A method and system for optimizing configuration of energy storage power generation capacity

Abstract

A method and system for optimizing configuration of energy storage power generation capacity, comprising: inputting acquired power system parameters and annual theoretical power sequence of new energy into a pre-constructed new energy time sequence production simulation optimization model to calculate annual abandoned power sequence of new energy; calculating abandoned power consumption minimum proportion coefficient based on annual abandoned power sequence of new energy and annual theoretical power sequence of new energy; inputting abandoned power consumption minimum proportion coefficient and annual abandoned power sequence of new energy into a pre-constructed energy storage power generation capacity optimization model to obtain energy storage power generation capacity and capacity demand meeting new energy utilization rate; wherein, the energy storage power generation capacity optimization model is constructed with minimum economic cost of energy storage power generation capacity as objective function and set constraint conditions when meeting new energy utilization rate. The random fluctuation of new energy output is fully considered to avoid model solving problems caused by simultaneous optimization of new energy output, energy storage power generation capacity and other factors.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy and energy storage power generation technology, in particular to an energy storage power generation installed capacity and capacity optimization configuration method and system. BACKGROUND

[0002] After high proportion of new energy access to the power grid, the pressure of grid peak regulation and frequency regulation is increased. Large-scale grid-connected operation of new energy makes both supply and demand sides present random fluctuation characteristics. The output of conventional power sources not only follows the load change, but also balances the output fluctuation of new energy, which increases the adjustment pressure of conventional power sources and the balancing difficulty of the power grid. Electrochemical energy storage can suppress the randomness and volatility of new energy output, that is, when new energy curtailment occurs, energy storage charges, and when new energy curtailment does not occur, energy storage discharges. Configuring electrochemical energy storage is one of the important technical means to solve the problem of new energy consumption.

[0003] At present, the investment cost of electrochemical energy storage is still relatively high. When configuring electrochemical energy storage, both economic efficiency and new energy consumption should be considered, and the reasonable optimization configuration of energy storage scale should be realized under the premise of ensuring new energy utilization rate. Energy storage optimization configuration needs to determine two elements of energy storage power generation installed capacity and energy storage capacity. Energy storage power generation installed capacity is the rated power value of energy storage inverter, which represents the maximum charging and discharging power of energy storage in each period. Energy storage capacity represents the maximum value of its stored power. Energy storage power generation installed capacity and capacity have an impact on the consumption of new energy curtailment. For example, when the new energy curtailment power is large, if the energy storage power generation installed capacity is insufficient, part of the curtailment power cannot be consumed. When there is a long time curtailment, if the energy storage capacity is small, the energy storage will be full of electricity and cannot consume curtailment power. However, excessive power generation installed capacity and capacity will increase the cost of energy storage investment. Therefore, under the requirement of new energy utilization rate, a balance between energy storage power generation installed capacity and capacity needs to be found to obtain the most economical energy storage configuration scheme.

[0004] Time sequence production simulation technology is a commonly used technical method in planning problems. Through 8760h time sequence production simulation optimization simulation, the random fluctuation of new energy output, system power balance and power regulation can be fully considered. However, for large-scale power grids at the provincial level, the number of power sources is large and the structure of the power grid is complex. When using 8760h time sequence production simulation method to optimize the configuration of energy storage, many factors such as system operation, conventional power source combination, energy storage charging and discharging state and new energy consumption need to be considered. The difficulty of directly solving the model is extremely great.

[0005] To solve this problem, the existing methods usually solve it in the following ways: first, only the new energy output in each season or each month is taken as input, so that the time range considered is greatly reduced, simplifying the difficulty of solving the problem, but the new energy typical day output curve cannot fully reflect the random fluctuation of new energy output, which will affect the scientificity of the energy storage planning result. Second, a number of energy storage configuration schemes are given, and the new energy utilization rate corresponding to each configuration scheme is calculated by simulating the annual time sequence production, and then the optimal result is selected by comparing the results of each scene. However, since the energy storage power generation capacity and the capacity are configurable parameters, there are infinite energy storage configuration schemes, and since all possible schemes cannot be exhausted, this method cannot obtain the optimal configuration result. In addition, the energy storage configuration needs to optimize the power generation capacity and the capacity at the same time, and some methods use the method of giving the energy storage time to optimize the energy storage capacity, which simplifies the problem, but since the ratio between the energy storage capacity and the power generation capacity is fixed, the flexibility of the energy storage configuration scheme is greatly limited, resulting in inaccurate results. SUMMARY

