Dispatching control method of distributed energy storage platform

Abstract

The invention relates to the technical field of energy storage, and aims to solve the problem that an effective large-scale energy storage equipment cooperative work mechanism is lacked at present in the aspect of cooperative control of multi-zone-area distributed energy storage equipment. For this purpose, the invention provides a scheduling control method for a distributed energy storage platform, and the method comprises the steps: collecting parameter data, transformer area operation data and cost income data of each energy storage device, and updating and substituting the parameter data, transformer area operation data and cost income data into a bottom layer optimization objective function and constraint conditions thereof; calculating an initial charging and discharging strategy of each energy storage device by adopting a linear programming algorithm, and controlling the operation of the energy storage devices; acquiring inter-transformer-area tie line parameter data, multi-transformer-area load data and voltage monitoring data, and substituting the acquired data and the calculated initial charging and discharging strategy into an upper-layer optimization objective function and constraint conditions of the upper-layer optimization objective function; and calculating a final charging and discharging strategy of each energy storage device by adopting a dynamic programming algorithm, and controlling the operation of the energy storage devices. According to the invention, the synchronous coordination control of thousands of energy storage devices among a plurality of transformer areas can be realized.

Description

Technical Field

[0001] The present invention relates to the field of energy storage technology, and in particular to a dispatching control method for a distributed energy storage platform. Background Art

[0002] With the rapid development of modern social economy, the demand for electricity is becoming increasingly diversified and complex. In the operation of distribution networks, the phenomenon of centralized and seasonal loads is becoming more and more prominent. For example, during the high temperature period in summer, the large-scale centralized use of refrigeration equipment such as air conditioners, and the sharp increase in industrial electricity load in certain areas during the peak season of specific industrial production, these situations often lead to the distribution network facing the dilemma of severe resource shortage in a short period of time.

[0003] The traditional way to deal with the shortage of distribution network resources mainly relies on the transformation and upgrading of the distribution network, such as increasing the capacity of distribution transformers and upgrading transmission lines. However, this traditional model has many disadvantages. Its process cycle is lengthy, and it often takes a lot of time from project planning, equipment procurement to construction and final commissioning and operation, making it difficult to respond quickly to short-term load peaks. Moreover, these transformation and upgrading projects involve a large amount of manpower, material and financial resources, and are less economical. For some load peaks that only appear for a short time, this large-scale transformation investment seems to be not worth the cost.

[0004] At the same time, distributed energy storage technology has developed rapidly in recent years. Energy storage equipment can store electricity when electricity is abundant and release electricity when electricity is scarce, thereby effectively adjusting the power balance of the distribution network. It is regarded as a highly potential solution to the problem of short-term resource shortage in the distribution network. However, the existing distributed energy storage management and control technology still has obvious limitations. In terms of intelligent control strategies, most existing technologies find it difficult to formulate accurate and cost-effective control strategies based on multiple factors such as the complex characteristics of energy storage equipment, dynamic load changes in substations, and cost-benefit.

[0005] In the coordinated control of distributed energy storage devices in multiple areas, there is currently a lack of effective large-scale energy storage equipment collaborative working mechanisms. The load demand, energy storage equipment distribution and power grid topology between different areas are interrelated and complex, but existing technologies are often unable to fully integrate this information, making it difficult to achieve synchronous coordinated control of thousands of energy storage devices between multiple areas to form a strong aggregation effect to support good interaction between source, grid, load and storage. For example, in the face of regional load fluctuations, due to the lack of efficient coordinated control across areas, some areas may have excessive use of energy storage equipment, while other areas may be idle, making it impossible to achieve the optimal configuration of the entire regional distribution network resources.

[0006] In view of this, the art needs a dispatching control method for a distributed energy storage platform to solve the above problems. Summary of the invention

[0007] In order to solve the above technical problems, namely, to solve the problem of coordinated control of distributed energy storage devices in multiple areas, there is currently a lack of effective large-scale energy storage equipment collaborative working mechanism, making it difficult to achieve synchronous coordinated control of thousands of energy storage devices among multiple areas.

[0008] The present invention provides a dispatching control method for a distributed energy storage platform, the dispatching control method comprising:

[0009] Collect parameter data, substation operation data and cost-benefit data of each energy storage device;

[0010] Update and substitute the parameter data of each energy storage device, the substation operation data and the cost-benefit data into the underlying optimization objective function and its constraints;

[0011] Using a linear programming algorithm to calculate a preliminary charging and discharging strategy for each of the energy storage devices;

[0012] Controlling the operation of the energy storage device based on the calculated preliminary charge and discharge strategy of each of the energy storage devices;

[0013] Collect parameter data of interconnection lines between substations, load data of multiple substations and voltage monitoring data;

[0014] Substituting the inter-substation tie line parameter data, the multi-substation load data, the voltage monitoring data and the calculated preliminary charge and discharge strategy of each energy storage device into the upper optimization objective function and its constraints;

[0015] Using a dynamic programming algorithm to calculate the final charging and discharging strategy of each of the energy storage devices;

[0016] The operation of the energy storage device is controlled based on the calculated final charge and discharge strategy of each of the energy storage devices.

