A method for optimizing energy storage capacity configuration based on power system time-series production simulation

Abstract

The present invention discloses a method for optimizing energy storage capacity configuration based on power system sequential production simulation, comprising: determining a power system sequential production simulation model based on unit aggregation, with minimizing system operating costs and maximizing new energy output as optimization goals, simulating and calculating the power system sequential operation process during a target planning period, and obtaining a reference value for energy storage planning capacity; determining a power system sequential production simulation model based on daily rolling, for solving the new energy consumption value under a specified energy storage planning capacity; iteratively optimizing the energy storage planning capacity according to the set new energy consumption value boundary, so that the new energy consumption rate corresponding to the energy storage planning capacity approaches the preset new energy consumption rate; and obtaining the optimal energy storage planning capacity during the target planning period. The present invention can quickly and accurately analyze and obtain an optimized configuration of energy storage capacity that meets the new energy consumption requirements, thereby improving the new energy consumption capacity and operational stability of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning, and in particular to a method for optimizing energy storage capacity configuration based on power system sequential production simulation. Background Art

[0002] In recent years, my country's wind power and photovoltaic industries have experienced rapid growth. However, the imbalance in the development of new energy sources has become increasingly prominent, particularly in the absorption of new energy sources, which has seriously constrained the healthy and sustainable development of the power industry. In this environment, the development of energy storage technology is crucial for ensuring the large-scale development of new energy sources and the security of the power grid. As a flexible, rapidly adjustable resource, energy storage can be used for stabilizing fluctuations, responding to demand, regulating frequency, and providing emergency reserves. Energy storage participates in energy systems, relieving peak user loads, delaying line expansion, and facilitating the absorption of new energy sources. It plays a crucial role in promoting the absorption of new energy sources and ensuring the safe and stable operation of power systems, and is one of the most promising technologies for the future.

[0003] Planning energy storage capacity is a complex optimization problem involving the economic efficiency of energy storage devices, system operational flexibility, and the uncertainty of renewable energy output. Furthermore, only by coordinating energy storage can we better address energy consumption and stability issues, thereby significantly improving the regulation capabilities of renewable energy systems and their potential for grid access.

[0004] Considering the current state of power system development, the "low-carbon" development trend, and the actual needs of power grid companies for dispatching systems, and addressing the uncertainties brought about by the integration of new energy sources, power systems require more backup resources compared to traditional operating methods. To address these issues, energy storage capacity planning should take into account existing, adjustable traditional generators, fully considering the operating characteristics of various system resources, and thus developing a more refined approach to energy storage capacity planning. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for optimizing the configuration of energy storage capacity based on power system time-series production simulation, which can quickly and accurately analyze and obtain the energy storage capacity configuration that meets the requirements of new energy consumption, realize the optimization of energy storage capacity configuration, and improve the new energy consumption capacity and operational stability.

[0006] To achieve the above objectives, the present invention adopts a technical solution: a method for optimizing energy storage capacity configuration based on power system time-series production simulation, comprising:

[0007] Determine the power system sequential production simulation model based on unit aggregation;

[0008] Using the power system sequential production simulation model based on unit aggregation, with minimizing system operating costs and maximizing renewable energy output as optimization goals, the power system sequential operation process during the target planning period is simulated and calculated to obtain a reference value for energy storage planning capacity during the target planning period;

[0009] Determine a daily rolling power system time-series production simulation model to solve the renewable energy consumption value under a specified energy storage planning capacity;

[0010] Iteratively optimizing the planned energy storage capacity according to the set renewable energy consumption value boundary so that the renewable energy consumption rate corresponding to the planned energy storage capacity approaches a preset expected renewable energy consumption rate: adjusting the planned energy storage capacity in each iteration based on a reference value of the planned energy storage capacity for the target planning period, and calculating the corresponding renewable energy consumption value based on the adjusted planned energy storage capacity using the daily rolling power system time series production simulation model;

[0011] The optimal energy storage planning capacity obtained through iterative optimization is used as the energy storage planning capacity for the target planning period.

[0012] Optionally, the method for constructing the power system time-series production simulation model based on unit aggregation includes:

[0013] Classify all thermal power units in the regional power system according to unit type, capacity level and operating characteristics;

[0014] Treat thermal power units with the same or similar operating characteristics as a whole to build a cluster of thermal power units;

[0015] Determine the variables used to characterize the operating status of cluster thermal power units and describe the aggregation effect of the temporal operating status of multiple thermal power units.

[0016] Optionally, the power system time-series production simulation model based on unit aggregation includes:

[0017] A) The operating status of a single thermal power unit at time t is expressed as:

[0018]

[0019] Where x i (t) represents the grid-connected state variable, u i (t) represents the startup state variable, d i (t) represents the shutdown state variable, x i u(t) is equal to 1 or 0, indicating that the state of unit i at time t is grid-connected operation or offline state; i (t) equal to 1 means that unit i starts at time t and switches from offline state to grid-connected operation state; d iu(t) is equal to 1, indicating that unit i is shut down at time t and switches from grid-connected operation to offline state; i (t) and d i (t) equal to 0 means that the operating status of unit i has not changed at time t;

[0020] B) The startup status of cluster thermal power units is expressed as:

[0021]

[0022]

[0023]

[0024] Where, They represent the startup capacity, start-up capacity and shutdown capacity variables of cluster thermal power unit j respectively; J represents the number of equivalent units in the cluster thermal power unit, represents the rated capacity of unit i;

[0025] The startup, start-up and shutdown capacity variables satisfy:

[0026]

[0027] C) The discrete variables of the startup capacity, start-up capacity and shutdown capacity of the cluster thermal power units are approximately described as:

[0028]

[0029]

[0030]

[0031] The equivalent unit capacity is:

