A power system peak regulation cost estimation method, medium and system

Abstract

The application discloses a power system peak regulation cost estimation method, medium and system, comprising: establishing a target function of a peak regulation cost optimization model containing a peak regulation thermal power generating unit, an energy storage power station and a pumped storage unit; determining constraint conditions of the target function; under the constraint conditions, solving the target function by using a particle swarm algorithm to obtain a decision variable combination when the target function is the minimum. The application comprehensively considers various peak regulation technical means, can realize the optimal system peak regulation cost, applies a time-of-use electricity price mechanism to guide energy storage charging and discharging behaviors, realizes reasonable utilization under an existing energy storage configuration capacity, reduces the wind curtailment and light curtailment phenomenon, and promotes new energy consumption.

Description

Technical Field

[0001] This invention relates to the field of power system peak shaving technology, and in particular to a method, medium and system for estimating power system peak shaving costs. Background Technology

[0002] The intermittent and random power output of renewable energy sources with a high proportion of renewable energy has brought difficulties to the power grid in peak shaving, posing a severe challenge to the grid's peak shaving efforts. With the large-scale integration of renewable energy, flexibly utilizing peak shaving resources to optimize power system peak shaving costs is of great significance.

[0003] Unlike other forms of energy, electricity cannot currently be stored in large quantities. The use of electricity in the power grid requires a balance between supply and demand; that is, the electricity generated by power plants and the electricity consumed by users must remain balanced for a short period. When this balance is disrupted, it will severely impact the safe operation of generators and the system, leading to frequency collapse. This necessitates appropriate measures for power system peak shaving to maintain this balance. In recent years, new energy sources such as wind power and solar power, which are low-cost, pollution-free, and have enormous development potential, have developed rapidly. However, the uncertainty of wind and solar power has significantly increased the peak-to-valley load difference in the system, severely restricting the absorption capacity of new energy sources. Relying solely on the existing peak-shaving capacity of the system is insufficient to alleviate the peak-shaving dilemma; it is necessary to further explore flexible power sources to reduce wind and solar curtailment.

[0004] With the advancement of electricity market construction, the market for electricity peak-shaving ancillary services has gradually developed. Grid access agreements and ancillary service management rules stipulate that power generation companies must provide ancillary services, distinguishing between basic ancillary services and paid ancillary services. The peak-shaving capacity and renewable energy consumption capacity of provincial grids have been significantly improved, effectively alleviating the phenomenon of clean energy curtailment.

[0005] However, existing peak-shaving cost calculation models ignore the transmission impact of electricity market ancillary service policies on grid peak-shaving costs, and simply provide deep peak-shaving quotes for generating units, which cannot be effectively linked with policies and cannot make reasonable use of peak-shaving resources. Summary of the Invention

[0006] This invention provides a method, medium, and system for estimating peak-shaving costs in multiple power systems, addressing the problem that existing technologies neglect the transmission impact of power market ancillary service policies on grid peak-shaving costs, simply provide unit deep peak-shaving quotations, fail to effectively link with policies, and fail to rationally utilize peak-shaving resources.

[0007] Firstly, a method for estimating peak-shaving costs in a power system is provided, including:

[0008] Establish the objective function of a peak-shaving cost optimization model that includes peak-shaving thermal power units, energy storage power stations, and pumped storage units;

[0009] Determine the constraints of the objective function;

[0010] Under the given constraints, the objective function is solved using the particle swarm optimization algorithm to obtain the combination of decision variables that minimizes the objective function;

[0011] The objective function includes:

[0012] C represents the total peak-shaving cost of the system, N C Ω is a collection of thermal power units. D N is a set of deep peak shaving levels. E N is a collection of energy storage power stations. P N is a collection of pumped storage units. N For a collection of new energy sources, c i,o For unit i, the price for deep peak shaving in the o-th segment, ΔP i,t,o y represents the peak shaving amount won by unit i in the oth deep peak shaving segment of time period t. i,t This is a 0-1 variable representing whether unit i is turned on during time period t, where 1 indicates it is turned on and 0 indicates it is not. Let z be the startup cost of unit i during time period t. i,t This is a 0-1 variable representing whether unit i is shut down during time period t, where 1 indicates shutdown and 0 indicates otherwise. Let be the downtime cost of unit i during time period t. Let c be the charging power of the energy storage power station g during time period t. g Quotation for charging a g-type energy storage power station. Let c be the discharge power of energy storage power station g during time period t. fs For the time-of-use electricity price of the power grid, c h A quote for the hourly pumping power of a pumped-storage hydroelectric unit. Let c be the pumping power of pumped storage unit h during time period t. m The unit cost of curtailing wind and solar power. Let m be the amount of wind and solar power curtailed during time period t, and T be the set of scheduling time periods;

[0013] The constraints include: power system-related constraints, thermal power unit constraints, energy storage power station constraints, pumped storage power station constraints, and new energy output constraints.

[0014] The decision variables include: ΔP i,t,o y i,t z i,t , and

[0015] In a second aspect, a computer-readable storage medium is provided, wherein computer program instructions are stored on the computer-readable storage medium; when the computer program instructions are executed by a processor, they implement the power system peak-shaving cost estimation method as described in the first aspect embodiment.

[0016] Thirdly, a power system peak-shaving cost estimation system is provided, comprising: a computer-readable storage medium as described in the second aspect embodiment.

[0017] Thus, this embodiment of the invention comprehensively considers various peak-shaving technologies, can achieve the optimal system peak-shaving cost, and applies the time-of-use pricing mechanism to guide the charging and discharging behavior of energy storage, realize the rational utilization of existing energy storage capacity, reduce wind and solar curtailment, and promote the consumption of new energy sources. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a power system peak-shaving cost estimation method according to an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] This invention discloses a method for estimating peak-shaving costs in power systems. For example... Figure 1 As shown, the method of this embodiment of the invention specifically includes the following steps:

[0022] Step S101: Establish the objective function of the peak-shaving cost optimization model that includes peak-shaving thermal power units, energy storage power stations, and pumped storage units.

[0023] Thermal power units, energy storage power stations, and pumped storage units are generally the main participants in paid peak shaving.

[0024] Specifically, the objective function is as follows:

[0025]

[0026] Where C is the total peak-shaving cost of the system, and N C Ω is a collection of thermal power units. D N is a set of deep peak shaving levels. E N is a collection of energy storage power stations. P N is a collection of pumped storage units. N For a collection of new energy sources, c i,o For unit i, the price for deep peak shaving in the o-th segment, ΔP i,t,o y represents the peak shaving amount won by unit i in the oth deep peak shaving segment of time period t. i,t This is a 0-1 variable representing whether unit i is turned on during time period t, where 1 indicates it is turned on and 0 indicates it is not. Let z be the startup cost of unit i during time period t. i,t This is a 0-1 variable representing whether unit i is shut down during time period t, where 1 indicates shutdown and 0 indicates otherwise. Let be the downtime cost of unit i during time period t. Let c be the charging power of the energy storage power station g during time period t. g Quotation for charging a g-type energy storage power station. Let c be the discharge power of energy storage power station g during time period t. fs The time-of-use pricing for the power grid is used to guide energy storage for peak shaving and valley filling. h A quote for the hourly pumping power of a pumped-storage hydroelectric unit. Let c be the pumping power of pumped storage unit h during time period t. m The unit cost of curtailing wind and solar power. Let m be the amount of wind and solar power curtailed during time period t, where T is the set of scheduling time periods.

[0027] For the bidding of unit i for deep peak shaving in stage o, each thermal power plant reports its peak shaving intention and adjustable active power output range to the trading center. Thermal power plants intending to participate in peak shaving must also submit their respective bids according to the bidding rules. The current bidding rules adopt a tiered bidding approach, specifying the upper and lower limits and output range for each tier. If the free peak shaving ancillary services cannot meet the peak shaving demand, the power dispatching unit will call upon paid peak shaving ancillary services from low to high based on the previous day's bidding results. In this embodiment of the invention, to simplify the analysis, the average of the upper and lower limits of each tier's bid in the power ancillary service market policy is used as the bid price for unit i for deep peak shaving in stage o, that is:

[0028]

[0029] in, This is the upper limit of the price for the o-th deep peak shaving segment. This is the lower limit of the price for the o-th deep peak shaving segment.