[0017] In some preferred embodiments, the underlying optimization objective function is:

[0018]

[0019] Among them, T is the scheduling period, C c is the charging cost coefficient, C d is the discharge cost coefficient, is the charging power of the i-th energy storage device at time t, is the discharge power of the i-th energy storage device at time t.

[0020] In some preferred embodiments, the constraints of the underlying optimization objective function are:

[0021]

[0022] Among them, P L,t is the load power of the substation at time t, N is the number of energy storage devices in the substation, P G,t is the power injected into the substation by the upper power grid at time t, P i,min is the minimum charge and discharge power of the i-th energy storage device, P i,max is the maximum charge and discharge power of the i-th energy storage device, E i,t is the remaining capacity of the i-th energy storage device at time t, E i,min is the minimum capacity of the i-th energy storage device, E i,max is the maximum capacity of the i-th energy storage device, N c,i is the cumulative charging times of the i-th energy storage device, N d,i is the cumulative discharge times of the i-th energy storage device, N c,max,i is the maximum allowed charging times of the i-th energy storage device, N d,max,i is the maximum allowed discharge times of the i-th energy storage device.

[0023] In certain preferred embodiments, the use of a linear programming algorithm to calculate the preliminary charge and discharge strategy of each energy storage device specifically includes:

[0024] Converting the underlying optimization objective function and its constraints into a standard form;

[0025] Construct the initial simplex table;

[0026] Starting from the initial simplex table, through a series of axis rotation operations, the table is updated according to the rules of the input and output variables until the preliminary charging and discharging strategy is obtained.

[0027] In some preferred embodiments, the upper layer optimization objective function is:

[0028]

[0029] Among them, T is the scheduling period, M is the number of stations, P ij,t is the tie line power flowing from area i to area j at time t, V i,t is the voltage amplitude at time t, V ref is the reference voltage, α and β are weight coefficients.

[0030] In some preferred embodiments, the constraints of the upper layer optimization objective function are:

[0031]

[0032] Among them, P Li,t is the load power of the station area i at time t, Ni is the number of energy storage devices in the area i, P k,t is the charging and discharging power of the kth energy storage device in the station area i at time t, P Gi,t is the power injected into the station i by the upper power grid at time t, P ij,min is the minimum transmission power of the tie line between area i and area j, P ij,max is the maximum transmission power of the tie line between area i and area j, V i,min is the minimum voltage amplitude allowed in the station area i, V i,max is the maximum voltage amplitude allowed in the station area i.

[0033] In certain preferred embodiments, the use of a dynamic programming algorithm to calculate the final charge and discharge strategy of each energy storage device specifically includes:

[0034] Define phase and state variables, determine decision variables;

[0035] Construct state transfer equations, calculate stage benefits and optimal indicator functions;

[0036] The final charging and discharging strategy is obtained by backtracking from the last stage to solve the optimal decision variables.

[0037] In certain preferred embodiments, the defining of the phase and state variables and determining the decision variables specifically include:

[0038] Divide the scheduling period T into multiple stages, each stage corresponds to a time interval;

[0039] The state variables are defined as the energy storage device state vector of each substation, the tie line power state vector and the load power state vector of the substation;

[0040] The decision variables are determined as the adjustment amount of the charging and discharging power of the energy storage equipment in each substation and the adjustment amount of the interconnection line power distribution.

[0041] In certain preferred embodiments, the state transfer equation includes a state transfer equation for the capacity of the energy storage device in the station area i and a state transfer equation for the power of the tie line between the stations;

[0042] The state transfer equation of the energy storage device capacity of the station area i is:

[0043] S i,t+1 =S i,t +(ΔP i,t +P i,t )·η i,t ·Δt

[0044] Among them, S i,t+1 is the state vector of the energy storage device in the station area i at time t+1, S i,tis the state vector of the energy storage device in station i at time t, ΔP i,t is the adjustment amount of the charging and discharging power of the i-th energy storage device in the i-th area at time t, P i,t is the charging and discharging power of the i-th energy storage device in the station area i at time t obtained by the bottom layer optimization, η i,t is the charging and discharging efficiency of the i-th energy storage device in the station area i at time t, and Δt is the time interval;

[0045] The state transfer equation of the tie line power between the substations is:

[0046] P ij,t+1 =P ij,t +ΔP ij,t

[0047] Among them, P ij,t+1 is the power of the tie line from area i to area j at time t+1, P ij,t is the power of the tie line from area i to area j at time t, ΔP ij,t It is the adjustment amount for the power distribution of the interconnection line between areas i and j at time t.

