Centralized Shared Energy Storage Optimal Configuration Method Based on Alternating Direction Method of Multipliers

Abstract

The present invention relates to a centralized shared energy storage optimization configuration method based on the alternating direction method of multipliers, which includes: establishing a shared energy storage planning model based on centralized scheduling; decomposing it into a shared energy storage supplier-side optimization sub-model and a grid-side optimization sub-model; calculating the shared variables on the shared energy storage supplier side; transmitting the shared variables from the shared energy storage supplier-side optimization sub-model to the grid-side optimization sub-model; calculating the shared variables on the grid side; substituting them into the alternating direction method of multipliers (ADMM) convergence criterion. If the convergence criterion is satisfied, stop calculating the shared variables in the shared energy storage supplier-side optimization sub-model and the grid-side optimization sub-model, and output the optimal value of the shared energy storage capacity. Otherwise, update the dual variables and perform iterative calculations. The present invention does not need to rely on a single decision-making entity to collect data and perform power generation scheduling for the centralized optimization model of the power grid and the shared energy storage operator, which conforms to the characteristics of multi-agent decision-making of the power grid and the shared energy storage operator in reality.

Description

Technical Field

[0001] The present invention relates to the technical field of using shared energy storage to absorb new energy power generation in regional power grids, and in particular to a centralized shared energy storage optimal configuration method based on the alternating direction method of multipliers. Background Technique

[0002] Developing new energy is an important measure to ensure the sustainable development of the economy and society. However, when a high proportion of new energy is connected to the power system, the stable operation of the power system is challenged, resulting in a large proportion of wind and light abandonment. How to absorb new energy power generation has become an urgent problem to be solved. Energy storage is a key link in the power grid and plays an important role in balancing intermittent power output and electricity load. Electrochemical energy storage is restricted by safety performance, electricity price policies, business models, etc. In this context, with the vigorous development of the sharing economy and energy storage technology, the shared energy storage technology has emerged. Centralized shared energy storage can regard all energy storage devices on the grid side, energy storage operator side, and new energy power station side as a whole, and through coordinated control and overall management at different levels, jointly provide power auxiliary services for new energy power stations and power grids within a certain regional scope.

[0003] Since the centralized optimization method relies on a single decision-making entity to collect data and perform power generation scheduling for the centralized optimization model of the power grid and shared energy storage operators, there are practical problems such as large communication volume, complex model, and information confidentiality. In the actual power system, the energy storage stations and the power grid do not completely belong to the same entity, and some information confidentiality is required between the entities, and it is unrealistic to perform complete information interaction.

[0004] At present, the research on the optimal configuration of shared energy storage has just started. Most of the research applies shared energy storage to small and medium-sized power systems and regards its parameters as constant values. There is little research on the optimal configuration of shared energy storage capacity under the condition of high proportion of new energy access, and the data privacy between different stakeholders is not protected when configuring parameters. Therefore, how to set reasonable parameters to avoid wasting the constructed capacity while protecting the data privacy of different stakeholders is an urgent issue that shared energy storage currently needs to pay attention to. Summary of the Invention

[0005] The purpose of the present invention is to provide a centralized shared energy storage optimal configuration method based on the alternating direction method of multipliers, which uses ADMM to construct a distributed autonomous decision-making and collaborative optimization model for the power grid and shared energy storage operators, avoids the disadvantages of centralized solution, conforms to the characteristics of multi-agent decision-making of the power grid and shared energy storage operators in reality, further reduces the amount of wind and light abandonment, and promotes the absorption of new energy.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A centralized shared energy storage optimal configuration method based on the alternating direction method of multipliers, the method includes the following steps in sequence:

[0007] (1) Establish a shared energy storage planning model based on centralized scheduling;

[0008] (2) Decompose the shared energy storage planning model based on centralized scheduling into an optimization sub-model on the shared energy storage provider side and an optimization sub-model on the grid side using the Alternating Direction Method of Multipliers (ADMM) decomposition mechanism;

[0009] (3) In the optimization sub-model on the shared energy storage provider side, with the goal of minimizing the investment and construction costs of centralized shared energy storage and the charging and discharging power costs of shared energy storage, calculate the supply volume of shared energy storage capacity, the supply volume of shared energy storage charging power, and the supply volume of shared energy storage discharging power

[0010] (4) The optimization sub-model on the shared energy storage provider side transfers the shared variables to the optimization sub-model on the grid side: the supply volume of shared energy storage capacity, the supply volume of shared energy storage charging power, and the supply volume of shared energy storage discharging power

[0011] (5) In the optimization sub-model on the grid side, with the goals of optimizing new energy consumption, peak shaving capacity, and cost on the grid, calculate the demand volume of shared energy storage capacity, the demand volume of shared energy storage charging power, the demand volume of shared energy storage discharging power