[0030] Step S102: Determine the constraints of the objective function.

[0031] Specifically, the constraints include: power system-related constraints, thermal power unit constraints, energy storage power station constraints, pumped storage power station constraints, and new energy output constraints.

[0032] 1. Power system related constraints

[0033] (1) System power balance constraints include:

[0034]

[0035] Among them, P i,t P represents the output of unit i during time period t. m,t For the output of new energy m in time period t, D t Let be the load during time period t. P represents the power generation of the pumped storage unit h during time period t. gd For a fixed output, t∈T.

[0036] (2) System standby constraints:

[0037]

[0038]

[0039] Among them, u i,t The variable is a 0-1 symbol representing the operating status of the unit. A value of 1 indicates that unit i is in the powered-on state during time period t, and a value of 0 indicates that unit i is in the powered-off state during time period t. and P represents the positive and negative reserve requirements of the system during time period t. i max and P i min These represent the maximum and minimum technical output of the generating units, respectively. Specifically, the maximum technical output indicates that all operating units are at full power, while the minimum technical output indicates the lower limit of output after the flexibility modification of the deep peak-shaving units.

[0040] 2. Constraints of thermal power units include:

[0041] (1) Power constraints of thermal power units:

[0042] u i,t P i min ≤P i,t ≤u i,t P i max (6)

[0043]

[0044]

[0045] in, For unit i, the maximum capacity for deep peak shaving in segment o, Let t be the basic peak-shaving reference value for unit i during time period t, i∈N C ,o∈Ω D .

[0046] Equation (8) indicates that the sum of the unit output and the deep peak shaving target value should exceed the basic peak shaving benchmark value, reflecting the deep peak shaving of the unit.

[0047] (2) Climbing constraints of thermal power units:

[0048] P i,t -P i,t-1 ≤ΔP i U +(1-u i,t-1 )P i max (9)

[0049] P i,t-1 -P i,t ≤ΔP i D +(1-u i,t )P i max (10)

[0050] Wherein, ΔP i U and ΔP i D These represent the upper and lower ramp speed limits for unit i, respectively, and P. i,t-1 For the output of unit i in time period t-1, u i,t-1 The variable is a 0-1 variable representing the operating status of the unit. A value of 1 indicates that the unit i is in the powered-on state during the time period t-1, and a value of 0 indicates that the unit i is in the powered-off state during the time period t-1.

[0051] (3) Operating status constraints of thermal power units:

[0052] y i,t -z i,t =u i,t -u i,t-1 (11)

[0053] y i,t +z i,t ≤1 (12)

[0054] Equations (11) and (12) show that start-up and shutdown costs are incurred only when the unit is in a different state at two different times. There are no start-up and shutdown costs at the initial position, and the unit can only start or stop at the same time.

[0055] (4) Constraints on continuous operating time of thermal power units:

[0056]

[0057]

[0058] Among them, T i on and T i off These represent the minimum continuous operating time and downtime of unit i, respectively, u i,a The variable is a 0-1 variable representing the operating status of the unit. A value of 1 indicates that unit i is in the on state during time period a, and a value of 0 indicates that unit i is in the off state during time period a.

[0059] 3. Constraints on energy storage power stations include:

[0060] (1) Energy storage charging and discharging power constraints:

[0061]

[0062]

[0063] in, and These represent the maximum charging and discharging power of the energy storage power station, respectively. Let g be a 0-1 variable representing the charging state of the energy storage power station g during time period t, where 1 indicates that it is in the charging state and 0 otherwise. Let g be a 0-1 variable representing the discharge state of the energy storage power station during time period t, where 1 indicates that it is in the discharge state and 0 otherwise, and g∈N. E .