[0048] In some preferred embodiments, the calculation formula of the stage benefit is:

[0049]

[0050] Among them, r t is the stage benefit of stage t, V i,t+1 is the voltage amplitude at time t+1;

[0051] The optimal index function is:

[0052]

[0053] Among them, f t (S t ) is the state S from the next stage t t The minimum objective function value from the start to the end of the scheduling period, ΔP t is the power adjustment vector of the energy storage device at the current stage t, ΔP Lt is the tie line power adjustment vector at the current stage t, f t+1 (S t+1 ) is the state S from the next stage t+1 t+1 The minimum objective function value from the start to the end of the scheduling period.

[0054] It can be seen from the above that the dispatching control method of the distributed energy storage platform provided by the present invention has the following beneficial technical effects:

[0055] The present invention can formulate the most suitable preliminary charging and discharging strategy according to the real-time load power of the substation and the power injected by the upper power grid, combined with the characteristic parameters of the energy storage device itself, through the precise regulation of a single energy storage device through bottom-level optimization. This enables the energy storage device to respond quickly at critical moments, effectively supplement the insufficient power supply of the substation, avoid voltage drops and frequency fluctuations caused by peak loads, and significantly enhance the power supply stability of a single substation; and under the coordinated control of multiple substations optimized at the upper level, the parameter restrictions of the interconnection lines between substations, the predicted data of the loads of each substation, and the voltage monitoring conditions are further considered to fine-tune the preliminary strategy obtained by bottom-level optimization. By coordinating the charging and discharging power of energy storage devices and the power distribution of interconnection lines between multiple substations, the optimized integration and balanced allocation of power resources in the region are achieved. In the face of regional large load shocks, power can be reasonably allocated from lightly loaded substations to heavily loaded substations through interconnection lines, giving full play to the overall aggregation effect of distributed energy storage devices, effectively alleviating the power supply pressure of the regional distribution network, greatly improving the ability of the entire regional distribution network to cope with complex load changes, and ensuring reliable power supply for a large number of users in the region.

[0056] Furthermore, when formulating the charging and discharging strategy, the underlying optimization fully considers the charging and discharging cost and benefit coefficient of the energy storage equipment, and takes economic factors into consideration. This allows the energy storage equipment to choose to charge when the electricity cost is low and discharge when the electricity benefit is high while meeting the power demand of the substation, thereby improving the economic operation efficiency of the energy storage equipment. At the same time, by strictly following the constraints such as the power limit, capacity limit, and charging and discharging number limit of the energy storage equipment, damage to the equipment caused by excessive charging and discharging or operation exceeding the rated power of the equipment is avoided, the service life of the energy storage equipment is effectively extended, the operation and maintenance cost and replacement frequency of the energy storage equipment are greatly reduced, and the overall economy and reliability of the energy storage system are improved. In addition, the dispatching and control method of the present invention significantly enhances the flexible dispatching capability of the distribution network. By real-time collection and analysis of multi-source information such as substation operation data, energy storage equipment data, and inter-substation interconnection line data, the operating state changes of the distribution network can be quickly and accurately perceived. Based on these rich data information, both the underlying optimization and the upper optimization can adjust the charging and discharging strategy of the energy storage equipment in a timely manner to adapt to different operating conditions. For example, when a distribution network fails or is subject to external interference, such as a sudden failure of a transmission line that causes power supply to some substations to be interrupted, the dispatching and control method of the present invention can respond quickly and adjust the working mode of the energy storage equipment in nearby substations to give priority to supplying power to important loads. It can also maintain power balance and voltage stability in the area through coordinated control of interconnection lines between substations, thereby achieving flexible response and rapid recovery of the distribution network in the event of a fault, improving the anti-interference and self-healing capabilities of the distribution network, and providing strong technical support for the construction of smart distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The features and advantages of the present invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the present invention in any way. In the accompanying drawings:

[0058] Figure 1 It is a flow chart of the dispatching control method of the distributed energy storage platform of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0060] Based on the background technology, it is pointed out that in the coordinated control of distributed energy storage devices in multiple areas, there is currently a lack of effective large-scale energy storage equipment collaborative working mechanism, and it is difficult to achieve the problem of synchronous coordinated control of thousands of energy storage devices among multiple areas. The present invention provides a dispatching and control method for a distributed energy storage platform, which aims to achieve synchronous coordinated control of thousands of energy storage devices among multiple areas, greatly improving the ability of the entire regional distribution network to cope with complex load changes.