[0032]

[0033] In the formula, the integer variable x j (t),u j (t) and d j (t) represents the number of equivalent units in the grid-connected operation state, the start-up state and the shutdown state at time t respectively;

[0034] The value range of the continuous variable of the cluster thermal power unit operating status is expressed as:

[0035]

[0036] Among them, S j Indicates the total capacity of the cluster's thermal power units, which is the sum of the rated capacities of all units;

[0037] The change of the startup capacity of cluster thermal power unit j between adjacent moments is expressed as:

[0038]

[0039] In the above technical solution, continuous variables are used to model the startup capacity, start-up capacity, and shutdown capacity of cluster units. A cluster consisting of J units is treated as a single unit, and it is assumed that the operating state of this unit is not limited to shutdown "0" and grid connection "1", but can exist in several operating states between 0 and 1. The continuously changing operating state is represented by the continuous unit startup capacity. The resulting power system sequential production simulation model based on unit aggregation can effectively reduce the number of variables. The cluster unit operating state, output power, ramping constraints, and minimum start-stop time constraints established based on the continuous variable of cluster unit startup capacity can accurately simulate the sequential operation process of the cluster units.

[0040] Optionally, the optimization goal is to minimize the system operating cost and maximize the output of renewable energy, and simulate the time sequence operation process of the power system in the target planning period. The corresponding objective function is expressed as:

[0041]

[0042] Where, They represent the power generation cost, startup cost, and shutdown cost of all cluster units respectively. The sum of the three is the total operating cost of the system; θ is the penalty coefficient for the power output of renewable energy, represents the renewable energy power output of regional power grid k at time t in the system, and:

[0043]

[0044] in, represents the maximum power that can be generated by renewable energy in regional power grid k at time t, represents the power generation of renewable energy in regional power grid k at time t.

[0045] The above technical solution constructs a power system timing production simulation model based on unit aggregation, with the optimization goals of minimizing system operating costs and maximizing the amount of renewable energy grid-connected power generation. It can simulate the timing operation process of large-scale power systems under the constraints of power balance, backup demand, renewable energy output and unit operating characteristics, and realize rapid calculation of medium- and long-term timing production simulation of regional power systems.

[0046] Furthermore, when performing a power system sequential production simulation based on a power system sequential production simulation model of unit aggregation, the input parameters of the model include: sequential load power curve, sequential maximum output curve of new energy, load reserve factor that meets reliability requirements, reserve factor of new energy output, transmission power limit of transmission section, and operating parameters of each generator set; the operating parameters of the generator set include rated capacity, minimum technical output ratio, maximum technical output ratio, ramp-up rate, ramp-down rate, minimum start-up time, minimum shutdown time, single linear operation coal consumption coefficient, and coal consumption requirement for each start and shutdown; the model output includes sequential operation information of the target planning period;

[0047] The energy storage planning capacity for the target planning period is calculated based on the time series operation information.

[0048] The transmission section information is used to split the power system so as to cluster the generator sets belonging to the same regional power grid and obtain the operating parameters of the cluster units.

[0049] Optionally, the objective function of the daily rolling power system time series production simulation model is expressed as:

[0050] Where F represents the total operating cost of the power system, K represents the number of regional power grids in the system, and T represents the total operating time. represents the power generation cost of thermal power units; represents the startup cost of thermal power units; represents the shutdown cost of thermal power units; θ S represents the penalty for abandoning light; θ W Indicates wind curtailment penalty; p S,k (t) represents the actual power generation of the photovoltaic power station; p W,k (t) represents the actual power generation of the wind farm; They represent the maximum power generation of photovoltaic and wind power converted from meteorological data respectively;

[0051] The constraints for solving the objective function include:

[0052] (1) Power balance constraints:

[0053]

[0054] Among them, p G,j (t) is the actual output of the thermal power unit; T I,k (t), T O,k (t) are the power flowing into and out of the k-th regional power grid tie line; p L,k (t) is the power load of the regional power grid;

[0055] (2) Backup constraints:

[0056]

[0057] Among them, u i The start and stop status of the unit. The theoretical maximum output of the thermal power unit is 1 when the unit is in grid-connected state, otherwise it is 0; ε W,k , ε S,k are the maximum prediction errors of wind power and photovoltaic power stations in regional power grid k; η L,k is the load reserve factor, generally taken as 5%;

[0058] (3) Grid constraints:

[0059] For interconnected regional grids, there are:

[0060]

[0061] Among them, p i,j (t) represents the exchange power on the tie line, Indicates the line transmission power limit;

[0062] (4) Generator output constraints:

[0063]

[0064]

[0065]

[0066] in, and p G,i (t) are the rated capacity and minimum technical output of the thermal power units respectively;

[0067] (5) Climbing constraints:

[0068]

[0069]

[0070] Among them, R U,i 、R D,i They are the unit's power ramp-up capability and power ramp-down capability per unit time, respectively, and M is a large constant;

[0071] (6) Upper limit constraint on power on / off:

[0072]

[0073]

[0074] Among them, S U,i 、S D,i They are the upper power per unit value at the time of unit startup and the upper power per unit value at the time of unit shutdown;

[0075] (7) Minimum start and stop time constraints:

[0076]

[0077]

[0078] Among them, T U,i 、T D,i They are the minimum operating time and the minimum shutdown time of the unit respectively;

[0079] (8) Output constraints of cogeneration units:

[0080]

[0081]

[0082] Among them, h i (t) is the per unit value of heat load; a i 、b i is the maximum main steam pressure limit parameter; c i d i It is the minimum steam pressure limit parameter of the low-pressure cylinder.

[0083] Optionally, when solving the power system time sequence production simulation model based on daily rolling, a time domain decomposition method and a no-solution automatic rollback method are used for solving.