[0064] (2) Constraints on the operating status of energy storage power stations:

[0065]

[0066] (3) Energy storage state of charge constraints:

[0067]

[0068]

[0069] Among them, S g,t and S g,t-1 These are the SOC values ​​of the energy storage power station g at time periods t and t-1, respectively. g min and S g max These represent the upper and lower limits of the energy storage SOC, respectively, where σ is the energy storage self-discharge rate, and η is the energy storage self-discharge rate. c and ηd These represent the energy storage charging and discharging efficiencies, respectively, in V. i max Let g be the maximum capacity of the energy storage power station.

[0070] 4. Constraints of pumped storage power stations include:

[0071] (1) Power constraints of pumped storage power stations:

[0072]

[0073]

[0074] Among them, P h G,max and P h S,max These represent the maximum pumping and discharging power (p) of the pumped storage unit, respectively. h G,min and P h S,min These represent the minimum pumping and discharging power (h) of the pumped storage unit, respectively. Let h be a 0-1 variable representing the pumped storage unit h during time period t, where 1 indicates it is in the charging state and 0 otherwise. Let h be a 0-1 variable representing the discharge state of the pumped storage unit h during time period t, where 1 indicates that it is in the discharge state and 0 otherwise, h∈N. P .

[0075] (2) Operating status constraints of pumped storage power stations:

[0076]

[0077]

[0078]

[0079] in, Let be a 0-1 variable representing the power generation state of a pumped storage power station during time period t, where 1 indicates that it is in power generation state, and 0 otherwise. This is a 0-1 variable representing the pumping state of a pumped storage power station during time period t. A value of 1 indicates that the station is in the pumping state, and a value of 0 indicates otherwise.

[0080] (3) Capacity constraints of pumped storage power stations:

[0081]

[0082] L min ≤L t ≤L max (26)

[0083]

[0084] Among them, L t and L t-1 The reservoir storage at the end of time periods t and t-1 are respectively, L min and L max These represent the lower and upper limits of the reservoir's water storage capacity, respectively, η s and η g These are the ratios of average water volume to electricity conversion during pumping and power generation, respectively.

[0085] 5. Constraints on new energy output include:

[0086]

[0087] Among them, P m,t pre Let m be the predicted output value of the new energy source m in time period t, where m∈N N .

[0088] Step S103: Under the constraints, the particle swarm optimization algorithm is used to solve the objective function to obtain the combination of decision variables when the objective function is minimized.

[0089] Specifically, the decision variables include: ΔP i,t,o y i,t z i,t , and

[0090] In the particle swarm optimization algorithm, a swarm of particles replaces a flock of birds. The distance between birds and food is represented by a fitness function, and particles fly at a certain speed in the search space. This speed is dynamically adjusted based on the flight experience of individuals and the group, continuously optimizing until an optimal fitness value is obtained. Here, the number of decision variables is used as the dimension of the vector, and the combination of decision variables adopted for power system peak shaving is used as the basic particles, with the number of combinations equal to the number of particles. Through optimization, the optimal solution for the peak shaving cost of the power system is finally obtained. Specifically, this step includes the following process:

[0091] 1. Initialize the parameters of the particle population.

[0092] The parameters of the particle swarm include: particle position, velocity, and particle swarm size.

[0093] Specifically, the position of the j-th particle represents the j-th combination of decision variables adopted by the power system, the velocity of the j-th particle represents the direction and distance of movement of the j-th particle in the next iteration, and the position of the j-th particle is: X jD =(x j1 ,x j2 ,…,x jD The velocity of the j-th particle is: VjD =(v j1 ,v j2 ,…,v jD ), j = 1, 2, ..., N, D is the number of decision variables, and N is the number of possible combinations of decision variables for power system peak shaving.

[0094] 2. Calculate the particle's fitness value based on its initial position to obtain the particle's individual historical best position and velocity, as well as its global best position and velocity.