[0006] In order to solve the problem that the random fluctuation of new energy output is not fully considered in the prior art, and it is difficult to obtain the optimal power generation capacity and capacity of energy storage at one time, the present application proposes an energy storage power generation capacity and capacity optimization configuration method, comprising:

[0007] The obtained power system parameters and the annual theoretical power sequence of new energy are input into the pre-constructed new energy time sequence production simulation optimization model without energy storage, and the annual abandoned power sequence of new energy is calculated;

[0008] The abandoned power consumption minimum proportion coefficient is calculated based on the annual abandoned power sequence of new energy and the annual theoretical power sequence of new energy;

[0009] The abandoned power consumption minimum proportion coefficient and the annual abandoned power sequence of new energy are input into the pre-constructed energy storage power generation capacity and capacity optimization model to obtain the energy storage power generation capacity and capacity demand that meets the new energy utilization rate;

[0010] The new energy time sequence production simulation optimization model is constructed with the maximum new energy consumption of the whole network as the optimization objective, and the power grid power balance, system reserve demand, operation of various power sources and transmission safety of cross-section as the constraint conditions;

[0011] The energy storage power generation capacity and capacity optimization model is constructed with the minimum economic cost of energy storage power generation capacity and capacity as the objective function when meeting the new energy utilization rate, and the constraint conditions set for the objective function.

[0012] Preferably, the construction of the energy storage power generation capacity and capacity optimization model comprises:

[0013] The objective function is to minimize the economic cost of energy storage power generation capacity. The objective function is to set constraints on energy storage capacity, energy storage system charge and discharge power, energy storage state of charge, energy storage duration and curtailment consumption.

[0014] An optimization model for energy storage power generation capacity is constructed using the objective function and the constraints on energy storage capacity, energy storage system charge and discharge power, energy storage state of charge, energy storage duration, and curtailment consumption.

[0015] Preferably, the step of inputting the minimum curtailment ratio coefficient and the annual curtailment sequence of new energy sources into a pre-built energy storage power generation capacity optimization model to obtain the energy storage power generation capacity and requirements that meet the utilization rate of new energy sources includes:

[0016] The annual curtailment sequence of the new energy source is input into the energy storage power generation capacity and capacity optimization model, and the optimal energy storage power generation capacity and capacity are obtained by using a mathematical programming solver.

[0017] Preferably, the annual curtailment sequence of the new energy source is calculated using the following formula:

[0018] P c ={P c (t), t=1,2,…,T}

[0019] In the formula, P c For the annual curtailment sequence of renewable energy, P c (t) represents the amount of renewable energy curtailed during time period t, where t is the time period and T is the total number of time periods throughout the year.

[0020] Preferably, the minimum ratio coefficient for power curtailment is calculated using the following formula:

[0021]

[0022] In the formula, α is the minimum proportion coefficient for power curtailment and consumption, r is the utilization rate of new energy sources, P0(t) is the theoretical power sequence, and P c (t) represents the sequence of abandoned renewable energy, and T represents the total amount of all periods throughout the year.

[0023] Preferably, the objective function of the energy storage power generation capacity optimization model is calculated using the following formula:

[0024] min c1E max +c2P max

[0025] In the formula, c1 is the cost per unit energy storage capacity, and E max c2 represents the energy storage capacity, c2 represents the unit cost of energy storage power generation, and P represents the energy storage capacity. max Installed for energy storage and power generation.

[0026] Preferably, the abandoned power consumption constraint is calculated according to the following formula:

[0027]

[0028] In the formula, P ch (t) is the charging power at time t, a is the abandoned power consumption minimum proportion coefficient, P c (t) is the abandoned power sequence of the new energy, and T is the number of all time periods in a year.