[0084] The above technical solution, based on the daily rolling power system time-series production simulation model, aims to minimize system operating costs (including power curtailment costs), carefully considers system operating constraints, and evaluates indicators such as the new energy consumption rate. It can accurately solve the new energy consumption value under a specific energy storage planning capacity.

[0085] Optionally, the iterative optimization of the energy storage planning capacity according to the set new energy consumption value boundary adopts a binary iteration method, including:

[0086] S41, determining a preset target value k0% of the new energy consumption rate;

[0087] S42, based on the energy storage planning capacity reference value M during the target planning period (0) , using the power system time series production simulation model based on daily rolling to calculate the corresponding new energy consumption value, and calculating the new energy consumption rate k% according to the new energy consumption value;

[0088] If k% ≠ k0%, then determine the adjustment range of the energy storage planning capacity according to the size relationship between k% and k0%: If k% < k0%, then the energy storage planning capacity range is [D (0) , U (0) , where U (0) = M (0) , D (0) = 1 / 2M (0) ; If k% > k0%, then in the energy storage planning capacity range [D (0) , U (0) , D (0) = M (0) , U (0) = 2M (0) ; Go to step S43 for iterative optimization of the energy storage planning capacity;

[0089] If k% = k0%, then stop the iteration and take M (0) as the optimal energy storage planning capacity;

[0090] S43, in the i-th iteration, update the energy storage capacity planning value according to M (i) = (D (i-1) + U (i-1) ) / 2, substitute the current energy storage capacity planning value M (i) into the daily-rolling-based power system time-series production simulation model, obtain the corresponding new energy consumption value, calculate the new energy consumption rate k i %, and compare the new energy consumption rate k i % with k0%:

[0091] a) If k i % > k0%, then update the energy storage planning capacity range to [D (i) , U (i) , where U (i) = M (i) , D (i) = D (i-1) , and return to step S43 for the (i + 1)-th iteration;

[0092] b) If k i ]>% < k0%, then update the energy storage planning capacity range to [D (i) , U (i) , where U (i) = U (i-1) , D (i) = M (i) , and return to step S43 for the (i + 1)-th iteration;

[0093] c) If k i % = k0% then stop the iteration and take the current energy storage planning capacity value M (i)As the optimal energy storage planning capacity;

[0094] S44: Determine the optimal energy storage planning capacity as the energy storage planning configuration for the target planning period.

[0095] In a second aspect, the present invention provides an energy storage capacity optimization configuration device based on power system time series production simulation, which is characterized by comprising:

[0096] A first power system time sequence production simulation model determination module is configured to determine a power system time sequence production simulation model based on unit aggregation;

[0097] The energy storage planning capacity reference value calculation module is configured to use the power system sequential production simulation model based on unit aggregation to simulate the power system sequential operation process during the target planning period with the optimization goals of minimizing system operating costs and maximizing renewable energy output, and obtain the energy storage planning capacity reference value during the target planning period;

[0098] A second power system time sequence production simulation model determination module is configured to determine a power system time sequence production simulation model based on a daily rolling basis, for solving a new energy consumption value under a specified energy storage planning capacity;

[0099] An iterative optimization module is configured to iteratively optimize the energy storage planning capacity according to a set new energy consumption value boundary, so that the new energy consumption rate corresponding to the energy storage planning capacity approaches a preset new energy consumption rate expected value: based on the energy storage planning capacity reference value of the target planning period, in each iteration, adjust the energy storage planning capacity, and calculate the corresponding new energy consumption value based on the adjusted energy storage planning capacity using the power system time series production simulation model based on a daily rolling basis;

[0100] Furthermore, the energy storage planning capacity determination module is configured to use the optimal energy storage planning capacity obtained through iterative optimization as the energy storage planning capacity for the target planning period.

[0101] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for optimizing energy storage capacity configuration based on power system sequential production simulation as described in the first aspect is implemented.

[0102] Beneficial effects

[0103] The energy storage capacity optimization method of the present invention: by constructing cluster units and continuously processing integer variables that describe the timing operation status of the cluster units, a mathematical model describing the timing operation characteristics of the cluster units is established, and a power system timing production simulation model based on unit aggregation is established; then, with minimizing system operating costs and maximizing new energy grid-connected power generation as optimization goals, rapid calculation of medium- and long-term timing production simulation of the regional power system is achieved, and the energy storage planning capacity for the entire target year is roughly solved; then, system operation constraints are refined, a power system timing production simulation model based on daily rolling is constructed, and new energy consumption is evaluated so as to accurately solve the new energy consumption value under a specific energy storage planning capacity; finally, based on the roughly solved energy storage planning capacity, the above two timing production simulation models are used for iterative optimization, and the energy storage capacity is adjusted with the set new energy consumption boundary as the target, so that the system gradually approaches the preset new energy consumption rate, and finally the optimal energy storage capacity configuration for the target planning period is obtained, which can optimize the energy storage capacity configuration and improve the new energy consumption capacity and operation stability.

[0104] At the same time, the present invention adopts the binary method for iterative optimization, which can quickly and accurately find the energy storage capacity configuration that meets the requirements of new energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1 FIG. 1 is a schematic diagram of an implementation flow of the method of the present invention in one embodiment;

[0106] Figure 2 Shown is a schematic diagram of the operating status of cluster units considering the operating status of a single unit;

[0107] Figure 3 Shown is a schematic diagram of the operating status of cluster units without considering the operating status of individual units;

[0108] Figure 4 Shown is a schematic diagram of the algorithm flow for solving the time-series production simulation model. DETAILED DESCRIPTION

[0109] The following is a further description with reference to the accompanying drawings and specific embodiments.