[0012] (6) Substitute the supply volume of shared energy storage capacity, the supply volume of shared energy storage charging power, the supply volume of shared energy storage discharging power, the demand volume of shared energy storage capacity, the demand volume of shared energy storage charging power, the demand volume of shared energy storage discharging power into the ADMM convergence criterion. If the convergence criterion is met, stop calculating the shared variables in the optimization sub-model on the shared energy storage provider side and the optimization sub-model on the grid side, and output the optimal value of shared energy storage capacity; otherwise, go to step (7);

[0013] (7) Update the dual variable and perform iterative calculations: update the Lagrange multiplier coefficient λ k 、 k = k + 1, return to step (3) for the next round of iterative optimization calculations until the shared variables: the supply volume of shared energy storage capacity, the supply volume of shared energy storage charging power, the supply volume of shared energy storage discharging power converge.

[0014] Step (1) specifically includes the following steps:

[0015] (1a) Taking the shared energy storage capacity as the decision variable and aiming at the minimum total cost of the average daily investment and construction cost, charging power cost, discharging power cost, wind curtailment penalty cost, PV curtailment penalty cost, grid connection power fluctuation penalty, and unit operation cost of the centralized shared energy storage, the objective function is obtained as follows:

[0016] minW = W SES + W pc + W pd + W w + W pw + W D + W g (1)

[0017] In the formula: W SES is the average daily investment and construction cost of the centralized shared energy storage; W pc is the charging power cost of the shared energy storage; W pd is the discharging power cost of the shared energy storage; W w is the wind curtailment penalty cost; W pw is the PV curtailment penalty cost; W D is the grid connection power fluctuation penalty; W g is the unit operation cost;

[0018] Among them, the calculation formula for the average daily investment and construction cost of the centralized shared energy storage is:

[0019]

[0020] In the formula: λ p is the power cost of the shared energy storage; λ s is the capacity cost of the shared energy storage; is the charge and discharge power limit of the shared energy storage; T s is the expected service life of the shared energy storage;

[0021] The calculation formulas for the charging power cost and discharging power cost of the shared energy storage are as follows:

[0022]

[0023]

[0024] In the formula: represents the charging power of the shared energy storage at time t; represents the discharging power of the shared energy storage at time t; λ pc1 and λ pc2 are both charging power cost coefficients; λ pd1, λ pd2 are both the cost coefficients of the discharge power;

[0025] The calculation formulas for the curtailment-of-wind penalty cost and the curtailment-of-solar penalty cost are as follows:

[0026]

[0027]

[0028]

[0029]

[0030] Where: λ w1 , λ w2 are both the curtailment-of-wind power penalty cost coefficients; λ pw1 , λ pw2 are both the curtailment-of-solar power penalty cost coefficients; is the curtailment-of-wind power at time t; is the curtailment-of-solar power at time t; is the predicted power generation output of wind power generation at time t; is the actual output of wind power at time t; is the predicted power generation output of the photovoltaic power station at time t; is the actual output of the photovoltaic power station at time t;

[0031] The calculation formula for the grid-connected power fluctuation penalty is as follows:

[0032]

[0033] Where: λ D is the penalty coefficient for the power deviation between the optimized load power and the target load power; is the optimized load power at time t;

[0034] The calculation formula for the unit operation cost of the generator set is as follows:

[0035]

[0036] Where: λ g is the unit generator output cost at time t; P g is the generator output power at time t; a, b, and c are all the fitting coefficients of the generator output cost;

[0037] (1b) The above optimization objectives need to satisfy the following constraint conditions: power balance constraint, shared energy storage capacity constraint, shared energy storage charge and discharge power constraint, relationship constraint between shared energy storage capacity and charge and discharge power, node power balance constraint, line transmission capacity constraint, generator output constraint, generator ramp rate constraint, wind curtailment constraint, and photovoltaic curtailment constraint; the above constraint conditions and the objective function of formula (1) together constitute a shared energy storage planning model based on centralized scheduling;

[0038] Among them, the power balance constraint is:

[0039]

[0040] In the formula: is the generator power at time t;

[0041] The shared energy storage capacity constraint is:

[0042]

[0043]

[0044]

[0045] In the formula: is the real-time capacity of the shared energy storage at time t; and respectively represent the charging and discharging efficiencies of the shared energy storage;

[0046] The shared energy storage charge and discharge power constraint is:

[0047]

[0048]

[0049] In the formula: and respectively represent the upper and lower limits of the shared energy storage charging; and respectively represent the upper and lower limits of the shared energy storage discharging; κSES is a Boolean variable;

[0050] The relationship constraint between shared energy storage capacity and charge and discharge power is:

[0051]

[0052] In the formula: β represents the proportionality coefficient between the rated capacity and power limit of the shared energy storage;

[0053] The node power balance constraint is:

[0054] P = B0θ (18)