[0061] like Figure 1 As shown, the dispatching control method of the distributed energy storage platform of the present invention includes:

[0062] Collect parameter data, substation operation data and cost-benefit data of each energy storage device;

[0063] Update the parameter data, area operation data and cost-benefit data of each energy storage device and substitute them into the underlying optimization objective function and its constraints;

[0064] A linear programming algorithm is used to calculate the preliminary charging and discharging strategy for each energy storage device;

[0065] Controlling the operation of the energy storage device based on the calculated preliminary charging and discharging strategy for each energy storage device;

[0066] Collect parameter data of interconnection lines between substations, load data of multiple substations and voltage monitoring data;

[0067] Substitute the parameter data of the interconnection lines between substations, the load data of multiple substations, the voltage monitoring data and the calculated preliminary charging and discharging strategy update of each energy storage device into the upper optimization objective function and its constraints;

[0068] A dynamic programming algorithm is used to calculate the final charging and discharging strategy for each energy storage device;

[0069] The operation of the energy storage device is controlled based on the calculated final charging and discharging strategy for each energy storage device.

[0070] Preferably, for the collection of parameter data of energy storage devices, a data connection can be established with the energy storage device management system, and through a specific data interface protocol, detailed parameter information of each energy storage device, including rated capacity (maximum capacity) E i,max 、Minimum capacity E i,min , Maximum charge and discharge power P i,max , minimum charge and discharge power P i,min , charging efficiency curve, discharge efficiency curve, initial capacity E i,0 、Maximum allowable charge and discharge times (N c,max,i , N d,max,i ), actual cumulative charging times N c,i 、Actual cumulative discharge times N d,i 、Remaining capacity E i,t , charging and discharging power, etc. For the collection of substation operation data, a stable communication link can be established with the substation monitoring system to receive and record the substation load power P according to the preset sampling frequency of once every 5 minutes. L,t Data. These data reflect the total power demand of all electrical equipment in the substation at different times. Their dynamic changes are one of the important bases for determining the charging and discharging strategy of energy storage equipment. At the same time, it interacts with the power grid dispatching automation system or the upper power grid communication interface to obtain the power P injected into the substation by the upper power grid. G,t Data, which reflects the power supply support provided by the upper power grid to the substation, and together with the load power of the substation, constitutes the power balance relationship within the substation. For the collection of cost-benefit data, the power purchase price, power sales price and other relevant market information can be obtained regularly from the power market trading platform, and the operation and maintenance cost data of energy storage equipment can be obtained from the internal cost accounting system of the enterprise, including equipment depreciation, maintenance personnel expenses, equipment operation loss, etc. Based on this information, the charging cost coefficient C is determined according to the established cost calculation model. c , which takes into account factors such as electricity procurement cost and loss cost of energy storage equipment during charging; Determine the discharge benefit coefficient C d This coefficient combines factors such as the electricity sales price and the compensation price obtained by the energy storage equipment for participating in grid auxiliary services (such as frequency regulation, voltage regulation, etc.), so that the charging and discharging strategy of the energy storage equipment can achieve economic optimization on the basis of technical feasibility.

[0071] Preferably, the underlying optimization objective function is:

[0072]

[0073] Among them, T is the scheduling period, C c is the charging cost coefficient, C d is the discharge cost coefficient, is the charging power (P i,t >0), is the discharge power of the i-th energy storage device at time t (P i,t <0), the underlying optimization objective function aims to minimize the charging and discharging cost of the energy storage equipment during the scheduling cycle, and to maximize the economic benefits while meeting the power demand of the substation by reasonably arranging the charging and discharging timing and power size.

[0074] Further preferably, the constraints of the underlying optimization objective function are:

[0075]

[0076] Among them, P L,t is the load power of the substation at time t, N is the number of energy storage devices in the substation, P G,t is the power injected into the substation by the upper power grid at time t, P i,min is the minimum charge and discharge power of the i-th energy storage device, P i,max is the maximum charge and discharge power of the i-th energy storage device, E i,t is the remaining capacity of the i-th energy storage device at time t, E i,min is the minimum capacity of the i-th energy storage device, E i,max is the maximum capacity of the i-th energy storage device, N c,i is the cumulative charging times of the i-th energy storage device, N d,i is the cumulative discharge times of the i-th energy storage device, N c,max,i is the maximum allowed charging times of the i-th energy storage device, N d,max,i is the maximum allowed discharge times of the i-th energy storage device.

[0077] In the above, the remaining capacity E of the i-th energy storage device at time t is i,t The calculation formula is: E i,t =E i,t-1 +P i,t-1 ·η i,t-1 Δt, E i,t-1 is the remaining capacity of the i-th energy storage device at time t-1, P i,t-1 is the charging and discharging power of the i-th energy storage device at time t-1, η i,t-1 is the charging and discharging efficiency of the i-th energy storage device at time t-1 (η when charging c , when discharging, η d ).

[0078] Preferably, the linear programming algorithm is used to calculate the preliminary charging and discharging strategy of each energy storage device, which specifically includes:

[0079] Convert the underlying optimization objective function and its constraints into standard form:

[0080] For the power limit constraint P i,min ≤P i,t ≤P i,max , introduce slack variables s1 and residual variables s2, and transform them into equality constraints P i,t +s1=P i,max and P i,t -s2=P i,min For other inequality constraints, such as capacity limit constraints and charge and discharge times limit constraints, appropriate slack variables or residual variables are also introduced to transform the entire objective function and constraint system into the standard form of linear programming.

