A Method for Optimal Configuration and Operation Strategy of a Distributed Energy Storage System

Abstract

The present invention discloses a method for optimizing the configuration and operation strategy of a distributed energy storage system, which includes the following steps: constructing an optimization configuration model of the distributed energy storage system, constructing an objective function with the minimum investment and operation costs of the energy storage system; constructing constraint conditions; using the second-order cone relaxation algorithm and linearizing bilinear terms to solve the model, and obtaining the optimized configuration and operation strategy of the energy storage system. The present invention optimizes the configuration of the distributed energy storage considering multiple factors such as line utilization efficiency, can improve the daily utilization rate of the distribution line, cut peak loads, and enhance the economic efficiency of the distribution network assets.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed energy storage, and more specifically, to a method for optimizing the configuration and operation strategy of a distributed energy storage system. Background Art

[0002] Problems such as the intensification of the peak-valley fluctuation characteristics of the power system load and the frequent heavy overload of lines are gradually emerging. At the distribution network level, too low line utilization rate will lead to idle assets, while too high line transmission capacity will lead to heavy overload conditions and prevent the output of distributed power sources from being connected to the grid.

[0003] The operation of the power grid faces the situation of continuously increasing maximum load and peak-valley difference, continuously increasing proportion of high-proportion distributed renewable energy access, extremely high short-term line utilization rate and low long-term line utilization rate of the distribution network. Under the influence of the above three factors of source, grid and load, there is a situation of heavy overload of power flow in the distribution network lines, which affects the reliability and economy of the operation of the distribution network. DESS (Distributed Energy Storage System) has the advantages of relatively small single-unit capacity scale, strong adaptability to the installation environment, and fast two-way regulation response. It is a key measure for the distribution network to adapt to the wide access of distributed energy and optimize network operation, and has a positive effect on peak shaving of substations and lines, power flow optimization control, etc. Therefore, reasonably arranging DESS in the distribution network has become a new way for the power grid to achieve lean investment.

[0004] The optimization of distributed energy storage configuration in the distribution network involves two aspects: capacity determination and site selection optimization. The location of distributed energy storage can be determined by minimizing the grid voltage deviation or co-locating with distributed power sources. In terms of capacity, the energy storage power can be determined according to the local power structure and the regulation ability of the external transmission channel, and the capacity can be determined according to the peak load duration. The operation strategy of the energy storage can be solved by constructing a semi-definite programming model. At present, there is less research on the alleviation of the peak shaving pressure and utilization degree of distribution and transformation equipment by grid-side energy storage. For substations, evaluation indicators can be constructed to judge whether energy storage needs to be configured, while it is more economical to configure distributed energy storage equipment for distribution lines with tight space resources. In terms of economy, energy storage equipment can bring benefits to power grid companies from two aspects: delaying power grid construction and reducing line losses. Most of the existing research focuses on the regulation effect of the investment of distributed energy storage equipment on the power grid, while less consideration is given to the utilization degree of distribution network equipment.

[0005] Under the new situation of the construction of active distribution networks, the distribution network presents new characteristics such as increased penetration rate of distributed power sources, intensified peak-valley difference of loads, local and time-periodic heavy overload of distribution and transformation equipment. Reasonably configuring distributed energy storage has become a feasible solution to optimize the service capacity of existing distribution and transformation equipment in the distribution network.

[0006] In the prior art, a method for distributing and arranging a distributed energy storage system is disclosed. The method includes: based on the obtained number of individuals of the distributed energy storage system, the maximum distance of the position movement of the distributed energy storage system in the power grid, the distance without influence between the distributed energy storage systems, the number of optional states of an individual of the distributed energy storage system within its field of vision, and the density of the number of individuals of the distributed energy storage system, using the artificial fish swarm algorithm to solve the target function preset with the minimum investment cost of the distributed energy storage system as the goal to obtain the optimal solution; arranging the system based on the optimal solution. This solution has insufficient consideration in terms of the utilization degree of distribution network equipment and cannot meet the peak shaving pressure of distribution and transformation equipment on the power grid side. Summary of the Invention

[0007] The present invention provides a method for optimizing the configuration and operation strategy of a distributed energy storage system, which overall improves the asset utilization efficiency of the distribution network.

[0008] To solve the above technical problems, the technical solution of the present invention is as follows:

[0009] A method for optimizing the configuration and operation strategy of a distributed energy storage system includes the following steps:

[0010] Construct an optimization configuration model of the distributed energy storage system, and construct a target function with the minimum investment and operation costs of the energy storage system;

[0011] Construct constraint conditions;

[0012] Use the second-order cone relaxation algorithm and linearize the bilinear terms to solve the model, and obtain the optimized configuration and operation strategy of the energy storage system.