[0055] Where: P = B0θ represents the active power matrix injected at the nodes; B0 represents the network node susceptance matrix under normal operation; θ represents the node voltage phase angle matrix;

[0056] The line transmission capacity constraint is:

[0057]

[0058] Where: and respectively represent the upper and lower limits of the line transmission capacity; θ i and θ j respectively represent the voltage phase angles of node i and node j; B ij represents the DC susceptance of the line;

[0059] The output constraint of the generator set is:

[0060]

[0061] Where: and respectively represent the upper and lower limits of the generator set power;

[0062] The ramp rate constraint of the generator set is:

[0063]

[0064] Where: and respectively represent the upper and lower limits of the ramp rate of the generator set;

[0065] The wind curtailment constraint and the photovoltaic curtailment constraint are:

[0066]

[0067]

[0068] In step (2), the objective function of the optimization sub-model on the side of the shared energy storage operator is:

[0069]

[0070] Where, W SES is the average daily investment and construction cost of the centralized shared energy storage; W pc is the charging power cost of the shared energy storage; W pd is the discharging power cost of the shared energy storage; is the penalty term, which couples the decision variables on the power grid side and the energy storage supplier side through the penalty term:

[0071]

[0072] In the formula: k represents the number of iterations; λ k 、 are both Lagrange multiplier coefficients; ρ, ρ1, and ρ2 are all penalty factors; is the required shared energy storage capacity obtained after the k-th iteration on the grid side; are respectively the required charging and discharging power of the shared energy storage obtained after the k-th iteration on the grid side;

[0073] The constraint conditions of the optimization sub-model on the shared energy storage operator side are the same as those of the shared energy storage planning model based on centralized scheduling.

[0074] The constraint conditions of the optimization sub-model on the shared energy storage operator side and the objective function of formula (24) together constitute the optimization sub-model on the shared energy storage operator side.

[0075] In step (2), the objective function of the optimization sub-model on the grid side is:

[0076]

[0077] Among them, W w is the penalty cost for wind curtailment; W pw is the penalty cost for light curtailment; W D is the penalty for grid-connected power fluctuation; W g is the unit operation cost; is the penalty term, which couples the decision variables on the grid side and the shared energy storage operator side through the penalty term:

[0078]

[0079] In the formula: is the energy storage capacity supply obtained after the (k + 1)-th iteration on the energy storage supplier side, are respectively the charging and discharging power supplies of the shared energy storage obtained after the (k + 1)-th iteration on the energy storage supplier side;

[0080] The constraint conditions of the optimization sub-model on the grid side are the same as those of the shared energy storage planning model based on centralized scheduling.

[0081] The constraint conditions of the optimization sub-model on the grid side and the objective function of formula (26) together constitute the optimization sub-model on the grid side.

[0082] Step (3) specifically includes the following steps:

[0083] (3a) Initialization: Assign the iteration number k as 1, and given the initial residual tolerance upper limit ε, penalty factors ρ, ρ1, ρ2, and the initial values of the Lagrange multiplier coefficients λ 0 、 Set the initial value of the shared energy storage capacity demand on the grid side Initial value of the charging and discharging power demand on the grid side

[0084] (3b) Call the YALMIP toolbox and the GUROBI commercial solver. In the optimization sub-model on the shared energy storage provider side, calculate and solve to obtain the initial value of the shared energy storage capacity supply of the shared variable Initial value of the charging and discharging power supply on the shared energy storage provider side

[0085] (3c) Perform iterative calculations until the shared variables: shared energy storage capacity supply Shared energy storage charging power supply Shared energy storage discharging power supply Converge. At this time, obtain the shared energy storage capacity supply Shared energy storage charging power supply Shared energy storage discharging power supply

[0086] The specific steps of step (5) include the following steps:

[0087] (5a) The grid side receives the shared energy storage capacity supply Shared energy storage charging power supply And the shared energy storage discharging power supply

[0088] (5b) Call the YALMIP toolbox and the GUROBI commercial solver, calculate the grid side optimization sub-model, and obtain the shared energy storage capacity demand Shared energy storage charging power demand Shared energy storage discharging power demand

[0089] The specific meaning of step (6) is:

[0090] Judge the convergence according to the following formula:

[0091]

[0092] In the formula: ε is the upper limit of the original residual tolerance; Is the shared energy storage capacity demand for the k-th iteration; Is the shared energy storage capacity supply for the k-th iteration; Is the grid side charging power demand for the k-th iteration; Is the grid side charging power supply for the k-th iteration; Is the grid side discharging power demand for the k-th iteration; The grid - side discharge power supply for the k - th iteration;

[0093] If the convergence criterion is met, stop the calculation and output the optimal value of the shared energy storage capacity; otherwise, enter step (7) for the next round of iterative optimization calculation until convergence.