[0110] The technical concept of the present invention is as follows: based on the power system time-series production simulation model of unit aggregation, with minimizing system operating costs and maximizing renewable energy grid-connected power generation as optimization goals, on the basis of traditional power planning and load forecasting results, simplifying the consideration of system operating constraints, and conducting rough energy storage capacity planning; according to the energy storage capacity planning results, taking into account the system operating constraints in a refined manner, establishing a power system time-series production simulation model based on daily rolling to evaluate renewable energy consumption, and based on the rough energy storage capacity planning results, iterative optimization is carried out through bisection method to quickly and accurately find the energy storage capacity configuration that meets the requirements of renewable energy consumption.

[0111] Example 1

[0112] This embodiment introduces a method for optimizing energy storage capacity configuration based on power system time-series production simulation, including:

[0113] Determine the power system sequential production simulation model based on unit aggregation;

[0114] Using the power system sequential production simulation model based on unit aggregation, with minimizing system operating costs and maximizing renewable energy output as optimization goals, the power system sequential operation process during the target planning period is simulated and calculated to obtain a reference value for energy storage planning capacity during the target planning period;

[0115] Determine a daily rolling power system time-series production simulation model to solve the renewable energy consumption value under a specified energy storage planning capacity;

[0116] Iteratively optimizing the planned energy storage capacity according to the set renewable energy consumption value boundary so that the renewable energy consumption rate corresponding to the planned energy storage capacity approaches a preset expected renewable energy consumption rate: adjusting the planned energy storage capacity in each iteration based on a reference value of the planned energy storage capacity for the target planning period, and calculating the corresponding renewable energy consumption value based on the adjusted planned energy storage capacity using the daily rolling power system time series production simulation model;

[0117] The optimal energy storage planning capacity obtained through iterative optimization is used as the energy storage planning capacity for the target planning period.

[0118] The specific implementation of this embodiment refers to Figure 1 , specifically involving the following contents.

[0119] 1. Constructing a power system time-series production simulation model based on unit aggregation

[0120] In this part, this embodiment adopts a cluster thermal power unit construction method, uses continuous variables to represent the aggregation effect of the single-period operating status of multiple units, and establishes a mathematical model that describes the timing operation characteristics such as cluster unit output power, ramping, start and stop time, thereby establishing a power system timing production simulation model based on unit aggregation.

[0121] When constructing clusters of thermal power units, the grid topology must be ignored, assuming that the generators and loads are connected to the same busbar. Given that the transmission capacity constraints faced by power system operations primarily occur on a small number of transmission sections, rather than on all transmission lines, it is necessary to partition the regional power system based on transmission sections, ignoring the grid structure of the resulting regional power system. The thermal power units within each regional power system can then be clustered separately, while retaining the constraints imposed by inter-regional transmission sections.

[0122] Cluster thermal power generation unit j is composed of J thermal power generation units with the same or similar rated capacity and operating characteristics belonging to the same partition subsystem. For a single thermal power generation unit in cluster thermal power generation unit j, the grid connection state variable x i (t), start state variable u i (t) and shutdown state variable d i (t) These three binary integer variables describe the operating status of a single thermal power unit at time t:

[0123]

[0124] Among them, x i u (t) is equal to 1 or 0, indicating that unit i is in grid-connected operation or offline state at time t; i (t) equal to 1 means that unit i starts at time t and switches from offline state to grid-connected operation state; d i u(t) is equal to 1, indicating that unit i is shut down at time t and switches from grid-connected operation to offline state; i (t) and d i (t) equal to 0 means that the operating status of unit i has not changed at time t.

[0125] If any unit in the cluster thermal power unit j changes its operating state at time t, the overall external operating capacity of the cluster unit will change. Therefore, the operating capacity of the unit can be used to represent the operating state of the cluster thermal power unit. Startup capacity and outage capacity Variable. The mathematical description is as follows:

[0126]

[0127]

[0128]

[0129] in, Represents the rated capacity of unit i. The startup, start-up and shutdown capacity variables satisfy:

[0130]

[0131] According to the above formula, the startup capacity Startup capacity and outage capacity is a discrete variable whose value is determined by the operating status of each unit in the cluster thermal power unit j at time t. Startup capacity and outage capacity It accurately describes the effect of the aggregation of the operating status of J units, but these three discrete variables can only be calculated based on the operating status of each unit at time t and cannot be used for direct decision-making.

[0132] Therefore, it is assumed that cluster thermal power unit j consists of J equivalent units, and an integer variable x is introduced j (t),u j (t) and d j (t) is used to describe the number of units in grid-connected operation, startup and shutdown states at time t. Startup capacity and outage capacity Discrete variables can be approximately described as:

[0133]

[0134]

[0135]

[0136] Among them, equivalent unit capacity:

[0137]

[0138] The introduced integer variable x j (t),u j (t) and d j (t) replaces the binary variable, and the number of integer variables describing the operating status of cluster thermal power unit j at time t can be reduced from 3*J to 3. Using integer variables to describe the number of units in the cluster thermal power unit that are in the on state at a certain operating time can approximately represent the discrete operating status of the cluster thermal power unit, such as Figure 2 and Figure 3 This modeling method considers the cluster units as an aggregation of J equivalent units, and then determines the startup capacity of the cluster thermal power units.

