, the weaker the renewable energy absorption capacity. The formula is as follows:

[0062] (twenty one)

[0063] in, This represents the amount of solar power curtailed during time period t. This represents the amount of wind power curtailed by the wind farm during time period t. Duration of each time period

[0064] The constraints are as follows:

[0065] (1) Demand response constraints:

[0066] (twenty two)

[0067] (twenty three)

[0068] in, To take into account the load value after demand response. The minimum value for satisfaction with electricity costs. This is the original load value; and These are the electricity prices before and after considering demand response.

[0069] (twenty four)

[0070] (2) Power supply constraints

[0071] Conventional peak-shaving units:

[0072] (26)

[0073] Deep peak shaving units:

[0074] (27)

[0075] Thermal power unit ramping constraints:

[0076] (28)

[0077] in, and These are the maximum and minimum technical outputs of the thermal power unit, respectively. This is the minimum output of the peak-shaving unit; and They are the maximum down-ramp rate and the maximum up-ramp rate of the thermal power unit respectively.

[0078] Wind power constraint:

[0079] (29)

[0080] Among them, is the maximum output of the wind farm at time t.

[0081] Photovoltaic power plant output constraint:

[0082] (30)

[0083] Among them, is the maximum output of the photovoltaic power plant at time t.

[0084] (31)

[0085] Among them, P b is the system spinning reserve capacity.

[0086] In S3, the improved simulated annealing algorithm is designed as follows:

[0087] The principle of the traditional simulated annealing algorithm is as follows: Random walk in the search interval, that is, randomly select points, and then use the Metropolis sampling criterion to make the random walk gradually converge to the local optimal solution. Temperature is an important control parameter in the Metropolis algorithm. The size of this parameter controls the speed of the random process moving towards the local or global optimal solution. The Metropolis algorithm is: When the system changes from an energy state E1 to another state E2, the corresponding energy changes from to, and its probability is:

[0088] (32)

[0089] If E2 < E1, the system accepts this state; otherwise, accept or discard this state with a random state. The probability that state 2E2 is accepted is:

[0090] (33)

[0091] After a certain number of iterations like this, the system will gradually tend to a stable distribution state. When sampling intensively, if it goes down in the new state, accept the local optimum; if it goes up for global search, accept it with a certain probability.

[0092] (1) Adaptive temperature control mechanism

[0093] An adaptive temperature control mechanism is proposed, which dynamically adjusts the temperature decrease rate based on the quality of the current solution. The temperature decrease rate is determined by the degree of improvement of the solution: if the solution found in the current iteration is significantly improved compared to the solution in previous iterations, the cooling rate is appropriately slowed down to further explore adjacent regions; if the improvement of the solution is small or nonexistent, the cooling process is accelerated to speed up the convergence.

[0094] (34)

[0095] T1 is the updated temperature, and T is the current temperature. Here, Δf is a control parameter, representing the difference in the objective function between the current solution and the previous solution.

[0096] (2) Multi-neighborhood dynamic search strategy

[0097] A multi-neighborhood dynamic search strategy is introduced, which expands the search range by switching between different neighborhoods. Specifically, in each iteration, different neighborhood ranges are dynamically selected based on the current state of the solution. In the initial stage, a large neighborhood is used for extensive searching; as the algorithm gradually approaches the optimal solution, it switches to a smaller neighborhood to improve the accuracy of the local search.

[0098] The mathematical expression for neighborhood selection is shown in formula (35):

[0099] E2=E1+εN(0,σ) t (35)

[0100] E2 is the new solution, ε is the step size, and N is the value with a mean of 0 and a standard deviation of σ. t For a normally distributed random variable, σ increases with the number of iterations. t Gradually reduce the size to ensure a more refined search later.

[0101] (3) Improved acceptance criteria

[0102] A dynamic acceptance criterion based on the current solution quality is proposed:

[0103] When the objective function value approaches the global optimum, the algorithm gradually reduces the probability of accepting inferior solutions, thereby accelerating convergence. In the initial stage, in order to maintain global search capability, a certain probability of accepting inferior solutions is reserved. The probability of acceptance is calculated as shown in formula (36):

[0104] (36)

[0105] In the formula, T is the current temperature. f is a control parameter. c f represents the objective function value of the current solution. bestThis represents the objective function value of the current optimal solution. Through this dynamic acceptance criterion, the algorithm can effectively balance global search and local convergence, improving the overall convergence speed.

[0106] Secondly, embodiments of this application provide a load scheduling system that considers proactive peak shaving and demand response complementarity. The system includes a memory and a processor. The memory includes a program for a load scheduling method that considers proactive peak shaving and demand response complementarity, and when executed by the processor, the program implements the following steps:

[0107] S1: In the analysis of peak shaving compensation and allocation methods of various generating units, considering the constraints on the initiative of various generating units in peak shaving, a model is established to incentivize various generating units to participate in peak shaving through peak shaving revenue;

[0108] S2: On the load side, adopt price-based demand response to establish a model that guides users to actively participate in load adjustment.