[0081] Construct the initial simplex table:

[0082] According to the standardized constraint coefficients and objective function coefficients, an initial simplex table is constructed. The rows of the table correspond to various constraints, including power limit, capacity limit, charge and discharge times limit, and power balance within the substation; the columns correspond to all variables, including decision variables (charging and discharging power P of energy storage equipment, etc.). i,t-1 ), slack variables and residual variables, such as s1, s2, etc., and objective function coefficients.

[0083] Starting from the initial simplex table, through a series of pivot operations, the table is updated according to the rules of the input and output variables until the preliminary charging and discharging strategy is obtained:

[0084] Starting from the initial simplex table, the iterative solution process begins. First, an entry variable is selected. Usually, a non-basic variable with the greatest improvement potential in the objective function coefficient is selected, that is, the variable that has the greatest impact on the objective function value and satisfies certain conditions (such as non-negativity, etc.). Then, the exit variable is determined according to the minimum ratio rule. This rule calculates the ratio of the right-hand constant in each constraint row to the coefficient of the column where the entry variable is located, and selects the basic variable corresponding to the row with the smallest and non-negative ratio as the exit variable to ensure the feasibility of the solution. Next, the axis rotation operation is performed, and the simplex table is updated through a series of matrix operations to change the position and coefficient of the new entry variable and exit variable in the table accordingly. The above process is repeated until all the test numbers (the dot product of the constraint coefficient of the corresponding column and the current solution vector minus the objective function coefficient) are non-negative. At this time, the solution obtained is the optimal solution for the underlying optimization, that is, the preliminary charging and discharging strategy for each energy storage device.

[0085] Preferably, for the collection of parameter data of the tie line between substations, the power grid topology database can be connected to query and obtain detailed parameter information of the tie line between substations, including the impedance Z of the tie line. ij , Rated capacity P ij,max (determines the maximum power limit that the interconnection line can safely transmit), the minimum transmission power limit P ij,min These data are determined during the planning and construction phase of the power grid and stored in the database. The required interconnection line parameter data can be accurately extracted through data query statements. For the collection of load and energy storage equipment data in multiple substations, the analysis and prediction model of historical load data (such as the ARIMA model based on time series analysis, the neural network model in machine learning, etc.) is used to predict the future load power of each substation to obtain load power prediction data. The prediction cycle is matched with the underlying optimized dispatch cycle to enable unified power resource allocation at the regional level. At the same time, the current status data of energy storage equipment in each area is collected, including the remaining capacity E i,t , charge and discharge power P i,t These data are obtained by the underlying optimization calculation and uploaded to the coordination control module through the data transmission interface. During the data aggregation process, the data integrity check and abnormal value processing are performed to ensure the accuracy and reliability of the data. For the collection of voltage monitoring data, a communication connection can be established with the voltage monitoring device installed in the substation, and the real-time voltage amplitude V of the substation can be received and stored according to the sampling interval adapted to other data (such as the same as the sampling interval of the substation operation data or set according to the voltage stability requirements). i,t Data. These data reflect the voltage quality in the substation area and play a key role in the voltage stability part of the upper optimization objective function. In the data acquisition process, a data verification algorithm is used to ensure the validity of the voltage monitoring data and prevent erroneous data caused by data transmission errors or monitoring equipment failures from entering the optimization calculation process.

[0086] Preferably, the upper layer optimization objective function is:

[0087]

[0088] Among them, T is the scheduling period, M is the number of stations, P ij,t is the tie line power flowing from area i to area j at time t, V i,t is the voltage amplitude at time t, V ref is the reference voltage, α and β are weight coefficients used to balance the relative importance of power balance and voltage stability in the objective function; The purpose of this part is to minimize the power fluctuations of the interconnection lines between substations. By optimizing the charging and discharging strategies of energy storage devices and the power distribution of interconnection lines, the power exchange between substations will be smoother, and the impact of power mutations on grid stability will be reduced. The other part is dedicated to stabilizing the voltage amplitude of each station area at the reference voltage V ref Nearby, improve the voltage quality in the area and ensure the normal operation of power equipment. By adjusting the weight coefficients α and β, the priority of power balance and voltage stability in the optimization process can be flexibly adjusted according to the actual grid operation needs and key issues.

[0089] Further preferably, the constraints of the upper optimization objective function are:

[0090]

[0091] in, is the load power of the station area i at time t, N i is the number of energy storage devices in the area i, P k,t is the charging and discharging power of the kth energy storage device in the station area i at time t, is the power injected into the station i by the upper power grid at time t, P ij,min is the minimum transmission power of the tie line between area i and area j, P ij,max is the maximum transmission power of the tie line between area i and area j, V i,min is the minimum voltage amplitude allowed in the station area i, V i,max is the maximum voltage amplitude allowed in the station area i.