[0013] Preferably, the construction of the target function is specifically:

[0014]

[0015] Where:

[0016] C investment,i = α(E rated,i I E +P rated,i I P )

[0017] C operation,i = C DESS E rated,i

[0018]

[0019] T rcu ≤T lmt

[0020] In the formula, C operation,iis the operating cost of energy storage system i; C investment,i is the investment cost of energy storage system i; Φ is the set of energy storage system deployment points; E rated,i is the rated capacity of energy storage system i; P rated,i is the rated power of energy storage system i; α is the annualized value coefficient converted to an annual basis; I E is the investment cost per unit capacity of the energy storage system; I P is the investment cost per unit power of the energy storage system; C DESS is the operation and maintenance cost per unit capacity of the energy storage system; T rcu is the cycle life of the energy storage device, r is the discount rate, T lmt is the floating charge life of the battery.

[0021] Preferably, when constructing the objective function, the cycle times of the energy storage device also need to be considered, specifically:

[0022]

[0023] In the formula, H life is the cycle times when the energy storage battery is retired, N0 is the cycle times when the energy storage battery is charged and discharged until retirement at 100% depth of discharge, given by the energy storage battery manufacturer, DOD cyc is the cycle discharge depth of the energy storage battery in each actual operation;

[0024] In actual operation, the cycle discharge depth of the energy storage each time is not a certain value. Convert the cycle discharge depth each time into the corresponding equivalent full cycle times:

[0025]

[0026] In the formula, k p is the curve fitting parameter of the energy storage battery cycle times, given by the energy storage battery manufacturer, DOD cyc (t) is the cycle discharge depth of the energy storage battery at the t-th time, h loop (t) is the equivalent full cycle discharge times corresponding to each charge and discharge cycle of the energy storage battery;

[0027] Then the total equivalent full cycle times generated by the energy storage device per day are:

[0028]

[0029] In the formula, H loop is the daily equivalent full cycle discharge times;

[0030] The cycle life T of the energy storage device rcu is determined by the daily equivalent full cycle times:

[0031]

[0032] Preferably, the constraint conditions include power grid power flow constraints, energy storage operation constraints, energy storage life loss operation constraints, and equipment utilization rate constraints.

[0033] Preferably, the power grid power flow constraints are specifically as follows:

[0034] Active power flow constraint equation:

[0035]

[0036] Reactive power flow constraint equation:

[0037]

[0038] Upper and lower voltage limit constraint equation:

[0039] U min ≤U i ≤U max

[0040] Upper and lower phase angle limit constraint equation:

[0041] θ min ≤θ i ≤θ max

[0042] Upper and lower line apparent power flow limit constraint equation:

[0043] P ij,min ≤P ij ≤δP ij,max

[0044] In the formula, P tot,i is the net injected active power of node i; U i is the voltage amplitude of node i; U j is the voltage amplitude of node j; θ ij is the voltage phase angle difference between node i and node j; g ij is the conductance of branch ij; b ij is the susceptance of branch ij; is the active load of node i, always positive; is the charging and discharging power of the distributed energy storage at node i, negative for charging and positive for discharging; is the output of the distributed power source at node i, always positive; Q tot,i is the net injected reactive power of node i, only including the load; is the reactive load of node i; U max is the upper voltage amplitude limit; U min is the lower voltage amplitude limit; P ij is the apparent power of branch ij; P ij,min is the lower limit of the apparent power transmission capacity of branch ij; Pij,max is the upper limit of the apparent power transfer capacity of branch ij; δ is the line overload constraint magnification factor; θ max is the upper limit of the node voltage phase angle; θ min is the lower limit of the node voltage phase angle; θ i is the node voltage phase angle.

[0045] Preferably, the energy storage operation constraint is specifically:

[0046] The state of charge constraint equation of the energy storage system:

[0047] E cap,i (t + 1)= E cap,i (t)+ P pc,i (t)· K pc,i (t)· Δt· η c - P pd,i (t)· K pd,i (t)· Δt / η d

[0048] SOC i (t)= E cap,i (t) / E rat,i

[0049] SOC min ≤ SOC i (t)≤ SOC max

[0050] The upper and lower limit constraint equations of the energy storage charge and discharge power:

[0051] 0 ≤ P pc,i (t)≤ K pc,i (t)· P pc,i,max

[0052] 0 ≤ P pd,i (t)≤ K pd,i (t)· P pd,i,max

[0053] The SOC conservation constraint equation at the beginning and end of the energy storage:

[0054] SOC i (0)= SOC i (T)

[0055] The constraint equation for the energy storage charge and discharge not occurring simultaneously:

[0056] K pc,i (t)+ K pd,i (t)≤ 1

[0057] In the formula, K pc,i(t) is the charging decision 0-1 variable of the i-th energy storage device at time t, 1 for charging and 0 for non-charging; K pd,i (t) is the discharging decision 0-1 variable of the i-th energy storage device at time t, 1 for discharging and 0 for non-discharging; Δt is the time period; SOC i (t) is the SOC of the i-th energy storage device at time t; SOC min is the minimum SOC of the energy storage device; SOC max is the maximum SOC of the energy storage device; E cap,i (t) is the stored electricity of the i-th energy storage system at time t; E rat,i is the rated capacity of the i-th energy storage system, P pc,i (t) and P pd,i (t) are the charging and discharging powers of the i-th energy storage system at time t respectively; P pc,i,max is the maximum charging power; P pd,i,max is the maximum discharging power; η c is the charging efficiency; η d is the discharging efficiency.

[0058] Preferably, the operation constraint of energy storage life loss is specifically:

[0059] DOD cyc (t) = DOD(t - 1)R ESS (t)

[0060] DOD(t) = 1 - SOC(t)

[0061] In the formula, DOD(t) is the depth of discharge of the energy storage battery at time t, DOD cyc (t) is the cyclic depth of discharge of the energy storage battery at time t, R ESS (t) is the 0-1 variable of the charge-discharge cycle of the energy storage battery. When the energy storage battery undergoes a charge-discharge cycle, the value is 1; when the energy storage battery does not undergo a charge-discharge cycle, the value is 0.

[0062] Preferably, the value of the R ESS (t) is specifically determined as follows:

[0063] Add the variable of the charging ramp process of the energy storage battery and the variable of the discharging downhill process to determine whether the energy storage battery undergoes a charge-discharge cycle. For the charging ramp process, the energy storage battery has only two states in this continuous process, charging or not operating; for the discharging downhill process, the energy storage battery also has only two states, discharging or not operating; therefore, the energy storage battery can only be in one of the two continuous processes at any time, specifically:

[0064]

[0065] In the formula, is a 0-1 variable for the charging ramp process. When its value is 1, it indicates that the energy storage battery is in the charging process; is a 0-1 variable for the discharging downhill process. When its value is 1, it indicates that the energy storage battery is in the discharging process;

[0066] The variables of the charging ramp process and the discharging downhill process of the energy storage battery are jointly determined by the process variables at the previous moment and the charge-discharge behavior of the energy storage battery itself at the current moment. The constraints are:

[0067]

[0068] R ESS (t) takes values as:

[0069]

[0070] Preferably, the equipment utilization rate constraint is specifically:

[0071]

[0072] In the formula, T all is all the statistical time periods within a day, and ν is the minimum daily utilization of the line.

[0073] Preferably, the model is solved by using the second-order cone relaxation algorithm and the linearization of bilinear terms, specifically:

[0074] The second-order cone relaxation algorithm is used to transform the non-convex and non-linear constraint problem into a mixed-integer second-order cone programming problem:

[0075] The transformation process based on the second-order cone relaxation is as follows. The following substitution transformation is used:

[0076]

[0077] Y ij =U i U j cosθ ij

[0078] Z ij =U i U j sinθ ij

[0079] Then the active power flow constraint formula and the reactive power flow constraint formula are expressed as:

[0080]

[0081] The parameters X i , Y ij , Zij Meet the following constraints:

[0082]

[0083] This constraint is further relaxed to the standard second-order cone form:

[0084]

[0085] After the above processing, the active power flow constraint formula and the reactive power flow constraint formula in the original model are converted into the constraint formula represented by the above formula, and solved by calling the GUROBI commercial solver through MATLAB;

[0086] The energy storage operation constraint formula contains a bilinear term of multiplying a 0-1 variable by a continuous variable. Its linearization method is as follows: introduce a new variable P kpc,i , and let P kpc,i =K pc,i (t)P pc,i (t), and then add the following 3 constraints, where R i is a free variable:

[0087]

[0088] Convert the original model into a MISOCP model, and it can be solved by calling the GUROBI commercial solver through MATLAB.

[0089] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0090] The present invention proposes an optimized configuration model for a distributed energy storage system considering factors such as power grid power flow, energy storage operation, life loss, and utilization rate of distribution network equipment. With the minimum total investment of the energy storage system as the optimization goal, considering constraints such as distribution network power flow constraints, energy storage operation, and equipment utilization rate constraints, the second-order cone relaxation method is used to convert the model into a mixed-integer second-order cone programming model to solve the optimal configuration and operation strategy of the distributed energy storage system. Case analysis shows that optimizing the configuration of the distributed energy storage considering multiple factors such as line utilization efficiency can improve the daily utilization rate of the distribution line, cut peak loads, and enhance the economic efficiency of the distribution network assets. Description of the Drawings

[0091] Figure 1 It is a schematic flow chart of the method of the present invention.