[0029] Based on the same inventive concept, the application further provides a system for optimizing configuration of energy storage power generation capacity, comprising:

[0030] a calculation sequence module, configured to input the acquired power system parameters and the annual theoretical power sequence of the new energy into a pre-constructed new energy time sequence production simulation optimization model without energy storage, and calculate an annual abandoned power sequence of the new energy;

[0031] a calculation coefficient module, configured to calculate the abandoned power consumption minimum proportion coefficient based on the annual abandoned power sequence of the new energy and the annual theoretical power sequence of the new energy;

[0032] a model solving module, configured to input the abandoned power consumption minimum proportion coefficient and the annual abandoned power sequence of the new energy into a pre-constructed energy storage power generation capacity optimization model, and obtain the energy storage power generation capacity required for meeting the new energy utilization rate;

[0033] The new energy time sequence production simulation optimization model is constructed with the maximum new energy consumption in the whole network as an optimization objective, and the power balance of the power grid, system reserve demand, operation of various power sources and transmission safety of cross-sections as constraint conditions.

[0034] The energy storage power generation capacity optimization model is constructed with the minimum economic cost of the energy storage power generation capacity as an objective function when the new energy utilization rate is met, and constraint conditions set for the objective function.

[0035] Preferably, the model solving module is specifically configured to:

[0036] input the annual abandoned power sequence of the new energy into the energy storage power generation capacity optimization model, and solve by using a mathematical programming solver to obtain the optimal energy storage power generation capacity.

[0037] Preferably, the calculation coefficient module calculates the abandoned power consumption minimum proportion coefficient according to the following formula:

[0038]

[0039] In the formula, a is the abandoned power consumption minimum proportion coefficient, r is the new energy utilization rate, P0(t) is the theoretical power sequence, and P c(t) is a new energy abandoned electricity sequence, T is the number of all time periods in a year.

[0040] Compared with the prior art, the present application has the following advantages:

[0041] A method and system for optimizing configuration of energy storage power generation capacity, comprising: inputting acquired power system parameters and a new energy annual theoretical power sequence into a pre-constructed new energy time sequence production simulation optimization model without energy storage, to calculate a new energy annual abandoned electricity sequence; based on the new energy annual abandoned electricity sequence and the new energy annual theoretical power sequence, calculating a minimum abandoned electricity consumption ratio coefficient; inputting the minimum abandoned electricity consumption ratio coefficient and the new energy annual abandoned electricity sequence into a pre-constructed energy storage power generation capacity optimization model, to obtain energy storage power generation capacity requirements that meet new energy utilization rate; wherein the new energy time sequence production simulation optimization model is constructed with maximum new energy consumption in the whole network as the optimization objective, and power balance, system reserve demand, operation of various power sources and transmission safety of cross-section as constraint conditions; the energy storage power generation capacity optimization model is constructed with minimum economic cost of energy storage power generation capacity as the objective function when meeting the new energy utilization rate, and constraint conditions set for the objective function; the present application considers random fluctuation of new energy output, and takes into account economic efficiency of energy storage investment, new energy consumption, optimization of thermal power unit combination, and model calculation efficiency and other factors, to directly obtain optimal energy storage power generation capacity and capacity. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 A flowchart of the present application for optimizing configuration of energy storage power generation capacity;

[0043] Figure 2 A flowchart of the present application for optimizing energy storage power and capacity to promote new energy consumption;

[0044] Figure 3 A new energy 8760h abandoned electricity sequence diagram of the present application;

[0045] Figure 4 A new energy abandoned electricity power and energy storage charging and discharging power diagram of the present application within 2 consecutive days before and after energy storage configuration. DETAILED DESCRIPTION

[0046] In order to realize the promotion of new energy consumption, and realize the optimal rated capacity and rated power of electrochemical energy storage, the application realizes two-step optimization, first, without considering the access of energy storage, by establishing a new energy time sequence production simulation optimization model, the new energy abandoned electricity sequence is calculated, then, based on the new energy abandoned electricity sequence, combined with the new energy utilization rate target, the electricity quantity scale of the abandoned electricity sequence which needs to be recovered and utilized by energy storage is calculated, finally, the energy storage power generation installed capacity and capacity optimization model based on new energy abandoned electricity is established, and the energy storage power generation installed capacity and capacity scale under the minimum economic cost are obtained by solving the model. In order to better understand the application, the content of the application will be further described in combination with the drawings and examples of the specification.