[0095] The fitness value of a particle is denoted as F. it [j] represents the value of the objective function calculated under the j-th combination of decision variables.

[0096] 3. Compare the particle's current fitness value with the fitness value corresponding to the particle's individual best historical position. If the current fitness value is higher, update the particle's individual best historical position and velocity with the position and velocity corresponding to the current fitness value.

[0097] 4. Compare the updated fitness value of the individual's historical best position with the fitness value of the global best position. If the updated fitness value of the individual's historical best position is higher, then update the global best position and velocity with the position and velocity corresponding to the updated fitness value of the individual's historical best position.

[0098] Specifically, the formula for updating the particle's velocity is:

[0099]

[0100] Where ω is the inertia weight, K is the number of iterations, c1 and c2 are learning factors, and r1 and r2 are uniformly distributed random numbers in the interval [0,1]. Let J be the individual historical optimal solution for the j-th particle after the k-th update. This is the globally optimal solution after the k-th update. This represents the position of the j-th particle after the k-th update.

[0101] Specifically, the formula for updating the particle's position is:

[0102]

[0103] in, This represents the position of the j-th particle after the (k+1)-th update. Let be the velocity of the j-th particle after the (k+1)-th update.

[0104] 5. Determine if the number of iterations has reached the maximum number of iterations.

[0105] 6. If the objective function is reached, output the fitness value corresponding to the global best position and the global best position, and obtain the minimum value of the objective function and the corresponding combination of decision variables. Otherwise, return to the step of comparing the updated fitness value of each particle's individual historical best position with the fitness value corresponding to the global best position of that particle.

[0106] The physical quantities used in the above steps of this invention can be obtained by acquiring key scenarios for power grid peak shaving, including system load characteristic curves, wind and solar power output prediction curves, and the composition of various power sources in the system, as well as by investigating the technical parameters of thermal power units, energy storage power stations, and pumped storage power stations in accordance with relevant electricity market policies, the trading rules for acquiring paid peak shaving resources, and the current time-of-use pricing mechanism, and determining the technical cost characteristic boundaries of various peak shaving resources.

[0107] Furthermore, embodiments of the present invention also provide a computer-readable storage medium storing computer program instructions; when the computer program instructions are executed by a processor, they implement the power system peak-shaving cost estimation method as described in the above embodiments.

[0108] Furthermore, embodiments of the present invention also provide a power system peak-shaving cost estimation system, comprising: a computer-readable storage medium as described in the above embodiments.

[0109] In summary, the embodiments of the present invention can study the technical and cost characteristics boundaries of different types of flexible adjustment resources based on the economic and technical characteristics of different flexible adjustment resources and in combination with the existing power structure. The peak-shaving resources considered include thermal power units, energy storage units and pumped storage units, and the time-of-use pricing mechanism in the electricity market is introduced, which is more in line with the actual situation of the electricity market and can effectively guide energy storage to "shave peaks and fill valleys".