[0094] The specific content of step (7) is as follows:

[0095] Update the Lagrange multiplier coefficient λ according to the following formula k and

[0096]

[0097] In the formula, ρ, ρ1, and ρ2 are penalty factors.

[0098] Update the iteration coefficient k = k + 1, and return to step (3) for the next round of iterative optimization calculation until the formula (28) converges.

[0099] As can be seen from the above technical solutions, the beneficial effects of the present invention are as follows: First, the present invention does not need to rely on a single decision - making entity to collect data and perform power generation scheduling for the centralized optimization model of the power grid and the shared energy storage operator, which conforms to the multi - subject decision - making characteristics of the power grid and the shared energy storage operator in reality; Second, it has small communication volume, strong autonomy, and strong flexibility. The power grid entity and the shared energy storage operator entity can make autonomous decisions and collaborative optimizations; Third, it has good information confidentiality. Compared with centralized solution, it can protect the data privacy of different interest subjects. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] Figure 1 is the flowchart of the method of the present invention;

[0101] Figure 2 is the distributed collaborative optimization framework diagram of the power grid and the shared energy storage investment operator in the present invention;

[0102] Figure 3 is the convergence curve diagram of the shared variable residuals in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0103] As Figure 1 shown, a centralized shared energy storage optimization configuration method based on the alternating direction multiplier method, the method includes the following steps in sequence:

[0104] (1) Establish a shared energy storage planning model based on centralized scheduling;

[0105] (2) Based on the ADMM decomposition mechanism of the alternating direction multiplier method, decompose the shared energy storage planning model based on centralized scheduling into a shared energy storage supplier - side optimization sub - model and a grid - side optimization sub - model;

[0106] (3) In the sub-optimization model on the side of the shared energy storage provider, with the goal of optimizing the investment and construction costs of centralized shared energy storage and the charging and discharging power costs of shared energy storage, calculate the supply quantity of the shared energy storage capacity on the side of the shared energy storage provider. Supply quantity of the shared energy storage charging power and supply quantity of the shared energy storage discharging power

[0107] (4) The sub-optimization model on the side of the shared energy storage provider transfers shared variables to the sub-optimization model on the grid side: supply quantity of the shared energy storage capacity Supply quantity of the shared energy storage charging power and supply quantity of the shared energy storage discharging power

[0108] (5) In the sub-optimization model on the grid side, with the goals of optimizing the consumption of new energy on the grid, peak shaving capacity, and cost, calculate the demand quantity of the shared energy storage capacity on the grid side Demand quantity of the shared energy storage charging power Demand quantity of the shared energy storage discharging power

[0109] (6) Substitute the supply quantity of the shared energy storage capacity Supply quantity of the shared energy storage charging power Supply quantity of the shared energy storage discharging power Demand quantity of the shared energy storage capacity Demand quantity of the shared energy storage charging power Demand quantity of the shared energy storage discharging power into the convergence criterion of the Alternating Direction Method of Multipliers (ADMM). If the convergence criterion is satisfied, stop calculating the shared variables in the sub-optimization model on the side of the shared energy storage provider and the sub-optimization model on the grid side, and output the optimal value of the shared energy storage capacity. Otherwise, go to step (7);

[0110] (7) Update the dual variable for iterative calculation: update the Lagrange multiplier coefficient λ k 、 k = k + 1, return to step (3) for the next round of iterative optimization calculation until the shared variables: supply quantity of the shared energy storage capacity Supply quantity of the shared energy storage charging power Supply quantity of the shared energy storage discharging power converge.

[0111] The specific steps of step (1) include the following steps:

[0112] (1a) Taking the shared energy storage capacity as the decision variable and aiming at the minimum of the sum of the average daily investment and construction cost, the charging power cost, the discharging power cost, the wind curtailment penalty cost, the light curtailment penalty cost, the grid connection power fluctuation penalty, and the unit operation cost of the centralized shared energy storage, the objective function is obtained as follows:

[0113] minW = W SES + W pc + W pd + W w + W pw + W D + W g (1)

[0114] In the formula: W SES is the average daily investment and construction cost of the centralized shared energy storage; W pc is the charging power cost of the shared energy storage; W pd is the discharging power cost of the shared energy storage; W w is the wind curtailment penalty cost; W pw is the light curtailment penalty cost; W D is the grid connection power fluctuation penalty; W g is the unit operation cost;

[0115] Among them, the calculation formula for the average daily investment and construction cost of the centralized shared energy storage is:

[0116]

[0117] In the formula: λ p is the power cost of the shared energy storage; λ s is the capacity cost of the shared energy storage; is the charge and discharge power limit of the shared energy storage; T s is the expected service life of the shared energy storage;

[0118] The calculation formulas for the charging power cost and the discharging power cost of the shared energy storage are as follows:

[0119]

[0120]