[0092] Figure 2 It is an example of a 10kV power supply grid provided for the embodiment.

[0093] Figure 3 It is a schematic diagram of the predicted load provided for the embodiment.

[0094] Figure 4A schematic diagram of the charge state of each distributed energy storage device matched with the configuration scheme in a typical peak load day scenario provided in an embodiment. DETAILED DESCRIPTION

[0095] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0096] In order to better illustrate the present embodiment, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0097] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0098] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0099] Example 1

[0100] This embodiment provides a distributed energy storage system optimization configuration and operation strategy method, such as Figure 1 As shown, the following steps are included:

[0101] Construct a distributed energy storage system optimization configuration model to minimize the investment and operation costs of the energy storage system and construct the objective function;

[0102] Construct constraints;

[0103] The model is solved using the second-order cone relaxation algorithm and bilinear term linearization to obtain the optimal configuration and operation strategy of the energy storage system.

[0104] Example 2

[0105] This embodiment further discloses the following contents based on the embodiment 1:

[0106] The construction objective function is specifically:

[0107]

[0108] in:

[0109] C investment,i =α(E rated,i I E +P rated,i I P )

[0110] C operation,i =C DESS E rated,i

[0111]

[0112] T rcu ≤Tlmt

[0113] Wherein, C operation,i is the operating cost of energy storage system i; C investment,i is the investment cost of energy storage system i; Φ is the set of energy storage system deployment points; E rated,i is the rated capacity of energy storage system i; P rated,i is the rated power of energy storage system i; α is the annualized value coefficient converted to an annual basis; I E is the investment cost per unit capacity of the energy storage system; I P is the investment cost per unit power of the energy storage system; C DESS is the operation and maintenance cost per unit capacity of the energy storage system; T rcu is the cycle life of the energy storage device, r is the discount rate, T lmt is the floating charge life of the battery.

[0114] When constructing the objective function, the cycle times of the energy storage device also need to be considered, specifically:

[0115]

[0116] Wherein, H life is the cycle times when the energy storage battery is retired, N0 is the cycle times when the energy storage battery is charged and discharged until retirement at 100% depth of discharge, given by the energy storage battery manufacturer, DOD cyc is the actual cycle discharge depth of the energy storage battery each time during actual operation;

[0117] During actual operation, the cycle discharge depth of the energy storage each time is not a certain value. The cycle discharge depth each time is converted into the corresponding equivalent full cycle times:

[0118]

[0119] Wherein, k p is the curve fitting parameter of the energy storage battery cycle times, given by the energy storage battery manufacturer, DOD cyc (t) is the cycle discharge depth of the energy storage battery at the t-th time, h loop (t) is the equivalent full cycle discharge times corresponding to each charge and discharge cycle of the energy storage battery;

[0120] Then the total equivalent full cycle times generated by the energy storage device daily are:

[0121]

[0122] Wherein, H loop is the daily equivalent full cycle discharge times;

[0123] The cycle life T of the energy storage device rcu is determined by the daily equivalent full cycle times:

[0124]

[0125] The above-mentioned constraint conditions include power grid power flow constraints, energy storage operation constraints, energy storage life loss operation constraints, and equipment utilization rate constraints.

[0126] The specific power grid power flow constraints are as follows:

[0127] Active power flow constraint equation:

[0128]

[0129] Reactive power flow constraint equation:

[0130]

[0131] Voltage upper and lower limit constraint equation:

[0132] U min ≤U i ≤U max

[0133] Phase angle upper and lower limit constraint equation:

[0134] θ min ≤θ i ≤θ max

[0135] Line apparent power flow upper and lower limit constraint equation:

[0136] P ij,min ≤P ij ≤δP ij,max

[0137] In the formula, P tot,i is the net injected active power of node i; U i is the voltage amplitude of node i; U j is the voltage amplitude of node j; θ ij is the voltage phase angle difference between node i and node j; g ij is the conductance of branch ij; b ij is the susceptance of branch ij; is the active load of node i, always positive; is the charging and discharging power of the distributed energy storage at node i, negative for charging and positive for discharging; is the output of the distributed power source at node i, always positive; Q tot,i is the net injected reactive power of node i, only including the load; is the reactive load of node i; U max is the upper limit of the voltage amplitude; U min is the lower limit of the voltage amplitude; P ijis the apparent power of branch ij; P ij,min is the lower limit of the apparent power transmission capacity of branch ij; P ij,max is the upper limit of the apparent power transmission capacity of branch ij; δ is the line heavy overload constraint magnification factor; θ max is the upper limit of the node voltage phase angle; θ min is the lower limit of the node voltage phase angle; θ i is the node voltage phase angle.