[0110] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for estimating peak-shaving costs in a power system, characterized in that, include: Establish the objective function of a peak-shaving cost optimization model that includes peak-shaving thermal power units, energy storage power stations, and pumped storage units; Determine the constraints of the objective function; Under the given constraints, the objective function is solved using the particle swarm optimization algorithm to obtain the combination of decision variables that minimizes the objective function; The objective function includes: C represents the total peak-shaving cost of the system, N C Ω is a collection of thermal power units. D N is a set of deep peak shaving levels. E N is a collection of energy storage power stations. P N is a collection of pumped storage units. N For a collection of new energy sources, c i,o For unit i, the price for deep peak shaving in the o-th segment, ΔP i,t,o y represents the peak shaving amount won by unit i in the oth segment of deep peak shaving during time period t. i,t This is a 0-1 variable representing whether unit i is turned on during time period t, where 1 indicates it is turned on and 0 indicates it is not. Let z be the startup cost of unit i during time period t. i,t This is a 0-1 variable representing whether unit i is shut down during time period t, where 1 indicates shutdown and 0 indicates otherwise. Let be the downtime cost of unit i during time period t. Let c be the charging power of the energy storage power station g during time period t. g Quotation for charging a g-type energy storage power station. Let c be the discharge power of energy storage power station g during time period t. fs For the time-of-use electricity price of the power grid, c h A quote for the hourly pumping power of a pumped-storage hydroelectric unit. Let c be the pumping power of pumped storage unit h during time period t. m The unit cost of curtailing wind and solar power. Let m be the amount of wind and solar power curtailed during time period t, and T be the set of scheduling time periods; The constraints include: power system-related constraints, thermal power unit constraints, energy storage power station constraints, pumped storage power station constraints, and new energy output constraints. The decision variables include: ΔP i,t,o y i,t z i,t , and 2. The method for estimating peak-shaving costs in a power system according to claim 1, characterized in that, The power system-related constraints include: System power balance constraints include: Among them, P i,t P represents the output of unit i during time period t. m,t For the output of new energy m in time period t, D t Let be the load during time period t. P represents the power generation of the pumped storage unit h during time period t. gd For a fixed output, t∈T; System backup constraints include: Among them, u i,t The variable is a 0-1 symbol representing the operating status of the unit. A value of 1 indicates that unit i is in the powered-on state during time period t, and a value of 0 indicates that unit i is in the powered-off state during time period t. and P represents the positive and negative reserve requirements of the system during time period t. i max and P i min These represent the maximum and minimum technical outputs of the generator unit, respectively.

3. The method for estimating peak-shaving costs in a power system according to claim 2, characterized in that, The constraints of the thermal power unit include: Power constraints for thermal power units include: u i,t Q i min ≤P i,t ≤u i,t Q i max ; in, For unit i, the maximum capacity for deep peak shaving in segment o, Let t be the basic peak-shaving reference value for unit i during time period t, i∈N C ,o∈Ω D ; The climbing constraints for thermal power units include: P i,t -P i,t-1 ≤ΔP i U +(1-u i,t-1 )P i max ; P i,t-1 -P i,t ≤ΔP i D +(1-u i,t )P i max ; Where, ΔP i U and ΔP i D These represent the upper and lower ramp speed limits for unit i, respectively, and P. i,t-1 For the output of unit i in time period t-1, u i,t-1 The variable is a 0-1 representing the operating status of the unit. A value of 1 indicates that unit i is in the powered-on state during time period t-1, and a value of 0 indicates that unit i is in the powered-off state during time period t-1. Operating status constraints for thermal power units include: yes i,t -z i,t =u i,t -u i,t-1 ; y i,t +z i,t ≤1; The continuous operating time constraints for thermal power units include: Among them, T i on and T i off These represent the minimum continuous operating time and downtime of unit i, respectively, u i,a The variable is a 0-1 variable representing the operating status of the unit. A value of 1 indicates that unit i is in the on state during time period a, and a value of 0 indicates that unit i is in the off state during time period a.

4. The method for estimating peak-shaving costs in a power system according to claim 3, characterized in that, The constraints of the energy storage power station include: Energy storage charging and discharging power constraints: in, and These represent the maximum charging and discharging power of the energy storage power station, respectively. Let g be a 0-1 variable representing the charging state of the energy storage power station g during time period t, where 1 indicates that it is in the charging state and 0 otherwise. Let g be a 0-1 variable representing the discharge state of the energy storage power station during time period t, where 1 indicates that it is in the discharge state and 0 otherwise, and g∈N. E ; The operating status constraints of energy storage power stations include: Energy storage state of charge constraints include: Among them, S g,t and S g,t-1 These are the SOC values ​​of the energy storage power station g at time periods t and t-1, respectively. g min and S g max These represent the upper and lower limits of the energy storage SOC, respectively, where σ is the energy storage self-discharge rate, and η is the energy storage self-discharge rate. c and η d These represent the energy storage charging and discharging efficiencies, respectively, in V. i max Let g be the maximum capacity of the energy storage power station.