[0109] S3: Then, with the optimization objectives of minimizing system operating costs and minimizing wind and solar curtailment, a day-ahead optimization scheduling model for new energy systems is constructed using the simulated annealing-particle swarm adaptive algorithm, taking into account the initiative of thermal power peak shaving and the demand response of the load side.

[0110] S4: Finally, we will take the improved IEEE 30-node system as an example to conduct multi-scenario analysis and verify its feasibility.

[0111] Thirdly, embodiments of this application provide a computer-readable storage medium storing program code, which, when executed by a processor, implements the steps of the above-described method for complementary load scheduling considering proactive peak shaving and demand response.

[0112] Compared with existing technologies, the beneficial effects of this invention are as follows: Price-based demand response can guide the demand side to actively participate in load adjustment. Combining the deep peak-shaving characteristics of thermal power units, and using an improved simulated annealing algorithm, a multi-energy system optimization scheduling strategy considering peak-shaving initiative and demand response is proposed. Taking into account the interests of each participant and their initiative in participating in peak shaving, this model can adjust the distribution of benefits between the injured party and the beneficiary party, adjust the output of thermal power units under the premise of benefit, enhance the flexibility of the units, effectively promote the consumption of renewable energy, improve the overall peak-shaving efficiency of the system, and provide an effective method for multi-energy system coordinated scheduling decision-making. Attached Figure Description

[0113] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0114] Figure 1 A flowchart of a load scheduling method considering active peak shaving and demand response complementary load scheduling provided in this disclosure embodiment;

[0115] Figure 2 This is a graph showing the predicted load and renewable energy output in the embodiments of this disclosure;

[0116] Figure 3 This is a comparison chart of load curve data for Modes 1 and 2 before and after demand response implementation in the embodiments of this disclosure;

[0117] Figure 4 This is a diagram showing the deep peak shaving scheduling results of Mode 4 in this embodiment of the present disclosure. Detailed Implementation

[0118] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. It should be noted that similar reference numerals and letters in the following drawings indicate similar items; therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0119] The terms “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0120] The terms “first,” “second,” etc., are used only to distinguish one entity or operation from another, and should not be construed as indicating or implying relative importance, nor as requiring or implying any such actual relationship or order between these entities or operations.

[0121] A load scheduling method that considers the complementarity of proactive peak shaving and demand response includes:

[0122] S1: In the analysis of peak shaving compensation and allocation methods of various generating units, considering the constraints on the initiative of various generating units in peak shaving, a model is established to incentivize various generating units to participate in peak shaving through peak shaving revenue;

[0123] S2: On the load side, adopt price-based demand response to establish a model that guides users to actively participate in load adjustment.

[0124] S3: Then, with the optimization objectives of minimizing system operating costs and minimizing wind and solar curtailment, a day-ahead optimization scheduling model for new energy systems is constructed using the simulated annealing-particle swarm adaptive algorithm, taking into account the initiative of thermal power peak shaving and the demand response of the load side.

[0125] S4: Finally, we will take the improved IEEE 30-node system as an example to conduct multi-scenario analysis and verify its feasibility;

[0126] In S1, in the study of complementary coordinated optimization scheduling of multi-energy systems considering both peak-shaving initiative and demand response, the ultimate goal of peak-shaving is to maximize the overall system benefit. g I w I pv I s These represent the number of thermal power, wind power, photovoltaic power, and energy storage participating in grid dispatch, respectively.

[0127] Firstly, regarding the peak-shaving compensation model for thermal power units, for a given scheduling time, if a thermal power unit takes measures to reduce its output for peak shaving, the resulting loss needs to be compensated. The expression is as follows:

[0128] (1)

[0129] in, This refers to the peak-shaving compensation cost for conventional thermal power units. This is the standard peak-shaving compensation price for thermal power plants; This refers to the paid peak-shaving power of conventional thermal power plants.

[0130] Total peak-shaving compensation expenditure at time t Represented as:

[0131] (2)

[0132] Peak-shaving compensation costs are shared by thermal power units, wind power, and photovoltaic power. The cost allocation for wind power and photovoltaic power is based on the proportion of total power generation on the deep peak-shaving trading day, while the cost allocation for thermal power units is based on their on-grid electricity price. The peak-shaving cost allocation model is as follows:

[0133] (3)

[0134] (4)

[0135] (5)

[0136] in, , and These represent the peak-shaving cost allocation for each conventional thermal power unit, wind farm, and photovoltaic power station at time t. Let t be the on-grid power of conventional thermal power unit i. Let t be the grid-connected power of the wind farm; Let t be the grid-connected power of the photovoltaic power plant. For conventional thermal power units, if the compensation provided by non-peak-shaving entities exceeds their own costs, the units are more motivated to participate. For wind farms and photovoltaic power plants, deep peak shaving can effectively increase their market share. As long as their shared costs are lower than the revenue from their increased power generation, wind farms and photovoltaic power plants are willing to actively participate in peak-shaving transactions.