[0121] In the formula: represents the charging power of the shared energy storage at time t; represents the discharging power of the shared energy storage at time t; λ pc1 and λ pc2 are both charging power cost coefficients; λ pd1 and λ pd2 are both discharging power cost coefficients;

[0122] The calculation formulas for the curtailment penalty cost of wind power and the curtailment penalty cost of photovoltaic power are as follows:

[0123]

[0124]

[0125]

[0126]

[0127] Where: λ w1 , λ w2 are both curtailment power penalty cost coefficients for wind power; λ pw1 , λ pw2 are both curtailment power penalty cost coefficients for photovoltaic power; is the curtailment power of wind power in period t; is the curtailment power of photovoltaic power in period t; is the predicted power generation output of wind power generation in period t; is the actual output of wind power in period t; is the predicted power generation output of the photovoltaic power station in period t; is the actual output of the photovoltaic power station in period t;

[0128] The calculation formula for the grid-connected power fluctuation penalty is as follows:

[0129]

[0130] Where: λ D is the penalty coefficient for the power deviation between the optimized load power and the target load power; is the optimized load power at time t;

[0131] The calculation formula for the unit operation cost of the generator set is as follows:

[0132]

[0133] Where: λ g is the unit generator set output cost in period t; P g is the output power of the generator set in period t; a, b, and c are all fitting coefficients for the unit output cost;

[0134] (1b) The above optimization objectives need to satisfy the following constraint conditions: power balance constraint, shared energy storage capacity constraint, shared energy storage charge and discharge power constraint, relationship constraint between shared energy storage capacity and charge and discharge power, node power balance constraint, line transmission capacity constraint, generator set output constraint, generator set ramp rate constraint, wind curtailment amount constraint, and photovoltaic curtailment amount constraint; The above constraint conditions and the objective function of formula (1) together constitute a shared energy storage planning model based on centralized scheduling;

[0135] Among them, the power balance constraint is:

[0136]

[0137] In the formula: is the power of the generator set in period t;

[0138] The shared energy storage capacity constraint is:

[0139]

[0140]

[0141]

[0142] In the formula: is the real-time capacity of the shared energy storage in period t; and respectively represent the charging and discharging efficiencies of the shared energy storage;

[0143] The shared energy storage charge and discharge power constraint is:

[0144]

[0145]

[0146] In the formula: and respectively represent the upper and lower limits of the shared energy storage charging; and respectively represent the upper and lower limits of the shared energy storage discharging; K SES is a Boolean variable, with a value of 1 when selected and 0 otherwise, ensuring that the energy storage does not charge and discharge simultaneously; Equation (15) can ensure that the shared energy storage provides long-term stable services; Equation (16) can prevent capacity over-limitation.

[0147] The constraint on the relationship between the shared energy storage capacity and the charge and discharge power is:

[0148]

[0149] In the formula: β represents the proportionality coefficient between the rated capacity and the power limit of the shared energy storage;

[0150] The node power balance constraint is:

[0151] P = B0θ (18)

[0152] In the formula: P = B0θ represents the active power matrix injected into the node; B0 represents the network node susceptance matrix during normal operation; θ represents the node voltage phase angle matrix;

[0153] The line transmission capacity constraint is:

[0154]

[0155] In the formula: and respectively represent the upper and lower limits of the line transmission capacity; θ i and θ j respectively represent the voltage phase angles of node i and node j; B ij represents the direct current susceptance of the line;

[0156] The output constraint of the generator set is:

[0157]

[0158] In the formula: and respectively are the upper and lower limits of the generator set power;

[0159] The ramp rate constraint of the generator set is:

[0160]

[0161] In the formula: and respectively are the upper and lower limits of the generator set ramp rate;

[0162] The curtailment of wind power constraint and the curtailment of photovoltaic power constraint are:

[0163]

[0164]

[0165] Since the centralized optimization method relies on a single decision-making entity to collect data and perform power generation scheduling for the centralized optimization model of the power grid and the shared energy storage operator, there are practical problems such as large communication volume, poor autonomy, poor flexibility, and information confidentiality. In the actual power system, the energy storage stations and the power grid do not completely belong to the same entity, and some information confidentiality is required between the entities. It is not realistic to perform complete information interaction. To avoid the disadvantages of centralized solution, aiming at the characteristics of multi-agent decision-making of the power grid and the shared energy storage operator in reality, the ADMM is used to construct a distributed autonomous decision-making and collaborative optimization model for the power grid and the shared energy storage operator.