5. The method for estimating peak-shaving costs in a power system according to claim 4, characterized in that, The constraints of the pumped storage power station include: Power constraints for pumped storage power stations include: in, and These represent the maximum pumping and discharging power (h) of the pumped storage unit, respectively. and These represent the minimum pumping and discharging power (h) of the pumped storage unit, respectively. Let h be a 0-1 variable representing the pumped storage unit h during time period t, where 1 indicates it is in the charging state and 0 otherwise. Let h be a 0-1 variable representing the discharge state of the pumped storage unit h during time period t, where 1 indicates that it is in the discharge state and 0 otherwise, h∈N. P ; The operational constraints of pumped storage power stations include: in, Let be a 0-1 variable representing the power generation state of a pumped storage power station during time period t, where 1 indicates that it is in power generation state, and 0 otherwise. For pumped storage power station in pumping state during time period t, 0-1 variable is used, where 1 indicates that it is in pumping state, and 0 otherwise. Pumped storage power station capacity constraints include: L min ≤L t ≤L max ; Among them, L t and L t-1 The reservoir storage at the end of time periods t and t-1 are respectively, L min and L max These represent the lower and upper limits of the reservoir's water storage capacity, respectively, η s and η g These are the ratios of average water volume to electricity conversion during pumping and power generation, respectively.

6. The method for estimating peak-shaving costs in a power system according to claim 5, characterized in that, The constraints on the output of the new energy sources include: in, Let m be the predicted output value of the new energy source m in time period t, where m∈N N .

7. The method for estimating peak-shaving costs in a power system according to claim 1, characterized in that, The steps of solving the objective function using the particle swarm optimization algorithm include: Initialize the parameters of the particle swarm, wherein the parameters of the particle swarm include: particle position, velocity, and particle swarm size; the position of the j-th particle represents the j-th decision variable combination adopted by the power system; the velocity of the j-th particle represents the direction and distance of movement of the j-th particle in the next iteration; and the position of the j-th particle is: X. jD =(x j1 ,x j2 ,…,x jD The velocity of the j-th particle is: V jD =(v j1 ,v j2 ,…,v jD ), j = 1, 2, ..., N, D is the number of decision variables, and N is the number of possible combinations of decision variables for power system peak shaving; The particle's fitness value is calculated based on its initial position, resulting in the particle's individual historical best position and velocity, as well as its global best position and velocity. The particle's fitness value is the value of the objective function calculated under the j-th combination of decision variables. Compare the particle's current fitness value with the fitness value corresponding to the particle's individual historical best position. If the current fitness value is higher, update the particle's individual historical best position and velocity with the position and velocity corresponding to the current fitness value. Compare the updated fitness value of the individual's historical best position with the fitness value of the global best position. If the updated fitness value of the individual's historical best position is higher, then update the global best position and velocity with the position and velocity corresponding to the updated fitness value of the individual's historical best position. Determine if the maximum number of iterations has been reached; If the objective function is reached, the fitness value corresponding to the global best position and the global best position are output, and the minimum value of the objective function and the corresponding combination of decision variables are obtained. Otherwise, the step of comparing the fitness value corresponding to the updated individual historical best position of the particle with the fitness value corresponding to the global best position is returned.

8. The method for estimating peak-shaving costs in a power system according to claim 1, characterized in that, The formula for updating the particle's velocity is: Where ω is the inertia weight, K is the number of iterations, c1 and c2 are learning factors, and r1 and r2 are uniformly distributed random numbers in the interval [0,1]. This represents the individual historical optimal solution for the j-th particle after the k-th update. This is the globally optimal solution after the k-th update. This represents the position of the j-th particle after the k-th update. The formula for updating the particle's position is: in, This represents the position of the j-th particle after the (k+1)-th update. Let be the velocity of the j-th particle after the (k+1)-th update.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by a processor, they implement the power system peak-shaving cost estimation method as described in any one of claims 1 to 8.

10. A power system peak-shaving cost estimation system, characterized in that, include: The computer-readable storage medium as described in claim 9.

Images