[0137] The active constraints on peak shaving for various generating units are as follows:

[0138] (5) Peak-shaving revenue of thermal power units:

[0139] (6)

[0140] (7)

[0141] in, For peak shaving costs of generating units; and These are the unit loss costs and coal costs during peak shaving periods, respectively. The coal-saving benefits resulting from reduced power generation by thermal power units; N represents the reduced power generation of thermal power units due to peak shaving. T One scheduling cycle.

[0142] (6) Wind power revenue:

[0143] (8)

[0144] in, The benchmark on-grid tariff for wind power, This indicates a punishment for abandoning the wind; This represents the amount of wind power curtailed at wind farm j during time period t.

[0145] (7) Revenue from thermal power:

[0146] (9)

[0147] in, The benchmark feed-in tariff for photovoltaic power. This indicates a punishment for abandoning light; This represents the wind curtailment power generated by photovoltaic power station k during time period t.

[0148] When wind power revenue Less than 0 or photovoltaic power plant revenue When the value is less than 0, it means that the cost of wind farms or photovoltaic power plants participating in peak shaving has exceeded the benefits they receive from deep peak shaving. In this case, wind farms or photovoltaic power plants will withdraw from peak shaving to ensure their profitability. When the peak shaving revenue of thermal power units... When the value is less than 0, it means that the peak-shaving cost of thermal power units participating in deep peak shaving is greater than the peak-shaving compensation, and thermal power units will be the first to withdraw from peak shaving.

[0149] In S2, price-based demand response is an effective measure that uses electricity prices as a signal to guide customers to change their electricity consumption behavior. When formulating day-ahead dispatch plans, power grid companies set time-of-use prices on a one-hour time scale. Here, the price elasticity matrix is ​​used to describe the price-based demand response model. From an economic perspective, relative changes in electricity prices will cause corresponding changes in customer electricity demand; the degree of this change is represented by the price elasticity coefficient ε.

[0150] (10)

[0151] Where Δq and Δp are the changes in electricity quantity q and electricity price p per unit time.

[0152] To balance system economics and renewable energy consumption levels, both system economics and wind / solar curtailment are incorporated into the objective function. Here, the two objective functions are scalarized, and a linear weighting method is used to transform the two functions with different dimensions, resulting in the following objective function:

[0153] (11)

[0154] Where w1 and w2 represent the weighting coefficients of economic efficiency and wind and solar curtailment, respectively, and w1+w2=1; f1 represents the operating cost of the proposed model; d0 is the optimization result of the original scheduling model with the objective function of minimizing operating cost; f2 is the amount of wind and solar curtailment of the proposed model; θ0 is the amount of wind and solar curtailment of the original scheduling model with the objective function of minimizing wind and solar curtailment.

[0155] The system operating cost C0 consists of the operating cost of thermal power units, the operating cost of energy storage systems, the cost of wind and solar curtailment penalties, and the reserve cost.

[0156] (12)

[0157] (13)

[0158] (14)

[0159] (15)

[0160] (16)

[0161] Where C is the operating cost of the thermal power unit, C sc For the operating cost of the energy storage system; C c The cost of penalties for curtailing wind and solar power; C b For system backup costs; c soc P represents the operating cost coefficient of the energy storage system. c, t and P d, t The charging and discharging power of the energy storage system; and The cost of penalizing the abandonment of wind and solar power; , The predicted output of wind power and photovoltaic power generation at time t are respectively; k b The price to provide a backup for the system; P L, t This represents the system load power.

[0162] The operating cost of thermal power units includes the following components:

[0163] (4) Coal consumption cost of thermal power units

[0164] (17)

[0165] Among them, a i b i and c i P is the consumption coefficient of thermal power unit i; i, t For the output of thermal power units.

[0166] (5) Loss costs of deep peak-shaving units

[0167] (18)

[0168] Where α is the thermal power unit operation impact coefficient; C unit , i N represents the purchase cost of the i-th thermal power unit; f This indicates the number of rotor cracking cycles determined by the rotor low-cycle fatigue curve.

[0169] (6) Fuel cost of deep peak shaving units

[0170] Combustion-supporting fuels incur additional peak-shaving fuel costs, which are as follows:

[0171] (19)

[0172] Among them, Q o , tR represents the amount of fuel oil input during the deep peak-shaving phase of a thermal power unit at time t; o This refers to the current season's oil price.