[0047] Embodiment 1:

[0048] A kind of energy storage power generation installed capacity and capacity optimization configuration method, its implementation process is as shown in Figure 1 , including:

[0049] Step 1, the power system parameters and new energy annual theoretical power sequence obtained are input into the new energy time sequence production simulation optimization model not containing energy storage constructed in advance, and the new energy annual abandoned electricity sequence is calculated;

[0050] Step 2, based on the new energy annual abandoned electricity sequence and the new energy annual theoretical power sequence, the lowest proportion coefficient of abandoned electricity consumption is calculated;

[0051] Step 3, the lowest proportion coefficient of abandoned electricity consumption and the new energy annual abandoned electricity sequence are brought into the pre-constructed energy storage power generation installed capacity and capacity optimization model, and the energy storage power generation installed capacity and capacity demand meeting the new energy utilization rate are obtained;

[0052] Wherein, the new energy time sequence production simulation optimization model is constructed with the maximum new energy consumption of the whole network as the optimization target, with the power balance of the grid, system standby demand, various power source operation and cross-section transmission safety as the constraint condition;

[0053] The energy storage power generation installed capacity and capacity optimization model is constructed with the minimum economic cost of energy storage power generation installed capacity and capacity as the objective function when meeting the new energy utilization rate, and the constraint condition set for the objective function.

[0054] The application of a kind of energy storage power generation installed capacity and capacity optimization configuration method will be described in detail in combination with Figure 2 .

[0055] In step 1, the power system parameters and new energy annual theoretical power sequence obtained are input into the new energy time sequence production simulation optimization model not containing energy storage constructed in advance, and the new energy annual abandoned electricity sequence is calculated, specifically including:

[0056] According to the power system parameters under a given scenario, a new energy time sequence production simulation not containing electrochemical energy storage is performed. A new energy time sequence production simulation optimization model not containing energy storage is established, and the model takes the maximum new energy consumption in the whole network as the optimization objective, considers the power balance constraint of the grid, the system reserve demand constraint, the operation constraint of various power sources, the transmission safety constraint of the section, etc.

[0057] The new energy curtailment sequence P c = P c (t), t = 1, 2, …, T} is obtained through the annual time sequence production simulation optimization calculation, wherein P c (t) is the new energy curtailment power at the t period, T is the number of all periods in a year, and P c represents the new energy annual curtailment sequence set. The annual time sequence production simulation optimization calculation can use the existing weekly calculation method to improve the solving efficiency.

[0058] In step 2, the lowest proportion coefficient of curtailment consumption is calculated based on the new energy annual curtailment sequence and the new energy annual theoretical power sequence, and specifically includes:

[0059] Based on the new energy curtailment sequence P c , the charge and discharge state sequence x = {x(t), t = 1, 2, …, T} of the energy storage at each period is obtained, and the charge and discharge state x(t) at the t moment is equal to the sign function of the curtailment power P c (t) at the t moment, as follows:

[0060] x(t) = sgn(P c (t))

[0061] Wherein, the sign function is:

[0062]

[0063] The meaning of the charge and discharge state is that when there is new energy curtailment in the system (P c (t) > 0), the charge and discharge state of the energy storage is 1, indicating that the energy storage can be charged; when there is no new energy curtailment in the system (P c (t) = 0), the charge and discharge state of the energy storage is 0, indicating that the energy storage can be discharged.

[0064] Based on the new energy curtailment sequence P c and the theoretical power sequence P0, the lowest proportion coefficient α that needs to be consumed in the new energy curtailment power to meet the current r of the new energy utilization rate is calculated:

[0065]

[0066] In step 3, the minimum curtailment ratio coefficient and the annual curtailment sequence of new energy are input into a pre-built energy storage power generation capacity optimization model to obtain the energy storage power generation capacity and requirements that meet the utilization rate of new energy. Specifically, this includes:

[0067] Based on the annual curtailment sequence of new energy sources and the minimum curtailment consumption ratio, an optimization model for energy storage power generation capacity is established to calculate the most economical power generation capacity and demand for energy storage under the condition of meeting the utilization rate of new energy sources.