[0092] Preferably, the dynamic programming algorithm is used to calculate the final charging and discharging strategy of each energy storage device, which specifically includes:

[0093] Define phase and state variables, determine decision variables;

[0094] In the above, defining the phases and state variables specifically includes: dividing the scheduling period T into multiple phases, each phase corresponds to a specific time interval (such as the sampling interval in the bottom layer optimization or set according to the calculation accuracy requirements of the upper layer optimization). The state variables are defined as follows:

[0095] The state vector S of the energy storage equipment in each substation i , which contains information such as the remaining capacity and charging and discharging status of the energy storage equipment, comprehensively describes the operating status of the energy storage equipment in each substation at a certain moment, and provides basic data for determining decision variables and constructing state transfer equations.

[0096] Tie line power state vector P Lij, which indicates the power status of the interconnection lines between substations and reflects the real-time situation of power transmission between substations. It is of great significance for regional power balance and coordinated control.

[0097] The load power state vector P of the substation Li , which records the load power changes in each substation, is one of the important bases for judging the power demand of the substation and formulating the charging and discharging strategy of energy storage equipment;

[0098] Determining the decision variables includes determining the decision variables as the adjustment amount ΔP of the charging and discharging power of the energy storage equipment in each substation i,t And the adjustment amount ΔP of the tie line power distribution ij,t These decision variables will directly affect the operating state of the energy storage equipment and the power interaction between stations. Through the optimization calculation of the dynamic programming algorithm, the optimal decision variable values ​​at different stages and states are determined to achieve the objective function of the upper-level optimization.

[0099] Construct the state transfer equation and calculate the stage benefits and optimal indicator function:

[0100] Constructing the state transfer equation specifically includes: constructing the state transfer equation according to the power balance constraint of the substation, the capacity change equation of the energy storage device and the power transmission characteristics of the interconnection line.

[0101] The state transfer equation of the energy storage equipment capacity in the substation is S i,t+1 =S i,t +(ΔP i,t +P i,t )·η i,t Δt, where S i,t+1 is the state vector of the energy storage device in the station area i at time t+1, S i,t is the state vector of the energy storage device in station i at time t, ΔP i,t is the adjustment amount of the charging and discharging power of the i-th energy storage device in the station area i at time t, P i,t is the charging and discharging power of the i-th energy storage device in the station area i at time t obtained by the bottom layer optimization, η i,t is the charging and discharging efficiency of the i-th energy storage device in the i-th area at time t, Δt is the time interval. This equation describes the dynamic change process of the capacity of the energy storage device after considering the underlying preliminary strategy and the upper-level adjustment amount, and reflects the energy storage and release relationship between the energy storage device in different stages.

[0102] The state transfer equation of the power of the tie line between substations is P ij,t+1 =P ij,t +ΔP ij,t , where P ij,t+1 is the power of the tie line from area i to area j at time t+1, P ij,tis the power of the tie line from area i to area j at time t, ΔP ij,t is the adjustment amount of the tie line power distribution between the stations i and j at time t. This equation intuitively expresses the change of the tie line power according to the adjustment amount at different stages, reflecting the dynamic adjustment process of power transmission between stations.

[0103] The calculation phase benefits and optimal indicator functions specifically include:

[0104] Calculate the stage benefit r t , and its calculation formula is:

[0105]

[0106] Among them, r t is the stage benefit of stage t, V i,t+1 is the voltage amplitude at time t+1, and the stage benefit represents the change in the objective function value caused by adjusting the charging and discharging power of the energy storage equipment and the power distribution of the interconnection line at the current stage, which comprehensively considers the reduction of power fluctuations and how close the voltage is to the reference value By calculating the stage benefits, we can evaluate the contribution of the decision variable adjustment at each stage to the overall objective function; calculate the optimal indicator function f t (S t ), through the recursive relation where f t (S t ) is the state S from the next stage t t The minimum objective function value from the start to the end of the scheduling period, ΔP t is the power adjustment vector of the energy storage device at the current stage t, ΔP Lt is the tie line power adjustment vector at the current stage t, f t+1 (S t+1 ) is the state S from the next stage t+1 t+1 The optimal indicator function represents the minimum objective function value from the current stage to the end of the scheduling cycle. It realizes the recursive optimization process from back to front by continuously searching for the decision variable that minimizes the sum of the current stage benefit and the optimal indicator function of the next stage in each stage. During the calculation process, all possible combinations of decision variables need to be traversed and evaluated to determine the optimal decision path.