[0139] Since the change of the start-up and shutdown status of the units will directly affect the change of the overall startup capacity of the cluster units, if the operating status of each unit is not considered, the startup capacity of the total capacity of the cluster thermal power unit j at time t can be directly regarded as the decision variable. Figure 2 In this method, a continuous variable is introduced to describe the startup capacity in the total capacity of the cluster thermal power units to approximately characterize the operating status of the cluster thermal power units. It means that at time t, the total capacity of cluster unit j is The units are connected to the grid and can participate in system power balance and provide system backup. It means that from time t-1 to time t, the total capacity is The units need to be started and their operating status is changed from offline to grid-connected. It means that from time t-1 to time t, the total capacity is The unit needs to be shut down and the operating state changes from the grid-connected state to the offline state. The value range of the continuous variable describing the operating state of the cluster thermal power unit is:

[0140]

[0141] The total capacity of the cluster thermal power units is S j is the sum of the rated capacities of all units, The change of the startup capacity of cluster thermal power unit j between adjacent moments can be expressed as:

[0142]

[0143] The left and right sides of the equality constraint represent the capacity of the unit that is continuously running from time t-1 to time t. The unit corresponding to this capacity does not change its operating state between these two times. It is both the offline capacity of the unit at time t-1 and the operating capacity of the unit at time t. On the contrary, the described part belongs to the unit's operating capacity at time t-1 and the offline capacity at time t. If the operating capacity of cluster thermal power unit j at time t is different from the operating capacity at time t-1, it indicates that a unit start-up and shutdown event has occurred.

[0144] Continuous variables are used to model the startup capacity, start-up capacity and shutdown capacity of cluster units. The cluster units consisting of J units are regarded as one unit. It is assumed that the operating status of this unit is not limited to the shutdown "0" and grid-connected "1", but can have several operating states between 0 and 1. The continuous unit startup capacity is used to represent the continuously changing operating state.

[0145] As described above, when establishing a power system timing production simulation model based on unit aggregation, this embodiment can effectively reduce the number of variables through the cluster unit construction method, and the cluster unit operating status, output power, climbing constraints and minimum start and stop time constraints established based on the continuous variable of the cluster unit start-up capacity can accurately simulate the timing operation process of the cluster unit.

[0146] 2. Conduct a power system time-series production simulation based on unit aggregation and output the energy storage planning capacity for the target planning period, such as the target year.

[0147] The power system sequential production simulation model based on unit aggregation only considers the key transmission sections of the interconnected power grid (inter-provincial and inter-regional interconnection channels and restricted lines in the internal grid), and does not consider other detailed power grid topology structures.

[0148] The objective function is to accept as much renewable energy output as possible with the minimum system operating cost, that is:

[0149]

[0150] Among them, the total operating cost is the power generation cost of all cluster units Startup costs and downtime costs The sum of θ is the penalty coefficient for the power output of renewable energy; represents the renewable energy output at time t in the regional power grid k, which is defined as the renewable energy output that cannot be connected to the grid:

[0151]

[0152] in, represents the maximum power that can be generated by renewable energy in regional power grid k at time t, represents the power generation of renewable energy in regional power grid k at time t.

[0153] The constructed power system timing production simulation model based on unit aggregation takes minimizing system operating costs and maximizing renewable energy grid-connected power generation as optimization goals. It simulates the timing operation process of large-scale power systems under the constraints of power balance, backup demand, renewable energy output and unit operating characteristics, and realizes rapid calculation of medium- and long-term timing production simulation of regional power systems.

[0154] When performing a time-series production simulation based on unit aggregation for large-scale power systems, the input parameters include: the time-series load power curve, the time-series maximum renewable energy output curve, the load reserve factor that meets reliability requirements, the renewable energy output reserve factor, the transmission power limit of the transmission section, and the operating parameters of each generator unit (including rated capacity, minimum technical output ratio, maximum technical output ratio, ramp-up rate, ramp-down rate, minimum start-up time, minimum shutdown time, single-stage linear operation coal consumption coefficient, and coal consumption requirements for each start and shutdown). Transmission section information is used to decompose the power system so that generator units belonging to the same regional power grid can be clustered and the operating parameters of the clustered units can be obtained. The output includes time-series operation information, which can be used to roughly solve the planned energy storage capacity for the target year.

[0155] 3. Constructing a daily rolling power system time-series production simulation model

[0156] The core of sequential production simulation is the unit commitment model, which is usually modeled as a mixed integer linear programming model. A unit commitment model with a time step of one hour is used.

[0157] The objective function is for the dispatching agency to arrange the start-up and shutdown status and output of all units in order to minimize the total operating cost of the system. In addition, to promote the consumption of new energy, the objective function usually also includes penalties for curtailing wind and solar power.

[0158]

[0159] Where F represents the total operating cost of the power system, K represents the number of regional power grids in the system, and T represents the total operating time. When the time step is 1 hour, T = 8760 (8784 in leap years). represents the power generation cost of thermal power units; represents the startup cost of thermal power units; represents the shutdown cost of thermal power units; θ S represents the penalty for abandoning light; θ W Indicates wind curtailment penalty; p S,k (t) represents the actual power generation of the photovoltaic power station; p W,k (t) represents the actual power generation of the wind farm; They represent the maximum power generation of photovoltaic and wind power converted from meteorological data respectively;

[0160] Constraints include:

[0161] (1) Power balance constraints

[0162]

[0163] Among them, p G,j (t) is the actual output of the thermal power unit; T I,k (t), T O,k (t) are the power flowing into and out of the k-th regional power grid tie line; p L,k (t) is the power load of the regional power grid.

[0164] (2) Backup constraints

[0165]

[0166] Among them, u i The start and stop status of the unit. is the theoretical maximum output of the thermal power unit, which is 1 when the unit is in grid-connected state, otherwise it is 0; ε W,k , ε S,k are the maximum prediction errors of wind power and photovoltaic power stations in regional power grid k; η L,kIt is the reserve factor of the load, which is generally taken as 5%.

[0167] (3) Grid constraints

[0168] For interconnected subsystems, the exchange power on the interconnection line shall not exceed the line transmission power limit.

[0169]

[0170] Among them, p i,j (t) represents the exchange power on the tie line, Indicates the line transmission power limit.