[0173] In summary, the operating cost of thermal power units can be divided into segments based on their operating conditions as follows:

[0174] (20)

[0175] in, and These represent the theoretical minimum and maximum output of the thermal power unit, respectively; P b For the unit's oil depth, peak shaving and fuel stability load limit; P a The limit for peak shaving and stable combustion load of the unit without oil.

[0176] (8) Renewable energy absorption capacity

[0177] Renewable energy absorption capacity is represented by the total amount of wind and solar curtailment (f2) during the dispatch cycle. The greater the amount of wind and solar curtailment, the weaker the renewable energy absorption capacity. The formula is as follows:

[0178] (twenty one)

[0179] in, This represents the amount of solar power curtailed during time period t. This represents the amount of wind power curtailed by the wind farm during time period t. The duration of each time period.

[0180] The constraints are as follows:

[0181] (1) Demand response constraints:

[0182] (twenty two)

[0183] (twenty three)

[0184] in, To take into account the load value after demand response. This represents the minimum satisfaction level with electricity costs. This is the original load value; and These are the electricity prices before and after considering demand response.

[0185] (twenty four)

[0186] (4) Power supply constraints

[0187] Conventional peak-shaving units:

[0188] (26)

[0189] Deep peak shaving units:

[0190] (27)

[0191] Thermal power unit ramping constraints:

[0192] (28)

[0193] in, and These are the maximum and minimum technical outputs of the thermal power unit, respectively. This is the minimum output of the peak-shaving unit; and These are the maximum downhill and uphill speeds of the thermal power unit, respectively.

[0194] Wind power constraints:

[0195] (29)

[0196] in, The maximum output of the wind farm is at time t.

[0197] Output constraints of photovoltaic power plants:

[0198] (30)

[0199] in, The maximum output of the photovoltaic power station is at time t.

[0200] (31)

[0201] Among them, P b Reserve capacity for system rotation.

[0202] In S3, the improved simulated annealing algorithm is designed as follows:

[0203] The traditional simulated annealing algorithm works as follows: A random walk (i.e., randomly selecting points) is performed within the search interval, and then the Metropolis sampling criterion is used to gradually converge the random walk to a local optimum. Temperature is a crucial control parameter in the Metropolis algorithm; its magnitude controls how quickly the random process moves towards a local or global optimum. The Metropolis algorithm is as follows: When the system changes from one energy state E1 to another E2, the probability of the corresponding energy changing from E1 to E2 is:

[0204] (32)

[0205] If E2 < E1, the system accepts this state; otherwise, it accepts or discards this state randomly. The probability of accepting state 2E2 is:

[0206] (33)

[0207] After a certain number of iterations, the system will gradually tend to a stable distribution state. During importance sampling, if it goes down in the new state, it is accepted (local optimum); if it goes up (global search), it is accepted with a certain probability.

[0208] (1)Adaptive temperature control mechanism

[0209] In traditional simulated annealing algorithms, temperature control usually adopts linear or exponential descent. This fixed cooling mechanism may lead to insufficient exploration in the initial search stage. This disclosure proposes an adaptive temperature control mechanism that dynamically adjusts the temperature descent rate according to the quality of the current solution. The temperature descent rate is determined by the improvement of the solution: if the solution found in the current iteration is significantly improved compared to the solution in the previous iteration, the cooling rate is appropriately slowed down to further explore the adjacent areas; if the improvement of the solution is small or there is no improvement, the cooling process is accelerated to speed up the convergence rate.

[0210] (34)

[0211] T1 is the updated temperature, T is the current temperature, is the control parameter, and Δf represents the difference in the objective function between the current solution and the previous solution.

[0212] (2)Multi-neighborhood dynamic search strategy

[0213] The search strategy of traditional simulated annealing algorithms is usually limited to a single neighborhood, which may lead to insufficient exploration of the solution space. Especially in complex resource scheduling problems, the single-neighborhood strategy often cannot fully explore the global optimal solution in the solution space. Therefore, this disclosure introduces a multi-neighborhood dynamic search strategy to expand the search scope by switching between different neighborhoods. The specific implementation method is to dynamically select different neighborhood ranges according to the current state of the solution in each iteration. A large neighborhood is used for extensive search in the initial stage; as the algorithm gradually approaches the optimal solution, it switches to a smaller neighborhood to improve the accuracy of local search.

[0214] The mathematical expression for neighborhood selection is shown in formula (35):

[0215] E2 = E1 + εN(0, σ t )(35)

[0216] E2 is the new solution, ε is the step size, and N is a normal distribution random variable with a mean of 0 and a standard deviation of σ t As the number of iterations increases, σt Gradually reduce the size to ensure a more refined search later.