[0107] Backtracking from the last stage to solve the optimal decision variables to obtain the final charging and discharging strategy:

[0108] Starting from the last stage, according to the optimal indicator function and state transfer equation, the optimal decision variables of each stage are backtracked to solve. Specifically, the optimal decision information of each stage recorded in the process of calculating the optimal indicator function is used to deduce step by step from the last moment of the scheduling cycle. At each stage, according to the current state variables and the determined optimal decision variables, the state variables of the next stage are updated through the state transfer equation, and the final adjustment amount ΔP of the charging and discharging power of the energy storage device corresponding to this stage is determined. i,t The final adjustment of the tie line power distribution ΔP ij,t Through this backtracking solution, the final adjustment of the charging and discharging power of the energy storage equipment in each substation and the final adjustment of the power distribution of the interconnection line are finally obtained, thereby determining the overall optimal operation strategy of the regional distribution network. In the backtracking process, the calculation results and decision variables of each stage are carefully checked for consistency to ensure the accuracy and reliability of the optimal strategy.

[0109] The final distributed energy storage device scheduling plan obtained by the upper layer optimization is sent to the controller of each energy storage device through a secure and reliable data transmission channel. After receiving the scheduling instruction, the energy storage device controller accurately adjusts the charging and discharging power and working state of the energy storage device according to the instruction requirements. For example, according to the calculated charging and discharging power adjustment amount ΔP i,t , adjust the power output of the energy storage equipment in real time to achieve optimal energy storage and release while meeting the power demand of the substation and regional coordinated operation. During the execution process, the execution of the energy storage equipment is monitored in real time to ensure that the instructions are executed accurately.

[0110] In addition, during the process of the energy storage device executing the dispatching control instructions, the actual operating status of the energy storage device (such as actual charging and discharging power, remaining capacity, etc.) and the changes in the operating parameters of the distribution network (such as load power, voltage amplitude, tie line power, etc.) are continuously monitored through the data connection with the energy storage device and the substation monitoring system. The actual data monitored is compared and analyzed with the expected control target to calculate the control error. For example, the actual voltage amplitude is compared with the target value within the voltage constraint range to calculate the voltage deviation; the actual substation power balance is compared with the ideal power balance state to calculate the power deviation, etc. According to the control error, the intelligent control strategy model (including the relevant parameters and model structure of the bottom layer optimization and the upper layer optimization) is optimized and adjusted in real time using the feedback control algorithm. For example, if it is found that the voltage of a certain substation is continuously low and exceeds the voltage constraint range, the feedback control algorithm may adjust the voltage weight coefficient β in the upper optimization objective function to increase the importance of voltage stability, and recalculate the optimal strategy at the same time to adjust the charging and discharging power of the energy storage device and the tie line power distribution to improve the voltage level of the substation. Through continuous monitoring, comparison and adjustment, the dispatching and control methods can adapt to the dynamic changes in the distribution network operating environment, continuously improve the accuracy and adaptability of control, and ensure that the regional distribution network always maintains an efficient and stable operating state.

[0111] It should be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0112] Each embodiment of the present invention is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0113] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present invention is limited to these examples. Under the concept of the present invention, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present invention as described above, which are not provided in detail for the sake of simplicity.

[0114] While the invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications and variations of these embodiments will be apparent to those skilled in the art in light of the foregoing description.

[0115] One or more embodiments of the present invention are intended to cover all such replacements, modifications and variations that fall within the broad scope of the present invention. Therefore, any omissions, modifications, equivalent replacements, improvements, etc. made within the spirit and principles of one or more embodiments of the present invention should be included in the protection scope of the present invention.

Claims

1. A dispatching control method for a distributed energy storage platform, characterized in that: The scheduling control method comprises: Collect parameter data, substation operation data and cost-benefit data of each energy storage device; Update and substitute the parameter data of each energy storage device, the substation operation data and the cost-benefit data into the underlying optimization objective function and its constraints; Using a linear programming algorithm to calculate a preliminary charging and discharging strategy for each of the energy storage devices; Controlling the operation of the energy storage device based on the calculated preliminary charge and discharge strategy of each of the energy storage devices; Collect parameter data of interconnection lines between substations, load data of multiple substations and voltage monitoring data; Substituting the inter-substation tie line parameter data, the multi-substation load data, the voltage monitoring data and the calculated preliminary charge and discharge strategy of each energy storage device into the upper optimization objective function and its constraints; Using a dynamic programming algorithm to calculate the final charging and discharging strategy of each of the energy storage devices; The operation of the energy storage device is controlled based on the calculated final charge and discharge strategy of each of the energy storage devices.

2. The dispatching control method of the distributed energy storage platform according to claim 1, characterized in that: The underlying optimization objective function is: Among them, T is the scheduling period, C c is the charging cost coefficient, C d is the discharge cost coefficient, is the charging power of the i-th energy storage device at time t, is the discharge power of the i-th energy storage device at time t.