[0068] The objective function of the energy storage power generation installed capacity and capacity optimization model is:

[0069] min c1E max +c2P max

[0070] In the formula: E max P represents energy storage capacity, which is an optimization variable; max c1 represents the installed capacity of energy storage power generation, which is used as an optimization variable; c2 represents the unit cost of energy storage capacity and c3 represents the unit cost of energy storage power generation.

[0071] The constraints include:

[0072] (1) Energy storage capacity constraints:

[0073] 0≤E(t)≤E max

[0074] E(t) represents the amount of energy stored at time t, and is an optimization variable. This constraint states that the amount of energy stored at time t, E(t), cannot exceed the energy storage capacity E. max .

[0075] (2) Energy storage system charging and discharging power constraints:

[0076]

[0077] P ch (t) represents the charging power of the energy storage at time t, which is an optimization variable; P dc (t) represents the discharge power of the energy storage at time t, which is an optimization variable. This constraint represents the charging power P at time t. ch (t) and discharge power P dc (t) does not exceed its installed power generation capacity P max The charging and discharging power is determined by the charging / discharging state x(t), and the charging and discharging power will not be positive simultaneously. As shown in step 2, the charging / discharging state x(t) at time t is predetermined and is a known quantity. When x(t) equals 1, the charging power P... ch (t) from 0 to P max The value can be selected at any interval, allowing charging to proceed, while the discharge power P...dc (t) is constrained to be 0; on the contrary, when x(t) is equal to 0, the discharging power P dc (t) is constrained to be 0; on the contrary, when x(t) is equal to 0, the discharging power P max (t) is constrained to be 0; on the contrary, when x(t) is equal to 0, the discharging power P ch (t) is constrained to be 0; on the contrary, when x(t) is equal to 0, the discharging power P

[0078] (3) Energy storage state of charge constraint:

[0079]

[0080] The constraint describes the relationship between the energy storage capacity of the energy storage at adjacent time and the charging and discharging power. That is: E(t) represents the energy storage capacity of the energy storage at t, which is an optimization variable; E(t-1) represents the energy storage capacity of the energy storage at t-1, which is an optimization variable; η represents the charging and discharging efficiency of the energy storage.

[0081] (4) Energy storage duration constraint:

[0082] nP max ≤E max

[0083] The constraint indicates that the duration of the energy storage should be greater than the given minimum energy storage duration, and n represents the minimum duration of the energy storage.

[0084] (5) Rejected electricity consumption constraint:

[0085]

[0086] The constraint indicates that the energy storage capacity of the energy storage in a year should be greater than the minimum proportion of new energy rejected electricity consumption.

[0087] Since the charging and discharging state of the energy storage has been determined in advance, the above-mentioned energy storage power generation installation and capacity optimization model is a linear programming model and does not contain integer variables.

[0088] Call the mathematical programming solver to solve the energy storage power generation installation and capacity optimization model to obtain the optimal power generation installation and capacity of the energy storage.

[0089] Based on a provincial power grid, the energy storage optimization configuration calculation is carried out. First, by establishing a 8760h time sequence production simulation optimization model, the 8760h rejected electricity sequence of the new energy in the whole province is obtained through optimization calculation without considering the access of the energy storage, as shown in the attached Figure 3 According to statistics, without configuring the energy storage, the new energy consumption in the whole province is 40.76 billion kilowatt-hours, the rejected electricity is 5.21 billion kilowatt-hours, and the new energy utilization rate is 88.7%. After setting the configuration of the energy storage, the new energy utilization rate target is 95%, and through calculation, it is known that at least 2.91 billion kilowatt-hours of rejected electricity needs to be consumed to meet the target of 95% of the new energy utilization rate, and the minimum proportion coefficient of the consumed new energy rejected electricity is 0.559.