[0217] (5) Improved acceptance criteria

[0218] A key mechanism of simulated annealing is accepting inferior solutions with a certain probability to avoid getting trapped in local optima. However, the acceptance criterion of traditional simulated annealing algorithms relies excessively on randomness, which may lead to the algorithm accepting too many inferior solutions early on, affecting the convergence speed. Therefore, this disclosure proposes a dynamic acceptance criterion based on the current solution quality:

[0219] As the objective function value approaches the global optimum, the algorithm gradually reduces the probability of accepting inferior solutions, thereby accelerating convergence. In the initial stage, to maintain global search capability, a certain probability of accepting inferior solutions is retained. The acceptance probability is calculated as shown in formula (36):

[0220] (36)

[0221] In the formula, T is the current temperature. f is a control parameter. c f represents the objective function value of the current solution. best This represents the objective function value of the current optimal solution. Through this dynamic acceptance criterion, the algorithm can effectively balance global search and local convergence, improving the overall convergence speed.

[0222] In S4, an improved IEEE 30-node example is used for verification, with the basic parameter settings as follows:

[0223] The system comprises 5 thermal power units, with basic unit parameters shown in Table 1; one 70 MW wind farm; and one 50 MW photovoltaic power station. This paper selects the largest 200 MW thermal power unit as the peak-shaving unit; assumes a 10% elastic load at each node in the system; the price-based demand response tariff is 400 yuan / (MW·h); and lithium iron phosphate batteries are used for energy storage, with specific parameters shown in Table 2. The benchmark on-grid tariff for thermal power is 368 yuan / (MW·h); the benchmark on-grid tariff for wind power is 570 yuan / (MW·h); the benchmark on-grid tariff for photovoltaic power is 450 yuan / (MW·h); the deep peak-shaving compensation tariff for thermal power is 470 yuan / (MW·h); and the wind and solar curtailment penalty factor is 400 yuan / (MW·h). The wind and solar output forecast and load forecast curves are shown below. Figure 2 As shown.

[0224] Table 1 Parameters of Thermal Power Units

[0225]

[0226] Table 2 Energy Storage System Parameters

[0227]

[0228] Next, the results of the joint system optimization scheduling are analyzed. To verify the effectiveness of the model in this paper, a comparative analysis is conducted based on four scheduling cases to analyze the system's economic efficiency and renewable energy consumption level under different cases. In the multi-objective optimization process, the weight coefficients w1:w2 for system operating costs and wind / solar curtailment are 0.5:0.5. The verification results of the four models are shown in Table 3.

[0229] Table 3. Optimization results of the system under four scheduling modes

[0230]

[0231] Mode 1: Conventional peak shaving of thermal power units with the optimization goal of minimizing system operating costs.

[0232] Mode 2: The optimization objective is to minimize system operating costs, taking into account demand response and conventional peak shaving of thermal power units.

[0233] Mode 3: With the goal of minimizing system operating costs, it takes into account demand response and deep peak shaving of thermal power units.

[0234] Mode 4: With the optimization objectives of minimizing system operating costs and minimizing wind and solar curtailment, it takes into account demand response and deep peak shaving of thermal power units.

[0235] From Table 3 and Figure 3 It can be seen that price-based demand response guides the shift of elastic load from the peak portion of the curve to the trough portion of the load by changing electricity prices. When renewable energy output is sufficient, the demand response used in Mode 2 reduces wind and solar curtailment by 1.47%, increasing renewable energy profits by 49,200 yuan compared to Case 1. When the proportion of wind and solar curtailment in the system decreases, the cost of wind and solar curtailment is further reduced, and the operating cost of thermal power units will also decrease. Compared to Mode 1, Mode 2 reduces the operating cost of thermal power units by 27,800 yuan, and the total system cost by 2.76%.

[0236] Comparing Modes 2 and 3, it is evident that when considering the deep peak-shaving operation of thermal power units, the unit loss cost and fuel input cost increase, but the coal consumption cost of thermal power units decreases, resulting in a total system operating cost reduction of 61,200 yuan compared to Mode 2. The reduced output of thermal power units provides space for renewable energy grid connection, reducing the system's wind and solar curtailment cost by 22.85% compared to Mode 2. Deep peak-shaving by thermal power units plays a role in promoting the consumption of wind and solar power, increasing the profits of wind farms and photovoltaic power plants.

[0237] When thermal power units are in deep peak-shaving mode, the increased peak-shaving depth leads to additional fuel costs during the fuel oil stage, and the unit's peak-shaving cost will have a major impact on the system's operating cost. At this point, if the optimization objective remains minimizing system operating cost, it will restrict the system's ability to absorb wind and solar power. Mode 4, based on Mode 3, performs multi-objective optimization with the objectives of minimizing system operating cost and minimizing wind and solar curtailment. Table 3 shows that Mode 4's total system operating cost is slightly higher than Mode 3, but the cost of wind and solar curtailment is reduced by 40,300 yuan, and the wind and solar curtailment rate is further reduced by 1.69%.