[0171] (4) Generator output constraints

[0172]

[0173]

[0174]

[0175] in, p G，i —respectively, they are the rated capacity and minimum technical output of thermal power units.

[0176] (5) Climbing constraints

[0177]

[0178]

[0179] Among them, R U,i 、R D,i They are the unit's power ramp-up capability and power ramp-down capability per unit time, respectively, and M is represented by a large constant.

[0180] (6) Upper power limit for power on / off

[0181]

[0182]

[0183] Among them, S U,i 、S D,i They are the upper power limit per unit value when the unit is turned on and the upper power limit per unit value when the unit is turned off.

[0184] (7) Minimum start-stop time constraint

[0185]

[0186]

[0187] Among them, T U,i 、T D,i They are the minimum running time and minimum shutdown time of the unit respectively.

[0188] (8) Output constraints of cogeneration units

[0189]

[0190]

[0191] Among them, h i (t) is the per unit value of heat load, a i 、b i is the maximum main steam pressure limit parameter; c i d i It is the minimum steam pressure limit parameter of the low-pressure cylinder.

[0192] Due to the limitations of the scale of mixed linear programming problems, a single calculation cannot be used to simulate the annual operation of a regional power system containing hundreds of generators. Therefore, a daily rolling simulation is currently commonly used for power system sequential operation simulations. To improve the speed of sequential operation simulations and avoid the problem of being unable to obtain simulation results due to rolling failures, a time-domain decomposition technique and an automatic rollback technique with no solution were proposed to solve the power system sequential operation model.

[0193] Among them, the time series decomposition method is to decompose the full-year simulation into multiple periods for parallel operation. The automatic rollback technology is that when the rolling solution encounters no solution, it does not directly exit the solution process, but incorporates the previous simulation period into the simulation. If there is still no solution, continue to roll back. When the rollback simulation result is feasible, the simulation result obtained by rolling back is used to overwrite the original simulation result of the previous period. The specific solution process is as follows Figure 4 .

[0194] According to the energy storage capacity planning plan, with the goal of minimizing system operating costs (including power curtailment costs), system operating constraints are carefully considered, indicators such as the new energy consumption rate are evaluated, and new energy consumption is evaluated to accurately solve the new energy consumption value under a specific energy storage planning capacity.

[0195] 4. Based on the two models constructed in Parts 1 and 3, iterative optimization of energy storage planning capacity is performed to obtain the optimal energy storage optimization configuration plan for the target planning period.

[0196] The technical concept of this part is as follows: based on the reference value of the energy storage planning capacity roughly obtained in the second part, through the model constructed in the third part, the new energy consumption obtained from the time-series production simulation of the power system under a specific energy storage planning capacity is iteratively optimized using the bisection method. Considering using a method combining time-series operation simulation and iterative solution to solve the model, the optimal energy storage capacity configuration plan is obtained. The solution steps are roughly as follows: taking the set new energy consumption boundary as the goal, adjusting the energy storage capacity to make the system gradually approach the preset new energy consumption rate, and finally obtaining the optimal energy storage capacity configuration.

[0197] Specifically: First, determine the upper and lower limits of the energy storage planning capacity based on the roughly calculated reference value of the energy storage capacity; second, successively bisect and search within the effective interval of the energy storage planning capacity, and substitute the newly found value back into the time-series production simulation of the power system for optimization calculation; finally, by continuously iteratively optimizing the energy storage capacity, the actual new energy consumption rate can exactly meet the preset new energy consumption rate target, and the solution process ends.

[0198] In this embodiment, the iterative optimization of the energy storage planning capacity is carried out according to the set new energy consumption value boundary, and the bisection iteration method is adopted, including:

[0199] S41, determine the preset new energy consumption rate target value k0%;

[0200] S42, according to the reference value M of the energy storage planning capacity of the target planning period roughly calculated in the second part (0) [[ID= 14]], calculate the corresponding new energy consumption value using the time-series production simulation model of the power system based on daily rolling, and calculate the new energy consumption rate k% according to the new energy consumption value;

[0201] If k%≠k0%, then according to the size relationship between k% and k0%, determine the adjustment interval of the energy storage planning capacity: if k%<k0%, then the energy storage planning capacity adjustment interval is [D (0) , U (0) , where U (0) = M (0) , D (0) = 1 / 2M (0) ; if k%>k0%, then in the energy storage planning capacity interval [D (0) , U (0) , D (0) = M (0) , U (0) = 2M (0) ; go to step S43 for iterative optimization of the energy storage planning capacity;

[0202] If k% = k0%, stop the iteration and take M (0) as the optimal energy storage planning capacity;

[0203] S43. In the i-th iteration, according to M (i) =(D (i-1) +U (i-1) ) / 2 to update the energy storage capacity planning value. Substitute the current energy storage capacity planning value M (i) into the power system time-series production simulation model based on daily rolling to obtain the corresponding new energy consumption value. Calculate the new energy consumption rate k i %. Compare the new energy consumption rate k i % with k0%:

[0204] a) If k i % > k0%, then update the energy storage planning capacity interval to [D (i) , U (i) , where U (i) = M (i) , D (i) = D (i-1) . Return to step S43 for the (i + 1)-th iteration;

[0205] b) If k i % < k0%, then update the energy storage planning capacity interval to [D (i) , U (i) , where U (i) = U (i-1) , D (i) = M (i) . Return to step S43 for the (i + 1)-th iteration;

[0206] c) If k i % = k0%, then stop the iteration and take the current energy storage planning capacity value M (i) as the optimal energy storage planning capacity;

[0207] S44. Determine the optimal energy storage planning capacity as the energy storage planning configuration for the target planning period.