[0090] The energy storage power generation equipment cost is set to 500 yuan / kW, the capacity cost is set to 2000 yuan / kWh, the charging and discharging efficiency is set to 95%, based on the annual 8760h of new energy abandoned power sequence, the energy storage power generation equipment and capacity optimization model is established, and the optimal energy storage power generation equipment is obtained by optimization solution. 2924MW, the optimal capacity is 11221MWh, and the converted energy storage time is 3.8h. Figure 4 The new energy abandoned power before and after the configuration of energy storage within 2 consecutive days is shown, and it can be found that the energy storage charges during the new energy abandoned power period and discharges during the non-abandoned power period, which realizes the consumption of new energy abandoned power. According to statistics, the production simulation calculation time without configuring energy storage is 8.7 minutes, the calculation time of the energy storage power generation equipment and capacity optimization model is 0.75 minutes, and the total time length can meet the engineering practicability demand.

[0091] Embodiment 2:

[0092] An energy storage power generation equipment and capacity optimization configuration system, comprising:

[0093] A calculation sequence module is configured to input the acquired power system parameters and the annual theoretical power sequence of new energy into a pre-constructed new energy time sequence production simulation optimization model without energy storage, and calculate the annual abandoned power sequence of new energy;

[0094] A calculation coefficient module is configured to calculate the minimum abandoned power consumption ratio coefficient based on the annual abandoned power sequence of new energy and the annual theoretical power sequence of new energy;

[0095] A model solution module is configured to input the minimum abandoned power consumption ratio coefficient and the annual abandoned power sequence of new energy into a pre-constructed energy storage power generation equipment and capacity optimization model, and obtain the energy storage power generation equipment and capacity demand that meets the new energy utilization rate;

[0096] The new energy time sequence production simulation optimization model is constructed with the maximum new energy consumption of the whole network as the optimization objective, and the power balance of the grid, the system reserve demand, the operation of various power sources and the safety of the cross-section transmission as the constraint conditions;

[0097] The energy storage power generation equipment and capacity optimization model is constructed with the minimum economic cost of energy storage power generation equipment and capacity as the objective function when meeting the new energy utilization rate, and the constraint conditions set for the objective function.

[0098] The calculation sequence module is specifically configured to:

[0099] According to the power system parameters under a given scenario, the new energy time sequence production simulation not containing electrochemical energy storage is carried out. The new energy time sequence production simulation optimization model not containing energy storage is established, and the model takes the maximum new energy consumption of the whole network as the optimization objective, considers the power balance constraint of the grid, the system reserve demand constraint, the operation constraint of various power sources, the transmission safety constraint of the section, etc.

[0100] The new energy abandoned power sequence P c ={P c (t),t=1,2,…,T} is obtained through the annual time sequence production simulation optimization calculation by taking the annual theoretical power sequence P0 of new energy P0={P0(t),t=1,2,…,T}, power load, grid and power source parameters as inputs, wherein P c (t) is the abandoned power of new energy at t period, T is the number of all periods in a year, and P c represents the set of the annual abandoned power sequence of new energy. The annual time sequence production simulation optimization calculation can adopt the existing weekly calculation and the like to improve the solving efficiency.

[0101] The calculation coefficient module is used for:

[0102] Based on the new energy abandoned power sequence P c , the charge and discharge state sequence x of the energy storage at each period is obtained, and the charge and discharge state x(t) at t time is equal to the sign function of the abandoned power P c (t) at t time, as follows:

[0103] x(t)=sgn(P c (t))

[0104] The sign function is as follows:

[0105]

[0106] The meaning of the charge and discharge state is that when there is new energy abandoned power in the system (P c (t)>0), the charge and discharge state of the energy storage is 1, indicating that the energy storage can be charged; when there is no new energy abandoned power in the system (P c (t)=0), the charge and discharge state of the energy storage is 0, indicating that the energy storage can be discharged.

[0107] Based on the new energy abandoned power sequence P c and the theoretical power sequence P0, the minimum proportion coefficient a needed to consume in the new energy abandoned power amount to meet the current r of the new energy utilization rate is calculated:

[0108]

[0109] The model solving module is specifically used for:

[0110] Based on the annual sequence of abandoned electricity of new energy and the minimum coefficient of abandoned electricity consumption, an optimization model of energy storage power generation capacity is established to calculate the most economical energy storage power generation capacity and capacity demand under the condition of meeting the utilization rate of new energy.