[0238] Finally, an analysis of the changes in the interests of all parties involved in deep peak shaving is presented. In Mode 2, the thermal power units did not undergo deep peak shaving, while in Mode 4, they did. A comparative analysis of the impact of deep peak shaving on the interests of all parties is conducted from both wind power and thermal power perspectives, and the results are shown in Table 4. The peak-shaving units, energy storage devices, and wind power output in Mode 4 of this paper are as follows: Figure 4 As shown.

[0239] Table 4 Changes in the Interests of All Parties in Deep Peak Shaving

[0240] When thermal power units are in their normal peak-shaving phase, wind power does not need to provide any compensation to the thermal power units, and the profits of each party come from their respective power generation connected to the grid. When thermal power units are in deep peak-shaving, the system's wind power absorption capacity is further enhanced, and the increased wind power connected to the grid requires a deep peak-shaving compensation of US$42,100 to the peak-shaving thermal power units. Therefore, although deep peak-shaving of thermal power units increases additional peak-shaving costs, it receives sufficient compensation, ultimately increasing power generation profits. Although wind power needs to bear the peak-shaving costs of thermal power, which is equivalent to increasing power generation costs, the compensation cost is less than the wind power grid connection revenue brought by deep peak-shaving, so wind power profits are also improved. During low-load periods, thermal power units perform deep peak-shaving, and the sum of their peak-shaving power and the power stored in energy storage devices is the newly added wind power connected to the grid. During high-load periods, thermal power units generate more power, wind power is fully connected to the grid, and energy storage devices discharge.

[0241] This application provides a load scheduling system that considers active peak shaving and demand response complementarity. The system includes a memory and a processor. The memory includes a program for an ordered charging method based on credit assessment. When the program for the ordered charging method based on credit assessment is executed by the processor, it implements the steps of the load scheduling method considering active peak shaving and demand response complementarity as described above.

[0242] This application provides a computer-readable storage medium storing program code. When the program code is executed by a processor, it implements the steps of the above-described method for complementary load scheduling that considers proactive peak shaving and demand response.

[0243] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0244] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0245] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0246] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0247] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0248] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0249] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0250] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

[0251] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A load scheduling method considering complementary active peak shaving and demand response, characterized in that, The specific steps include the following: S1: In the analysis of peak shaving compensation and allocation methods of various generating units, considering the constraints on the initiative of various generating units in peak shaving, a model is established to incentivize various generating units to participate in peak shaving through peak shaving revenue; S2: On the load side, adopt price-based demand response to establish a model that guides users to actively participate in load adjustment. S3: Then, with the optimization objectives of minimizing system operating costs and minimizing wind and solar curtailment, a day-ahead optimization scheduling model for new energy systems is constructed using the simulated annealing-particle swarm adaptive algorithm, taking into account the initiative of thermal power peak shaving and the demand response of the load side. S4: Finally, we will take the improved IEEE 30-node system as an example to conduct multi-scenario analysis and verify its feasibility.

2. The load scheduling method considering complementary active peak shaving and demand response as described in claim 1, characterized in that, In S1, considering the proactive constraints of peak shaving for various generating units, the ultimate goal of peak shaving is to maximize the overall system benefit. g I w I pv I s These represent the number of thermal power, wind power, photovoltaic power, and energy storage participating in grid dispatch, respectively. Firstly, regarding the peak-shaving compensation model for thermal power units, for a given scheduling time, if a thermal power unit takes measures to reduce its output for peak shaving, the resulting loss needs to be compensated. The expression is as follows: （1） in, This refers to the peak-shaving compensation cost for conventional thermal power units. This is the standard peak-shaving compensation price for thermal power plants; This refers to the paid peak-shaving power of conventional thermal power plants. Total peak-shaving compensation expenditure at time t Represented as: （2） Peak-shaving compensation costs are shared by thermal power units, wind power, and photovoltaic power. The cost allocation for wind power and photovoltaic power is based on the proportion of total power generation on the deep peak-shaving trading day, while the cost allocation for thermal power units is based on their on-grid electricity price. The peak-shaving cost allocation model is as follows: （3） （4） （5） in, , and These represent the peak-shaving cost allocation for each conventional thermal power unit, wind farm, and photovoltaic power station at time t. Let t be the on-grid power of conventional thermal power unit i. Let t be the grid-connected power of the wind farm; For a given photovoltaic (PV) power generation at time t, if the compensation provided by non-peak-shaving entities exceeds the unit's own costs, the unit will be more motivated to participate. For wind farms and PV power plants, deep peak shaving can effectively increase their market share. As long as their shared costs are lower than the revenue from their increased power generation, wind farms and PV power plants are willing to actively participate in peak-shaving transactions. The active constraints on peak shaving for various generating units are as follows: (1) Peak-shaving revenue of thermal power units: （6） （7） in, For peak shaving costs of generating units; and These are the unit loss costs and coal costs during peak shaving periods, respectively. The coal-saving benefits resulting from reduced power generation by thermal power units; N represents the reduced power generation of thermal power units due to peak shaving. T For one scheduling cycle, (2) Wind power revenue: （8) in, The benchmark on-grid tariff for wind power, This indicates a punishment for abandoning the wind; This represents the amount of wind power curtailed at wind farm j during time period t. (3) Revenue from thermal power: （9） in, The benchmark feed-in tariff for photovoltaic power. This indicates a punishment for abandoning light; This represents the amount of wind power curtailed at photovoltaic power station k during time period t. When wind power revenue Less than 0 or photovoltaic power plant revenue When the value is less than 0, it means that the cost of wind farms or photovoltaic power plants participating in peak shaving has exceeded the benefits they receive from deep peak shaving. In this case, wind farms or photovoltaic power plants will withdraw from peak shaving to ensure their profitability. When the peak shaving revenue of thermal power units... When the value is less than 0, it means that the peak-shaving cost of thermal power units participating in deep peak shaving is greater than the peak-shaving compensation, and thermal power units will be the first to withdraw from peak shaving.