[0138] The specific energy storage operation constraints are as follows:

[0139] The state of charge constraint equation of the energy storage system:

[0140] E cap,i (t + 1)=E cap,i (t)+P pc,i (t)·K pc,i (t)·Δt·η c -P pd,i (t)·K pd,i (t)·Δt / η d

[0141] SOC i (t)=E cap,i (t) / E rat,i

[0142] SOC min ≤SOC i (t)≤SOC max

[0143] The upper and lower limit constraints of the energy storage charge and discharge power:

[0144] 0≤P pc,i (t)≤K pc,i (t)·P pc,i,max

[0145] 0≤P pd,i (t)≤K pd,i (t)·P pd,i,max

[0146] The SOC conservation constraint equation at the beginning and end of the energy storage:

[0147] SOC i (0)=SOC i (T)

[0148] The constraint equation for the energy storage charge and discharge not occurring simultaneously:

[0149] K pc,i (t)+K pd,i (t)≤1

[0150] In the formula, Kpc,i (t) is the charging decision 0-1 variable of the i-th energy storage device at time t, 1 for charging and 0 for not charging; K pd,i (t) is the discharging decision 0-1 variable of the i-th energy storage device at time t, 1 for discharging and 0 for not discharging; Δt is the time period; SOC i (t) is the SOC of the i-th energy storage device at time t; SOC min is the minimum SOC of the energy storage device; SOC max is the maximum SOC of the energy storage device; E cap,i (t) is the stored electricity of the i-th energy storage system at time t; E rat,i is the rated capacity of the i-th energy storage system, P pc,i (t) and P pd,i (t) are the charging and discharging powers of the i-th energy storage system at time t respectively; P pc,i,max is the maximum charging power; P pd,i,max is the maximum discharging power; η c is the charging efficiency; η d is the discharging efficiency.

[0151] The operation constraint of the energy storage life loss is specifically as follows:

[0152] DOD cyc (t) = DOD(t - 1)R ESS (t)

[0153] DOD(t) = 1 - SOC(t)

[0154] In the formula, DOD(t) is the depth of discharge of the energy storage battery at time t, DOD cyc (t) is the cyclic depth of discharge of the energy storage battery at time t, R ESS (t) is the 0-1 variable for the charge and discharge cycle of the energy storage battery. When the energy storage battery undergoes a charge and discharge cycle, the value is 1, and when the energy storage battery does not undergo a charge and discharge cycle, the value is 0.

[0155] The value of the said R ESS (t) is determined specifically as follows:

[0156] Add the variable for the charging ramp process of the energy storage battery and the variable for the discharging downhill process to determine whether the energy storage battery undergoes a charge and discharge cycle. For the charging ramp process, the energy storage battery has only two states in this continuous process, charging or not operating; for the discharging downhill process, the energy storage battery also has only two states, discharging or not operating; therefore, the energy storage battery can only be in one of the two continuous processes at any moment, specifically:

[0157]

[0158] In the formula, is a 0-1 variable for the charging ramp-up process. When its value is 1, it indicates that the energy storage battery is in the charging process; is a 0-1 variable for the discharging ramp-down process. When its value is 1, it indicates that the energy storage battery is in the discharging process;

[0159] The variables of the charging ramp-up process and the discharging ramp-down process of the energy storage battery are jointly determined by the process variable at the previous moment and the charge-discharge behavior of the energy storage battery itself at the current moment, and the constraints are:

[0160]

[0161] R ESS (t) takes the value of:

[0162]

[0163] The specific equipment utilization rate constraint is:

[0164]

[0165] In the formula, T all is all the statistical time periods within a day, and ν is the minimum daily utilization of the line.

[0166] The model is solved by using the second-order cone relaxation algorithm and linearizing the bilinear terms, specifically as follows:

[0167] The second-order cone relaxation algorithm is used to transform the non-convex and non-linear constraint problem into a mixed-integer second-order cone programming problem:

[0168] The transformation process based on the second-order cone relaxation is as follows. The following variable substitution is used:

[0169]

[0170] Y ij =U i U j cosθ ij

[0171] Z ij =U i U j sinθ ij

[0172] Then the active power flow constraint formula and the reactive power flow constraint formula are expressed as:

[0173]

[0174] The parameters X i , Y ij , Z ijMeet the following constraints:

[0175]

[0176] The constraint is further relaxed to the standard second-order cone form:

[0177]

[0178] After the above processing, the active power flow constraint formula and the reactive power flow constraint formula in the original model are converted into the constraint formula represented by the above formula, and solved by calling the GUROBI commercial solver through MATLAB;

[0179] The energy storage operation constraint formula contains a bilinear term of multiplying a 0-1 variable by a continuous variable. The linearization method is as follows: introduce a new variable P kpc,i , and let P kpc,i =K pc,i (t)P pc,i (t), and then add the following 3 constraints, where R i is a free variable:

[0180]

[0181] Convert the original model into a MISOCP model, and it can be solved by calling the GUROBI commercial solver through MATLAB.