[0111] The objective function of the energy storage power generation capacity optimization model is:

[0112] min c1E max +c2P max

[0113] In the formula: E max represents the energy storage capacity, which is an optimization variable; P max represents the energy storage power generation capacity, which is an optimization variable; c1 represents the unit cost of energy storage capacity, and c2 represents the unit cost of energy storage power generation capacity.

[0114] The constraint conditions include:

[0115] (1) Energy storage capacity constraint:

[0116] 0≤E(t)≤E max

[0117] E(t) is the energy storage capacity at time t, which is an optimization variable. This constraint indicates that the energy storage capacity E(t) at time t cannot be greater than the energy storage capacity E max .

[0118] (2) Energy storage system charging and discharging power constraint:

[0119]

[0120] P ch (t) represents the charging power of the energy storage at time t, which is an optimization variable; P dc (t) represents the discharging power of the energy storage at time t, which is an optimization variable. This constraint indicates that the charging power P ch (t) and the discharging power P dc (t) at time t cannot exceed the power generation capacity P max , and are determined by the charging and discharging state x(t), and the charging and discharging power cannot be positive at the same time. As known from step 2, the charging and discharging state x(t) of the energy storage at time t has been determined in advance, which is a known quantity. When x(t) is equal to 1, the charging power P ch (t) takes a value between 0 and P max , i.e., charging can be performed, and the discharging power P dc (t) is constrained to be 0; conversely, when x(t) is equal to 0, the discharging power P dc (t) takes a value between 0 and P max , and the charging power P ch (t) is constrained to be 0.

[0121] (3) Energy storage state of charge constraint:

[0122]

[0123] The constraint describes the relationship between the energy storage capacity of the energy storage at adjacent time and the charging and discharging power. That is, E(t) represents the energy storage capacity of the energy storage at t, which is an optimization variable; E(t-1) represents the energy storage capacity of the energy storage at t-1, which is an optimization variable; and η represents the charging and discharging efficiency of the energy storage.

[0124] (4) Energy storage duration constraint:

[0125] nP max ≤E max

[0126] The constraint indicates that the duration of the energy storage should be greater than the given minimum energy storage duration, and n represents the minimum duration of the energy storage.

[0127] (5) Spillage consumption constraint:

[0128]

[0129] The constraint indicates that the energy storage capacity of the energy storage in a year should be greater than the minimum proportion of new energy spillage consumption.

[0130] Since the charging and discharging state of the energy storage has been determined in advance, the above-mentioned energy storage power generation installation and capacity optimization model is a linear programming model and does not contain integer variables.

[0131] A mathematical programming solver is called to solve the energy storage power generation installation and capacity optimization model to obtain the optimal power generation installation and capacity of the energy storage.

[0132] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. 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 disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0133] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps in one or more flowcharts and / or blocks

[0134] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps in one or more flowcharts and / or blocks

[0135] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps in one or more flowcharts and / or blocks

[0136] The above merely provides an embodiment of the present application, but is not intended to limit the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall fall within the scope of the present application.

Claims

1. A method for optimizing the configuration of energy storage power generation capacity, characterized in that, include: The acquired power system parameters and the theoretical annual power sequence of new energy sources are input into a pre-constructed new energy time-series production simulation optimization model that does not include energy storage, and the annual power curtailment sequence of new energy sources is calculated. The minimum curtailment ratio coefficient is calculated based on the annual curtailment sequence of the new energy source and the annual theoretical power sequence of the new energy source. By inputting the minimum curtailment ratio coefficient and the annual curtailment sequence of new energy into a pre-built energy storage power generation capacity and capacity optimization model, the energy storage power generation capacity and capacity requirements that meet the utilization rate of new energy are obtained. The new energy time-series production simulation optimization model is constructed with the goal of maximizing the consumption of new energy across the entire network, and with constraints such as grid power balance, system reserve requirements, operation of various power sources, and cross-sectional transmission safety. The energy storage power generation installation and capacity optimization model is constructed with the objective function of minimizing the economic cost of energy storage power generation installation and capacity while satisfying the utilization rate of new energy sources, and with the constraints set for the objective function.