3. The load scheduling method considering complementary active peak shaving and demand response as described in claim 1, characterized in that, In S2, the price elasticity matrix is ​​used to describe the price-based demand response model. From an economic perspective, relative changes in electricity prices will cause corresponding changes in customer electricity demand. The degree of this change is represented by the price elasticity coefficient ε. （10） Where Δq and Δp are the changes in electricity consumption q and electricity price p per unit time, respectively. To balance system economics and renewable energy consumption levels, and to incorporate both system economics and wind / solar curtailment into the objective function, the two objective functions are scalarized. A linear weighting method is then used to transform the two functions with different dimensions, resulting in the following objective function: （11） Where w1 and w2 represent the weighting coefficients of economic efficiency and wind / solar curtailment, respectively, and w1 + w2 = 1; f1 represents the operating cost of the proposed model; d0 is the optimization result of the original scheduling model with the objective function of minimizing operating cost; f2 is the amount of wind / solar curtailment in the proposed model; θ0 is the amount of wind / solar curtailment in the original scheduling model with the objective function of minimizing wind / solar curtailment. The system operating cost C0 consists of the operating cost of thermal power units, the operating cost of energy storage systems, the cost of wind and solar curtailment penalties, and the reserve cost. （12） （13） （14） （15） （16） Where C is the operating cost of the thermal power unit, C sc For the operating cost of the energy storage system; C c The cost of penalties for curtailing wind and solar power; C b For system backup costs; c soc P represents the operating cost coefficient of the energy storage system. c, t and P d, t The charging and discharging power of the energy storage system; and The cost of penalizing the abandonment of wind and solar power; , The predicted output of wind power and photovoltaic power generation at time t are respectively; k b The price to provide a backup for the system; P L, t For system load power, The operating cost of thermal power units includes the following components: (1) Coal consumption cost of thermal power units （17) Among them, a i b i and c i P is the consumption coefficient of thermal power unit i; i, t For the output of thermal power units, (2) Loss cost of deep peak shaving units (18) Where α is the thermal power unit operation impact coefficient; C unit , i N represents the purchase cost of the i-th thermal power unit; f This represents the number of rotor cracking cycles determined by the rotor low-cycle fatigue curve. (3) Fuel cost of deep peak shaving units Combustion-supporting fuels incur additional peak-shaving fuel costs, which are as follows: （19） Among them, Q o , t R represents the amount of fuel oil input during the deep peak-shaving phase of a thermal power unit at time t; o For the current season's oil price, In summary, the operating cost of thermal power units can be divided into segments based on their operating conditions as follows: （20） in, and These represent the theoretical minimum and maximum output of the thermal power unit, respectively; P b For the unit's oil depth, peak shaving and fuel stability load limit; P a The limit for peak shaving and fuel stability load of the unit without oil. (4) Renewable energy absorption capacity Renewable energy absorption capacity is represented by the total amount of wind and solar curtailment (f2) during the dispatch cycle. The greater the amount of wind and solar curtailment, the weaker the renewable energy absorption capacity. The formula is as follows: （21） in, This represents the amount of solar power curtailed during time period t. This represents the amount of wind power curtailed by the wind farm during time period t. Duration of each time period The constraints are as follows: (1) Demand response constraints: (22) （23） in, To take into account the load value after demand response. The minimum value for satisfaction with electricity costs. This is the original load value; and These are the electricity prices before and after considering demand response. (24) (2) Power supply constraints Conventional peak-shaving units: （26） Deep peak shaving units: （27） Thermal power unit ramping constraints: （28） in, and These are the maximum and minimum technical outputs of the thermal power unit, respectively. This is the minimum output of the peak-shaving unit; and These are the maximum downhill and maximum uphill speeds of the thermal power unit, respectively. Wind power constraints: （29） in, The maximum output of the wind farm at time t; Photovoltaic power plant output constraints: （30) in, The maximum output of the photovoltaic power station at time t; (31) Among them, P b Reserve capacity for system rotation.