[0208] As above, through the dichotomy method, the energy storage capacity configuration that meets the new energy consumption requirements can be quickly and accurately found.

[0209] Embodiment 2

[0210] Based on the same inventive concept as Embodiment 1, this embodiment introduces an energy storage capacity optimization configuration device based on power system time-series production simulation, which includes:

[0211] The first power system time-series production simulation model determination module is configured to determine the power system time-series production simulation model based on unit aggregation;​​The energy storage planning capacity reference value calculation module is configured to use the power system sequential production simulation model based on unit aggregation to simulate the power system sequential operation process during the target planning period with the optimization goals of minimizing system operating costs and maximizing renewable energy output, and obtain the energy storage planning capacity reference value during the target planning period;

[0213] A second power system time sequence production simulation model determination module is configured to determine a power system time sequence production simulation model based on a daily rolling basis, for solving a new energy consumption value under a specified energy storage planning capacity;

[0214] An iterative optimization module is configured to iteratively optimize the energy storage planning capacity according to a set new energy consumption value boundary, so that the new energy consumption rate corresponding to the energy storage planning capacity approaches a preset new energy consumption rate expected value: based on the energy storage planning capacity reference value of the target planning period, in each iteration, adjust the energy storage planning capacity, and calculate the corresponding new energy consumption value based on the adjusted energy storage planning capacity using the power system time series production simulation model based on a daily rolling basis;

[0215] Furthermore, the energy storage planning capacity determination module is configured to use the optimal energy storage planning capacity obtained through iterative optimization as the energy storage planning capacity for the target planning period.

[0216] For the specific implementation of the above functional modules, please refer to the relevant content in the method of Example 1.

[0217] Example 3

[0218] This embodiment introduces a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for optimizing energy storage capacity configuration based on power system time series production simulation as described in the embodiment is implemented.

[0219] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0220] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0221] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0222] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0223] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A method for optimizing energy storage capacity configuration based on power system time series production simulation, characterized by: include: Determine the power system sequential production simulation model based on unit aggregation; Using the power system sequential production simulation model based on unit aggregation, with minimizing system operating costs and maximizing renewable energy output as optimization goals, the power system sequential operation process during the target planning period is simulated and calculated to obtain a reference value for energy storage planning capacity during the target planning period; Determine a daily rolling power system time-series production simulation model to solve the renewable energy consumption value under a specified energy storage planning capacity; Iteratively optimizing the planned energy storage capacity according to the set renewable energy consumption value boundary so that the renewable energy consumption rate corresponding to the planned energy storage capacity approaches a preset expected renewable energy consumption rate: adjusting the planned energy storage capacity in each iteration based on a reference value of the planned energy storage capacity for the target planning period, and calculating the corresponding renewable energy consumption value based on the adjusted planned energy storage capacity using the daily rolling power system time series production simulation model; The optimal energy storage planning capacity obtained through iterative optimization is used as the energy storage planning capacity for the target planning period. The method for constructing the power system time-series production simulation model based on unit aggregation includes: Classify all thermal power units in the regional power system according to unit type, capacity level and operating characteristics; Treat thermal power units with the same or similar operating characteristics as a whole to build a cluster of thermal power units; Determine the variables used to characterize the operating status of cluster thermal power units and describe the aggregation effect of the temporal operating status of multiple thermal power units.

2. The method according to claim 1, wherein: The power system time sequence production simulation model based on unit aggregation includes: A) The operating status of a single thermal power unit at time t is expressed as: (1)， Where, Indicates the grid-connected state variable, Indicates the startup state variable, Represents the shutdown state variable, Equal to 1 or 0 indicates that the state of unit i at time t is grid-connected operation or offline state; Equal to 1 means that unit i starts at time t and switches from offline state to grid-connected operation state; Equal to 1 means that unit i shuts down at time t and switches from grid-connected operation to offline state; and Equal to 0 means that the operating status of unit i has not changed at time t; B) The startup status of cluster thermal power units is represented as: (2)， (3)， (4)， Where, 、 、 They represent the startup capacity, start-up capacity and shutdown capacity variables of cluster thermal power unit j respectively; represents the number of equivalent units in the cluster thermal power units, represents the rated capacity of unit i; The startup, start-up and shutdown capacity variables satisfy: (5)， C) The discrete variables of cluster thermal power unit startup capacity, start-up capacity and shutdown capacity are approximately described as: (6)， (7)， (8)， The equivalent unit capacity is: (9)， In the formula, the integer variable 、 and They represent the number of equivalent units in grid-connected operation, startup and shutdown states at time t respectively; The value range of the continuous variable of the cluster thermal power unit operating status is expressed as: (10)， in, Indicates the total capacity of the cluster's thermal power units, which is the sum of the rated capacities of all units; The change of the startup capacity of cluster thermal power unit j between adjacent moments is expressed as: (11)。 3. The method according to claim 2, wherein: The optimization goals are to minimize system operating costs and maximize renewable energy output, and simulate the timing operation process of the power system during the target planning period. The corresponding objective function is expressed as: （12）， Where, 、 、 They represent the power generation cost, startup cost, and shutdown cost of all cluster units respectively, and the sum of the three is the total operating cost of the system; The penalty coefficient for limiting the output of new energy. represents the renewable energy power output of regional power grid k at time t in the system, and: （13）， in, represents the maximum power that can be generated by renewable energy in regional power grid k at time t, represents the power generation of renewable energy in regional power grid k at time t.