[0166] In step (2), the objective function of the optimization sub-model on the side of the shared energy storage operator is:

[0167]

[0168] In the formula, W SES is the average daily investment and construction cost of the centralized shared energy storage; Wpc is the charging power cost of shared energy storage; W pd is the discharging power cost of shared energy storage; is the penalty term, which couples the decision variables on the grid side and the energy storage provider side through the penalty term:

[0169]

[0170] In the formula: k represents the number of iterations; λ k 、 are both Lagrange multiplier coefficients; ρ, ρ1, and ρ2 are all penalty factors; is the required capacity of shared energy storage obtained after the k-th iteration on the grid side; are the required charging and discharging power of shared energy storage obtained after the k-th iteration on the grid side, respectively;

[0171] The constraint conditions of the optimization sub-model on the shared energy storage operator side are the same as those of the shared energy storage planning model based on centralized scheduling.

[0172] In step (2), the objective function of the optimization sub-model on the grid side is:

[0173]

[0174] Among them, W w is the curtailment penalty cost of wind power; W pw is the curtailment penalty cost of photovoltaic power; W D is the penalty for grid-connected power fluctuation; W g is the unit operation cost; is the penalty term, which couples the decision variables on the grid side and the shared energy storage operator side through the penalty term:

[0175]

[0176] In the formula: is the supplied energy storage capacity obtained after the (k + 1)-th iteration on the energy storage provider side, are the supplied charging and discharging power of shared energy storage obtained after the (k + 1)-th iteration on the energy storage provider side, respectively;

[0177] The constraint conditions of the optimization sub-model on the grid side are the same as those of the shared energy storage planning model based on centralized scheduling.

[0178] Step (3) specifically includes the following steps:

[0179] (3a) Initialization: Assign the iteration number k as 1, and give the initial residual tolerance upper limit ε, penalty factors ρ, ρ1, ρ2, and the initial values of the Lagrange multiplier coefficients λ 0 、 Set the initial value of the shared energy storage capacity demand on the grid side Initial value of the charging and discharging power demand on the grid side

[0180] (3b) Call the YALMIP toolbox and the GUROBI commercial solver. In the optimization sub-model on the shared energy storage provider side, calculate and solve to obtain the initial value of the shared energy storage capacity supply of the shared variable Initial value of the charging and discharging power supply on the shared energy storage provider side

[0181] (3c) Perform iterative calculations until the shared variables: shared energy storage capacity supply Shared energy storage charging power supply Shared energy storage discharging power supply Converge. At this time, obtain the shared energy storage capacity supply Shared energy storage charging power supply Shared energy storage discharging power supply

[0182] The specific steps of step (5) are as follows:

[0183] (5a) The grid side receives the shared energy storage capacity supply Shared energy storage charging power supply And the shared energy storage discharging power supply

[0184] (5b) Call the YALMIP toolbox and the GUROBI commercial solver, calculate the grid side optimization sub-model, and obtain the shared energy storage capacity demand Shared energy storage charging power demand Shared energy storage discharging power demand

[0185] The specific meaning of step (6) is:

[0186] Judge the convergence according to the following formula:

[0187]

[0188] In the formula: ε is the upper limit of the original residual tolerance; Is the shared energy storage capacity demand in the k-th iteration; Is the shared energy storage capacity supply in the k-th iteration; Is the grid side charging power demand in the k-th iteration; Is the grid side charging power supply in the k-th iteration; Is the grid side discharging power demand in the k-th iteration; The grid - side discharge power supply for the k - th iteration;

[0189] If the convergence criterion holds, stop the calculation and output the optimal value of the shared energy storage capacity. Here, the optimal value of the shared energy storage capacity refers to the shared variable of the optimization sub - model on the shared energy storage provider side and the grid - side optimization sub - model when the calculation stops; otherwise, enter step (7) for the next round of iterative optimization calculation until convergence.

[0190] The specific content of step (7) is as follows:

[0191] Update the Lagrange multiplier coefficient λ according to the following formula k 、

[0192]

[0193] In the formula, ρ, ρ1, and ρ2 are penalty factors.

[0194] Update the iteration coefficient k = k + 1, and return to step (3) for the next round of iterative optimization calculation until the formula (28) converges.

[0195] As Figure 2 shown, the optimization sub - model on the shared energy storage provider side and the grid - side optimization sub - model are constructed respectively. Through a small number of information transmissions between the power grid and the energy storage operator, iterative calculations are carried out, and finally, the purpose of autonomous decision - making and collaborative optimization on the grid - side and the shared energy storage operator side to achieve the optimal objective function of the centralized shared energy storage is achieved. To decompose the optimization problem, variables common to the objective functions of the power grid and the shared energy storage investment operator, namely the shared energy storage investment and construction capacity and the energy storage charge - discharge power, can be selected as the collaborative shared variables of the centralized shared energy storage optimization construction model. Aiming at the characteristics of multi - subject decision - making between the power grid - side and the shared energy storage power station in reality, the ADMM algorithm, that is, the alternating direction method of multipliers (ADMM), is used to establish a shared energy storage planning model based on centralized scheduling. Based on the ADMM algorithm, the centralized shared energy storage optimization problem is decomposed into a sub - problem on the energy storage operator side and a sub - problem on the grid - side. The energy storage operator side aims to optimize the investment and construction cost and the charge - discharge power cost of the centralized shared energy storage, and the grid - side aims to optimize the new energy consumption of the power grid, the peak - shaving capacity, and minimize the total operating cost of the regional power grid. Through a small number of information exchanges between the two sub - problems, the collaboration and optimization between the energy storage operator and the power grid are realized.