4. The load scheduling method considering complementary active peak shaving and demand response as described in claim 1, characterized in that, In S3, the improved simulated annealing algorithm is designed as follows: The principle of the traditional simulated annealing algorithm is as follows: Random walk in the search interval, that is, randomly select points, and then use the Metropolis sampling criterion to make the random walk gradually converge to the local optimal solution. Temperature is an important control parameter in the Metropolis algorithm. The size of this parameter controls the speed of the random process moving towards the local or global optimal solution. The Metropolis algorithm is: When the system changes from an energy state E1 to another state E2, the corresponding energy changes from to, and the probability is: (32) If E2 < E1, the system accepts this state; otherwise, it accepts or discards this state with a random state. The probability that state 2E2 is accepted is: （33） After a certain number of iterations like this, the system will gradually tend to a stable distribution state. When sampling with emphasis, if it goes down in the new state, accept the local optimum; if it goes up for global search, accept it with a certain probability. (1) Adaptive temperature control mechanism An adaptive temperature control mechanism is proposed to dynamically adjust the temperature drop rate according to the quality of the current solution. The temperature drop rate is determined by the improvement degree of the solution: If the solution found in the current iteration is significantly improved compared to the solution in the previous iteration, appropriately slow down the cooling rate to further explore the adjacent area; If the improvement of the solution is small or there is no improvement, accelerate the cooling process to speed up the convergence rate. （34） T1 is the updated temperature, and T is the current temperature. Here, Δf is a control parameter, representing the difference in the objective function between the current solution and the previous solution. (2) Multi-neighborhood dynamic search strategy The multi-neighborhood dynamic search strategy is introduced to expand the search range by switching between different neighborhoods. The specific implementation method is to dynamically select different neighborhood ranges according to the current state of the solution in each iteration. Use a large-range neighborhood for extensive search in the initial stage; as the algorithm gradually approaches the optimal solution, switch to a smaller-range neighborhood to improve the accuracy of local search. The mathematical expression for neighborhood selection is shown in formula (35): E2=E1+εN(0,σ t )(35) E2 is the new solution, ε is the step size, and N is the value with a mean of 0 and a standard deviation of σ. t For a normally distributed random variable, σ increases with the number of iterations. t Gradually reduce the size to ensure a more refined search later. (3) Improved acceptance criterion A dynamic acceptance criterion based on the quality of the current solution is proposed: When the objective function value is close to the global optimum, the algorithm gradually reduces the probability of accepting inferior solutions, thereby accelerating convergence; in the initial stage, to maintain the global search ability, a certain probability of accepting inferior solutions is retained. The acceptance probability calculation is shown in formula (36): （36） In the formula, T is the current temperature. f is a control parameter. c f represents the objective function value of the current solution. best This represents the objective function value of the current optimal solution. Through this dynamic acceptance criterion, the algorithm can effectively balance global search and local convergence, improving the overall convergence speed.

5. A load dispatching system that considers complementary active peak shaving and demand response, characterized in that, The system includes: a memory and a processor. The memory includes a program for the complementary load scheduling method considering active peak shaving and demand response. When the program for the complementary load scheduling method considering active peak shaving and demand response is executed by the processor, the following steps are implemented: S1: Analyze various types of unit peak shaving compensation and sharing methods on the power generation side, consider the active peak shaving constraints of various types of units, and establish a model to encourage various types of units to participate in peak shaving through peak shaving benefits; S2: Adopt price-based demand response on the load side and establish a model to guide users to actively participate in load adjustment; S3: Then, with the minimization of system operation cost and the minimization of wind and light abandonment as the optimization objectives, use the simulated annealing-particle swarm optimization algorithm to construct a day-ahead optimal scheduling model for a new energy system considering the active peak shaving of thermal power and demand response on the load side; S4: Finally, take the improved IEEE 30-node system as an example for multi-scenario analysis and verify its feasibility.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code that, when executed by a processor, implements the steps of the load scheduling method considering active peak shaving and demand response complementary load scheduling as described in any one of claims 1 to 4.

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