[0182] Example 3

[0183] This example provides a case analysis. As Figure 2 shown, the case planning area is a 10kV power supply grid, and the power loads in this grid mainly include residential, commercial and agricultural loads. The superior power source in the grid is an 110kV substation in this area, and the loads are distributed along the main line and 3 branch lines.

[0184] The detailed information of the distribution network lines is shown in Table 1.

[0185] Table 1

[0186]

[0187] Taking 80% exceeding the rated capacity as the heavy overload standard, Figure 2 the lines between the medium load nodes 1-2, 2-8, and 6-14 are the main heavy overload lines. The parameters of the substation equipment in the case are shown in Table 2.

[0188] Table 2

[0189]

[0190] Figure 3 is the predicted load of each line on a typical peak load day in the future long-term year,Figure 3 The middle straight dotted line represents 80% of the rated capacity of the corresponding line.

[0191] The parameters of the energy storage equipment used in the plan are shown in Figure 2.

[0192] Table 3

[0193]

[0194] Analysis of energy storage configuration results:

[0195] In the example scenario provided in this embodiment, it is decided to configure distributed energy storage devices at nodes 5, 15, and 27 respectively, with a total rated power of 2.18MW and a total rated capacity of 4.89MWh. The configuration results of the distributed energy storage devices are shown in Table 4.

[0196] Table 4

[0197]

[0198] The peak load reduction of heavily overloaded lines before and after being equipped with distributed energy storage equipment is shown in Table 5.

[0199] Table 5

[0200]

[0201] In the peak load typical day scenario, the charge status of each distributed energy storage device matched with the configuration solution is as follows: Figure 4 shown.

[0202] It can be seen that both energy storage devices are charged first and then discharged within a day to maintain their own power balance. The difference is that the energy storage at node 11 is charged during the low load period at night and discharged during the peak load period during the day. The energy storage at node 16 is charged during the low load period at night, discharged from 7 am to 1 pm during the day, and then charged to SOC reaching about 60% and discharged again during the peak load period in the evening.

[0203] It can be seen that after configuring the distributed energy storage equipment, the peak-shaving and valley-filling effect of the line is achieved. The distributed energy storage equipment at nodes 5, 15, and 27 discharges during the peak load period to avoid heavy overload of the line. It charges during the low load period to maintain the daily balance of its own power.

[0204] In addition, in this example, the peak load reduction of four lines can be achieved by only two energy storage devices, indicating that when the distribution network lines are heavily overloaded with end-to-end connection, the use of multi-point distributed energy storage devices can replace the traditional line expansion planning scheme and alleviate the heavy overload of the distribution lines.

[0205] Line utilization analysis:

[0206] Through the optimized regulation of the charge and discharge strategies of distributed energy storage devices, it is possible to alleviate the slight overloading of lines during certain periods and regions, and improve the utilization rate of existing line equipment. It can be seen that without energy storage, due to the inability to utilize the idle capacity of the lines during the low-load period at night for charging, the utilization rate of the lines is relatively low. After configuring energy storage, the energy storage device charges through the line during the low-load period at night, improving the utilization rate of the line. During the peak load period, with the role of the energy storage device, the line reaches the maximum allowable transmission power, and the short-term utilization rate at this time reaches the maximum. Therefore, overall, after planning energy storage, the two-way power support and energy throughput capacity of energy storage can improve the short-term utilization rate of the distribution network lines at night and reduce the overloading situation during the peak load period.

[0207] Table 6

[0208]

[0209] Distributed energy storage planning is an important part of distribution network planning, and it has a flexible adjustment effect on the power flow control and efficient utilization of equipment such as distribution lines. In this paper, by configuring multi-point distributed energy storage devices to regulate the power flow of distribution lines, the utilization rate of distribution lines is improved, and the coordinated operation of "source-network-storage" is realized. The case study shows that:

[0210] 1) Configuring multi-point distributed energy storage is suitable for solving the short-term overloading problem of distribution network lines. Through the coordinated operation of "source-network-storage" of multi-point distributed energy storage and distributed power sources, the peak load of the line can be reduced, the transformation pressure of the substation can be alleviated, and the probability of load loss in the distribution network can be reduced.