[0196] As Figure 3 shown, it can be seen from the figure that this mode has a relatively fast convergence. Since at the initial stage of iteration, due to the initial value of the shared energy storage capacity demand on the grid - side The initial value of the charge - discharge power demand on the grid - side is 0, so the residual value is relatively high. As the iteration progresses, the capacity and charge-discharge power of the energy storage station are getting closer and closer to the grid side, so the residual value gradually decreases until it approaches 0. From Figure 3 it can be seen that the shared variable of the present invention is updated quickly and can converge quickly.

[0197] In summary, the present invention does not need to rely on a single decision-making entity to collect data and perform power generation scheduling for the centralized optimization model of the power grid and the shared energy storage operator, which conforms to the characteristics of multi-agent decision-making of the power grid and the shared energy storage operator in reality; the present invention has small communication volume, strong autonomy and strong flexibility, and the power grid entity and the shared energy storage operator entity can make autonomous decisions and cooperate for optimization; the present invention has good information confidentiality and can protect the data privacy of different interested parties compared with centralized solution.

Claims

1. A centralized shared energy storage optimization configuration method based on the alternating direction method of multipliers, characterized in that: The method includes the following steps in sequence: (1) Establish a shared energy storage planning model based on centralized scheduling; (2) Decompose the shared energy storage planning model based on centralized scheduling into an optimization sub-model on the shared energy storage provider side and an optimization sub-model on the grid side based on the alternating direction multiplier method (ADMM) decomposition mechanism; (3) In the shared energy storage provider-side optimization sub-model, with the goal of minimizing the investment and construction costs of centralized shared energy storage and the costs of the charging and discharging power of shared energy storage, the supply volume of shared energy storage capacity on the shared energy storage provider side is calculated. Supply volume of shared energy storage charging power and the supply volume of shared energy storage discharging power (4) The shared energy storage provider-side optimization sub-model transfers shared variables to the grid-side optimization sub-model: the shared energy storage capacity supply The shared energy storage charging power supply and the shared energy storage discharging power supply (5) In the grid-side optimization sub-model, with the goal of optimizing new energy consumption, peak shaving capacity, and cost on the grid side, calculate the required capacity of the shared energy storage on the grid side Required charging power of the shared energy storage Required discharging power of the shared energy storage (6) Substitute the shared energy storage capacity supply Shared energy storage charging power supply Shared energy storage discharging power supply Shared energy storage capacity demand Shared energy storage charging power demand Shared energy storage discharging power demand Substitute into the convergence criterion of the Alternating Direction Method of Multipliers (ADMM). If the convergence criterion holds, stop calculating the shared variables in the shared energy storage supplier-side optimization sub-model and the grid-side optimization sub-model, and output the optimal value of the shared energy storage capacity. Otherwise, go to step (7); (7) Update the iterative calculation of dual variables: Update the Lagrange multiplier coefficient λ k , k = k + 1, return to step (3) for the next round of iterative optimization calculation until the shared variables: shared energy storage capacity supply Shared energy storage charging power supply Shared energy storage discharging power supply Converge.