2. The method according to claim 1, characterized in that, The construction of the energy storage power generation installed capacity and capacity optimization model includes: The objective function is to minimize the economic cost of energy storage power generation capacity. The objective function is to set constraints on energy storage capacity, energy storage system charging and discharging power, energy storage state of charge, energy storage duration, and curtailment consumption. An optimization model for energy storage power generation capacity is constructed using the objective function and the constraints on energy storage capacity, energy storage system charge and discharge power, energy storage state of charge, energy storage duration, and curtailment consumption.

3. The method according to claim 1, characterized in that, The process involves inputting the minimum curtailment ratio coefficient and the annual curtailment sequence of new energy sources into a pre-built energy storage power generation capacity optimization model to obtain the energy storage power generation capacity and requirements that meet the utilization rate of new energy sources, including: The annual curtailment sequence of the new energy source is input into the energy storage power generation capacity and capacity optimization model, and the optimal energy storage power generation capacity and capacity are obtained by using a mathematical programming solver.

4. The method according to claim 1, characterized in that, The annual curtailment sequence of new energy sources is calculated using the following formula: P c ={P c (t),t=1,2,…,T} In the formula, P c For the annual curtailment sequence of renewable energy, P c (t) represents the amount of renewable energy curtailed during time period t, where t is the time period and T is the total number of time periods throughout the year.

5. The method according to claim 1, characterized in that, The minimum proportion coefficient for waste power consumption is calculated using the following formula: In the formula, α is the minimum proportion coefficient for power curtailment and consumption, r is the utilization rate of new energy sources, P0(t) is the theoretical power sequence, and P c (t) represents the sequence of abandoned renewable energy, and T represents the total amount of all periods throughout the year.

6. The method according to claim 2, characterized in that, The objective function of the energy storage power generation installed capacity and capacity optimization model is calculated using the following formula: my c1E max +c2P max In the formula, c1 is the cost per unit energy storage capacity, and E max c2 represents the energy storage capacity, c2 represents the unit cost of energy storage power generation, and P represents the energy storage capacity. max Installed for energy storage and power generation.

7. The method according to claim 2, characterized in that, The constraint on the amount of abandoned electricity to be absorbed is calculated using the following formula: In the formula, P ch (t) represents the charging power of energy storage at time t, α is the minimum proportion coefficient for power curtailment and utilization, and P c (t) represents the sequence of abandoned renewable energy, and T represents the total amount of all periods throughout the year.

8. A system for optimizing the configuration of energy storage power generation capacity, characterized in that, include: The calculation sequence module is used to input the acquired power system parameters and the annual theoretical power sequence of new energy into a pre-built new energy time-series production simulation optimization model that does not include energy storage, and calculate the annual curtailment sequence of new energy. The calculation coefficient module is used to calculate the minimum curtailment ratio coefficient based on the annual curtailment sequence of the new energy source and the annual theoretical power sequence of the new energy source. The model solving module is used to input the minimum curtailment ratio coefficient and the annual curtailment sequence of new energy into a pre-built energy storage power generation capacity and capacity optimization model to obtain the energy storage power generation capacity and capacity requirements that meet the utilization rate of new energy. The new energy time-series production simulation optimization model is constructed with the goal of maximizing the consumption of new energy across the entire network, and with constraints such as grid power balance, system reserve requirements, operation of various power sources, and cross-sectional transmission safety. The energy storage power generation installation and capacity optimization model is constructed with the objective function of minimizing the economic cost of energy storage power generation installation and capacity while satisfying the utilization rate of new energy sources, and with the constraints set for the objective function.

9. The system according to claim 8, characterized in that, The model solving module is specifically used for: The annual curtailment sequence of the new energy source is input into the energy storage power generation capacity and capacity optimization model, and the optimal energy storage power generation capacity and capacity are obtained by using a mathematical programming solver.

10. The system according to claim 8, characterized in that, The calculation coefficient module calculates the minimum curtailment and consumption ratio coefficient using the following formula: In the formula, α is the minimum proportion coefficient for power curtailment and consumption, r is the utilization rate of new energy sources, P0(t) is the theoretical power sequence, and P c (t) represents the sequence of abandoned renewable energy, and T represents the total amount of all periods throughout the year.

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