4. The method according to claim 3, wherein: When performing power system sequential production simulation based on the power system sequential production simulation model of unit aggregation, the input parameters of the model include: sequential load power curve, sequential maximum renewable energy output curve, load reserve factor that meets reliability requirements, renewable energy output reserve factor, transmission power limit of the transmission section, and operating parameters of each generator set; the operating parameters of the generator set include rated capacity, minimum technical output ratio, maximum technical output ratio, ramp-up rate, ramp-down rate, minimum start-up time, minimum shutdown time, single-stage linear operation coal consumption coefficient, and coal consumption requirements for each start and shutdown; the model output includes sequential operation information for the target planning period; The energy storage planning capacity for the target planning period is calculated based on the time series operation information.

5. The method according to claim 3, wherein: The objective function of the daily rolling power system time series production simulation model is expressed as: （14）， Where, represents the total operating cost of the power system, Indicates the number of regional power grids in the system; Indicates the total running time; represents the power generation cost of thermal power units; represents the startup cost of thermal power units; represents the shutdown cost of thermal power units; Indicates the penalty for abandoning light; It indicates the penalty for wind curtailment; Indicates the actual power generation of the photovoltaic power station; Indicates the actual power generation of the wind farm; 、 They represent the maximum power generation of photovoltaic and wind power converted from meteorological data respectively; The constraints for solving the objective function include: (1) Power balance constraints: (15)， in, is the actual output of the thermal power unit; 、 are the power flowing into and out of the k-th regional grid tie line respectively; is the power load of the regional power grid; (2) Backup constraints: (16)， in, The start and stop status of the unit. It is the theoretical maximum output of the thermal power unit; 、 are the maximum prediction errors of wind power and photovoltaic power stations in regional power grid k, respectively; is the reserve factor of the load; (3) Grid constraints: For interconnected regional grids, there are: (17)， in, represents the exchange power on the tie line, Indicates the line transmission power limit; (4) Generator output constraints: (18)， (19)， (20)， in, and are the rated capacity and minimum technical output of thermal power units respectively; (5) Climbing constraints: (21)， （22)， in, 、 They are the unit's power ramp-up capability and power ramp-down capability per unit time, is a large constant; (6) Upper limit constraint on power on / off: （23)， （24)， in, 、 They are the upper power per unit value at the time of unit startup and the upper power per unit value at the time of unit shutdown; (7) Minimum start-stop time constraint: （25)， （26)， in 、 They are the minimum operating time and the minimum shutdown time of the unit respectively; (8) Output constraints of cogeneration units: （27)， （28)， in, is the per unit value of heat load, 、 It is the maximum main steam pressure limiting parameter; 、 It is the minimum steam pressure limit parameter of the low-pressure cylinder.

6. The method according to claim 5, wherein: When solving the power system time sequence production simulation model based on daily rolling, a time domain decomposition method and a no-solution automatic rollback method are used for solving.

7. The method according to claim 1, wherein: The iterative optimization of the energy storage planning capacity according to the set new energy consumption value boundary adopts a binary iteration method, including: S41, determine the preset target value of new energy consumption rate ; S42, based on the energy storage planning capacity reference value of the target planning period , using the daily rolling power system time series production simulation model to calculate the corresponding new energy consumption value, and calculate the new energy consumption rate based on the new energy consumption value ; like , then according to and The adjustment range of energy storage planning capacity is determined based on the size relationship of , then the energy storage planning capacity range is ,in , ;like , then the energy storage planning capacity range middle, , ; Go to step S43 to perform iterative optimization of energy storage planning capacity; like , then stop the iteration and As the optimal energy storage planning capacity; S43, in In the iterations, according to Update the energy storage capacity planning value and change the current energy storage capacity planning value Substitute the above-mentioned daily rolling power system time series production simulation model to obtain the corresponding new energy consumption value, and calculate the new energy consumption rate based on the new energy consumption value , the new energy consumption rate and For comparison: a) If , then update the energy storage planning capacity range to ,in, , , return to step S43 to perform the iterations; b) If , then update the energy storage planning capacity range to ,in, , , return to step S43 to perform the iterations; c) If Then stop the iteration and set the current energy storage planning capacity value As the optimal energy storage planning capacity; S44: Determine the optimal energy storage planning capacity as the energy storage planning configuration for the target planning period.

8. A device for optimizing energy storage capacity configuration based on power system time-series production simulation, characterized in that: include: A first power system time sequence production simulation model determination module is configured to determine a power system time sequence production simulation model based on unit aggregation; The energy storage planning capacity reference value calculation module is configured to use the power system sequential production simulation model based on unit aggregation to simulate the power system sequential operation process during the target planning period with the optimization goals of minimizing system operating costs and maximizing renewable energy output, and obtain the energy storage planning capacity reference value during the target planning period; A second power system time sequence production simulation model determination module is configured to determine a power system time sequence production simulation model based on a daily rolling basis, for solving a new energy consumption value under a specified energy storage planning capacity; An iterative optimization module is configured to iteratively optimize the energy storage planning capacity according to a set new energy consumption value boundary, so that the new energy consumption rate corresponding to the energy storage planning capacity approaches a preset new energy consumption rate expected value: based on the energy storage planning capacity reference value of the target planning period, in each iteration, adjust the energy storage planning capacity, and calculate the corresponding new energy consumption value based on the adjusted energy storage planning capacity using the power system time series production simulation model based on a daily rolling basis; and an energy storage planning capacity determination module configured to use the optimal energy storage planning capacity obtained through iterative optimization as the energy storage planning capacity for the target planning period; The method for constructing the power system time-series production simulation model based on unit aggregation includes: Classify all thermal power units in the regional power system according to unit type, capacity level and operating characteristics; Treat thermal power units with the same or similar operating characteristics as a whole to build a cluster of thermal power units; Determine the variables used to characterize the operating status of cluster thermal power units and describe the aggregation effect of the temporal operating status of multiple thermal power units.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing energy storage capacity configuration based on power system time series production simulation as described in any one of claims 1 to 7 is implemented.

Images