3. The dispatching control method of the distributed energy storage platform according to claim 2 is characterized in that: The constraints of the underlying optimization objective function are: Among them, P L,t is the load power of the substation at time t, N is the number of energy storage devices in the substation, P G,t is the power injected into the substation by the upper power grid at time t, P i,min is the minimum charge and discharge power of the i-th energy storage device, P i,max is the maximum charge and discharge power of the i-th energy storage device, E i,t is the remaining capacity of the i-th energy storage device at time t, E i,min is the minimum capacity of the i-th energy storage device, E i,max is the maximum capacity of the i-th energy storage device, N c,i is the cumulative charging times of the i-th energy storage device, N d,i is the cumulative discharge times of the i-th energy storage device, N c,max,i is the maximum allowed charging times of the i-th energy storage device, N d,max,i is the maximum allowed discharge times of the i-th energy storage device.

4. The dispatching control method of a distributed energy storage platform according to claim 1, characterized in that: The use of a linear programming algorithm to calculate the preliminary charge and discharge strategy of each energy storage device specifically includes: Converting the underlying optimization objective function and its constraints into a standard form; Construct the initial simplex table; Starting from the initial simplex table, through a series of axis rotation operations, the table is updated according to the rules of the input and output variables until the preliminary charging and discharging strategy is obtained.

5. The dispatching control method of a distributed energy storage platform according to claim 1, characterized in that: The upper layer optimization objective function is: Among them, T is the scheduling period, M is the number of stations, P ij,t is the tie line power flowing from area i to area j at time t, V i,t is the voltage amplitude at time t, V ref is the reference voltage, α and β are weight coefficients.

6. The dispatching control method of the distributed energy storage platform according to claim 5, characterized in that: The constraints of the upper optimization objective function are: in, is the load power of the station area i at time t, N i is the number of energy storage devices in the area i, P k,t is the charging and discharging power of the kth energy storage device in the station area i at time t, is the power injected into the station i by the upper power grid at time t, P ij,min is the minimum transmission power of the tie line between area i and area j, P ij,max is the maximum transmission power of the tie line between area i and area j, V i,min is the minimum voltage amplitude allowed in the station area i, V i,max is the maximum voltage amplitude allowed in the station area i.

7. The dispatching control method of the distributed energy storage platform according to claim 5, characterized in that: The use of a dynamic programming algorithm to calculate the final charge and discharge strategy of each energy storage device specifically includes: Define phase and state variables, determine decision variables; Construct state transfer equations, calculate stage benefits and optimal indicator functions; The final charging and discharging strategy is obtained by backtracking from the last stage to solve the optimal decision variables.

8. The dispatching control method of the distributed energy storage platform according to claim 7, characterized in that: The defining phase and state variables and determining the decision variables specifically include: Divide the scheduling period T into multiple stages, each stage corresponds to a time interval; The state variables are defined as the energy storage device state vector of each substation, the tie line power state vector and the load power state vector of the substation; The decision variables are determined as the adjustment amount of the charging and discharging power of the energy storage equipment in each substation and the adjustment amount of the interconnection line power distribution.

9. The dispatching control method of the distributed energy storage platform according to claim 8, characterized in that: The state transfer equation includes the state transfer equation of the energy storage device capacity of the station area i and the state transfer equation of the tie line power between the stations; The state transfer equation of the energy storage device capacity of the station area i is: S i,t+1 =S i,t +(ΔP i,t +P i,t )·or i,t ·Δt Among them, S i,t+1 is the state vector of the energy storage device in the station area i at time t+1, S i,t is the state vector of the energy storage device in station i at time t, ΔP i,t is the adjustment amount of the charging and discharging power of the i-th energy storage device in the i-th area at time t, P i,t is the charging and discharging power of the i-th energy storage device in the station area i at time t obtained by the bottom layer optimization, η i,t is the charging and discharging efficiency of the i-th energy storage device in the station area i at time t, and Δt is the time interval; The state transfer equation of the tie line power between the substations is: P ij,t+1 =P ij,t +ΔP ij,t Among them, P ij,t+1 is the power of the tie line from area i to area j at time t+1, P ij,t is the power of the tie line from area i to area j at time t, ΔP ij,t It is the adjustment amount for the power distribution of the interconnection line between areas i and j at time t.

10. The dispatching control method of the distributed energy storage platform according to claim 9, characterized in that: The calculation formula of the stage benefit is: Among them, r t is the stage benefit of stage t, V i,t+1 is the voltage amplitude at time t+1; The optimal index function is: Among them, f t (S t ) is the state S from the next stage t t The minimum objective function value from the start to the end of the scheduling period, ΔP t is the power adjustment vector of the energy storage device at the current stage t, ΔP Lt is the tie line power adjustment vector at the current stage t, f t+1 (S t+1 ) is the state S from the next stage t+1 t+1 The minimum objective function value from the start to the end of the scheduling period.

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