[0211] 2) Optimizing the planning of distributed energy storage can alleviate the problem of low utilization rate of distribution lines at night. Through the two-way power regulation of energy storage, the line capacity resources during the low-load period are effectively utilized, the utilization rate of the existing equipment assets in the distribution network is improved, and it helps the distribution company to manage its various costs and revenues in a refined manner.

[0212] The same or similar reference numerals correspond to the same or similar components;

[0213] The terms used to describe the positional relationship in the drawings are for illustrative purposes only and should not be construed as a limitation of this patent;

[0214] Obviously, the above-mentioned embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A method for optimizing the configuration and operation strategy of a distributed energy storage system, characterized in that It includes the following steps: Construct an optimal configuration model for a distributed energy storage system, with the minimum investment and operating costs of the energy storage system as the objective function. Construct the constraint conditions. Use the second-order cone relaxation algorithm and bilinear term linearization to solve the model, and obtain the optimal configuration and operation strategy of the energy storage system. The construction of the objective function is specifically as follows: Where: C investment,i = α(E rated,i I E + P rated,i I P ) C operation,i = C DESS E rated,i T rcu ≤T lmt Where, C operation,i is the operating cost of energy storage system i; C investment,i is the investment cost of energy storage system i; Φ is the set of energy storage system placement points; E rated,i is the rated capacity of energy storage system i; P rated,i is the rated power of energy storage system i; α is the annualized value coefficient converted to an annual basis; I E is the investment cost per unit capacity of the energy storage system; I P is the unit power investment cost of the energy storage system; C DESS is the operation and maintenance cost per unit capacity of the energy storage system; T rcu is the cycle life of the energy storage device, r is the discount rate, T lmt is the floating charge life of the battery; When constructing the objective function, the cycle times of the energy storage device also need to be considered, specifically: Where, H life is the number of cycles at the end of the energy storage battery's retirement, N0 is the number of cycles when the energy storage battery is charged and discharged at 100% depth of discharge until retirement, which is given by the energy storage battery manufacturer, and DOD cyc is the depth of discharge of each cycle during the actual operation of the energy storage battery; In actual operation, the depth of discharge of each cycle of the energy storage is not a fixed value. Convert the depth of discharge of each cycle into the corresponding equivalent full cycle times: where k p is the fitting parameter of the energy storage battery cycle number curve, given by the energy storage battery manufacturer, and DOD cyc (t) is the cycle discharge depth of the energy storage battery at the t-th actual cycle, h loop (t) is the equivalent full cycle discharge number corresponding to each charge and discharge cycle of the energy storage battery; Then the total equivalent full cycle times generated by the energy storage device per day are: where H loop is the daily equivalent full-cycle discharge times; The cycle life T of the energy storage device rcu Determined by the daily equivalent full cycle times: The constraint conditions include power grid power flow constraints, energy storage operation constraints, energy storage life loss operation constraints, and equipment utilization rate constraints.

2. The method for optimizing the configuration and operation strategy of the distributed energy storage system according to claim 1, wherein The power grid power flow constraints are specifically: Active power flow constraint formula: P tot,i = -P i LOAD +P i DESS +P i DG Reactive power flow constraint formula: Voltage upper and lower limit constraint formula: U min ≤U i ≤U max Phase angle upper and lower limit constraint formula: θ min ≤ θ i ≤ θ max Line apparent power flow upper and lower limit constraint formula: P ij,min ≤P ij ≤δP ij,max Wherein, P tot,i is the net active power injection of node i; U i is the voltage amplitude of node i; U j is the voltage amplitude of node j; θ ij is the voltage phase angle difference between node i and node j; g ij is the conductance of branch ij; b ij is the susceptance of branch ij; P i LOAD is the active power load of node i, always positive; P i DESS is the charge and discharge power of the distributed energy storage at node i, negative for charging and positive for discharging; P i DG is the output of the distributed power source at node i, always positive; Q tot,i is the net reactive power injection of node i, only including the load; is the reactive power load of node i; U max is the upper limit of the voltage amplitude; U min is the lower limit of the voltage amplitude; P ij is the apparent power of branch ij; P ij,min is the lower limit of the apparent power transmission capacity of branch ij; P ij,max is the upper limit of the apparent power transmission capacity of branch ij; δ is the line heavy overload constraint magnification factor; θ max is the upper limit of the node voltage phase angle; θ min is the lower limit of the node voltage phase angle; θ i is the node voltage phase angle.

3. The method for optimizing the configuration and operation strategy of the distributed energy storage system according to claim 2, characterized in that The energy storage operation constraints are specifically: State of charge constraint formula of the energy storage system: E cap,i (t + 1)= E cap,i (t)+ P pc,i (t)· K pc,i (t)· Δt· η c -P pd,i (t)· K pd,i (t)· Δt / η d SOC i (t) = E cap,i (t) / E rat,i SOC min ≤ SOC i (t) ≤ SOC max Upper and lower limit constraint formula of the energy storage charge and discharge power: 0 ≤ P pc,i (t) ≤ K pc,i (t) · P pc,i,max 0 ≤ P pd,i (t) ≤ K pd,i (t) · P pd,i,max SOC conservation constraint formula at the beginning and end of the energy storage: SOC i (0) = SOC i (T) Constraint formula that the energy storage charge and discharge do not occur simultaneously: K pc,i (t) + K pd,i (t) ≤ 1 Where, K pc,i (t) is the charging decision 0-1 variable of the i-th energy storage device at time t, 1 for charging and 0 for not charging; K pd,i (t) is the discharging decision 0-1 variable of the i-th energy storage device at time t, 1 for discharging and 0 for not discharging; Δt is the time period; SOC i (t) is the SOC of the i-th energy storage device at time t; SOC min is the minimum SOC of the energy storage device; SOC max is the maximum SOC of the energy storage device; E cap,i (t) is the stored electricity of the i-th energy storage system at time t; E rat,i is the rated capacity of the i-th energy storage system, P pc,i (t) and P pd,i (t) are the charging and discharging powers of the i-th energy storage system at time t respectively; P pc,i,max is the maximum charging power; P pd,i,max is the maximum discharging power; η c is the charging efficiency; η d is the discharge efficiency.

4. The method for optimizing the configuration and operation strategy of the distributed energy storage system according to claim 3, wherein The energy storage life loss operation constraints are specifically: DOD cyc DOD(t) = DOD(t - 1)R ESS (t) DOD(t) = 1 - SOC(t) Where DOD(t) is the depth of discharge of the energy storage battery at time t, and DOD cyc (t) is the cyclic depth of discharge of the energy storage battery at time t, and R ESS (t) is a 0-1 variable for the charge-discharge cycle of the energy storage battery. When the energy storage battery undergoes a charge-discharge cycle, the value is 1; when the energy storage battery does not undergo a charge-discharge cycle, the value is 0.

5. The method for optimizing the configuration and operation strategy of the distributed energy storage system according to claim 4, wherein The said R ESS (t) is valued, and the specific judgment method is as follows: Add variables for the charging ramp process of the energy storage battery and variables for the discharging downhill process to determine whether the energy storage battery undergoes charge-discharge cycles. For the charging ramp process, the energy storage battery has only two states in this continuous process: charging or not operating; for the discharging downhill process, the energy storage battery also has only two states: discharging or not operating; therefore, the energy storage battery can only be in one of the two continuous processes at any given moment, specifically: wherein, is a 0-1 variable for the charging ramp process, and when the value is 1, it indicates that the energy storage battery is in the charging process; is a 0-1 variable for the discharging downhill process, and when the value is 1, it indicates that the energy storage battery is in the discharging process; The variables of the charging ramp process and the discharging downhill process of the energy storage battery are jointly determined by the process variables at the previous moment and the charging and discharging behavior of the energy storage battery itself at the current moment. The constraint is: R ESS (t) takes values as follows:

6. The method for optimizing the configuration and operation strategy of the distributed energy storage system according to claim 5, wherein The equipment utilization rate constraints are specifically: Where, T all is all the statistical periods within a day, and ν is the minimum daily utilization of the line.

7. The method for optimizing the configuration and operation strategy of the distributed energy storage system according to claim 6, wherein The use of the second-order cone relaxation algorithm and bilinear term linearization for model solving is specifically: Use the second-order cone relaxation algorithm to transform the non-convex and non-linear constraint problem into a mixed-integer second-order cone programming problem: The transformation process based on the second-order cone relaxation is as follows. Use the following variable substitution: Y ij = U i U j cosθ ij Z ij = U i U j sinθ ij Then the active power flow constraint formula and the reactive power flow constraint formula are expressed as: The parameter X after substitution i , Y ij , Z ij satisfy the following constraints: This constraint is further relaxed into the standard second-order cone form: Solve through MATLAB by calling the GUROBI commercial solver; The energy storage operation constraint contains a bilinear term that is the product of a 0-1 variable and a continuous variable. The linearization method is as follows: introduce a new variable P kpc,i , and let P kpc,i = K pc,i (t)P pc,i (t). Then add the following 3 constraints, where R i is a free variable: Convert the original model into a MISOCP model, and it can be solved through MATLAB by calling the GUROBI commercial solver.

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