2. The centralized shared energy storage optimization configuration method based on the alternating direction multiplier method according to claim 1, wherein: The specific steps of step (1) include the following steps: (1a) Taking the shared energy storage capacity as the decision variable, and aiming at the optimal sum of the average daily investment and construction cost of the centralized shared energy storage, the charging power cost of the shared energy storage, the discharging power cost of the shared energy storage, the penalty cost for wind curtailment, the penalty cost for light curtailment, the penalty for grid-connected power fluctuation, and the unit operation cost, the objective function is obtained as follows: minW = W SES + W pc + W pd + W w + W pw + W D + W g (1) Where: W SES is the average daily investment and construction cost of centralized shared energy storage; W pc is the charging power cost of shared energy storage; W pd is the discharging power cost of shared energy storage; W w is the penalty cost for curtailed wind; W pw is the penalty cost for curtailed light; W D is the penalty for grid-connected power fluctuation; W g is the unit operation cost; Among them, the calculation formula for the average daily investment and construction cost of the centralized shared energy storage is: Where: λ p is the power cost of the shared energy storage; λ s is the capacity cost of the shared energy storage; is the charge and discharge power limit of the shared energy storage; T s is the expected number of days of use of the shared energy storage; The calculation formulas for the charging power cost of the shared energy storage and the discharging power cost of the shared energy storage are as follows: In the formula: represents the charging power of the shared energy storage in the t period; represents the discharging power of the shared energy storage in the t period; λ pc1 and λ pc2 are both charging power cost coefficients; λ pd1 and λ pd2 are both discharging power cost coefficients; The calculation formulas for the penalty cost for wind curtailment and the penalty cost for light curtailment are as follows: Where: λ w1 , λ w2 are both curtailment power penalty cost coefficients for wind power; λ pw1 , λ pw2 are both curtailment power penalty cost coefficients for photovoltaic power; is the curtailment power of wind power at time t; is the curtailment power of photovoltaic power at time t; is the predicted power generation output of wind power at time t; is the actual power output of wind power at time t; is the predicted power generation output of a photovoltaic power station at time t; is the actual power output of a photovoltaic power station at time t; The calculation formula for the penalty for grid-connected power fluctuation is as follows: where: λ D is the penalty coefficient of the power deviation between the optimized load power and the target load power; is the optimized load power at time t; The calculation formula for the unit operation cost is as follows: where: λ g is the cost per unit generator output during period t; P g is the generator output power during period t; a, b, and c are all fitting coefficients of the generator output cost; (1b) The above optimization objectives need to satisfy the following constraint conditions: power balance constraint, shared energy storage capacity constraint, shared energy storage charge and discharge power constraint, relationship constraint between shared energy storage capacity and charge and discharge power, node power balance constraint, line transmission capacity constraint, generator output constraint, generator ramp rate constraint, wind curtailment amount constraint, and light curtailment amount constraint; The constraint conditions and the objective function of formula (1) together constitute the shared energy storage planning model based on centralized scheduling; Among them, the power balance constraint is: Where: is the power of the generator set in the t period; The shared energy storage capacity constraint is: Where: is the real-time capacity of the shared energy storage in the t period; and respectively represent the charging and discharging efficiencies of the shared energy storage; The shared energy storage charge and discharge power constraint is: In the formula: and respectively represent the upper and lower limits of the charging of the shared energy storage; and respectively represent the upper and lower limits of the discharging of the shared energy storage; κ SES is a Boolean variable; The relationship constraint between shared energy storage capacity and charge and discharge power is: In the formula: β represents the proportionality coefficient between the rated capacity and the power limit of the shared energy storage; The node power balance constraint is: P = B0θ (18) In the formula: P = B0θ represents the active power matrix injected into the node; B0 represents the network node susceptance matrix during normal operation; θ represents the node voltage phase angle matrix; The line transmission capacity constraint is: Wherein: and respectively represent the upper and lower limits of the line transmission capacity; θ i and θ j respectively represent the voltage phase angles of node i and node j; B ij represents the DC susceptance of the line; The generator output constraint is: Wherein: and are respectively the upper and lower limits of the power of the generator set; The generator ramp rate constraint is: Wherein: and are respectively the upper and lower limits of the ramp rate of the generator set; The wind curtailment amount constraint and the light curtailment amount constraint are:

3. The centralized shared energy storage optimization configuration method based on the alternating direction multiplier method according to claim 1, characterized in that: In step (2), the objective function of the optimization sub-model on the shared energy storage operator side is: Where, W SES is the average daily investment and construction cost of centralized shared energy storage; W pc is the charging power cost of shared energy storage; W pd is the discharging power cost of shared energy storage; is the penalty term, which couples the decision variables on the grid side and the energy storage supplier side through the penalty term: Where: k represents the number of iterations; λ k , are both Lagrange multiplier coefficients; ρ, ρ1, and ρ2 are all penalty factors; is the required capacity of the shared energy storage obtained after the k-th iteration on the grid side; are respectively the required charging and discharging powers of the shared energy storage obtained after the k-th iteration on the grid side; The constraint conditions of the optimization sub-model on the shared energy storage operator side are the same as those of the shared energy storage planning model based on centralized scheduling.

4. The centralized shared energy storage optimization configuration method based on the alternating direction multiplier method according to claim 1, wherein: In step (2), the objective function of the grid-side optimization sub-model is: Among them, W w is the curtailment penalty cost of wind power; W pw is the curtailment penalty cost of solar power; W D is the penalty for grid-connected power fluctuation; W g is the operation cost of the unit; is the penalty term, which couples the decision variables on the grid side and the shared energy storage operator side through the penalty term: In the formula: is the energy storage capacity supply obtained after the (k + 1)-th iteration on the energy storage provider side, are respectively the shared energy storage charging and discharging power supplies obtained after the (k + 1)-th iteration on the energy storage provider side; The constraint conditions of the grid-side optimization sub-model are the same as those of the shared energy storage planning model based on centralized scheduling.

5. The centralized shared energy storage optimization configuration method based on the alternating direction multiplier method according to claim 1, wherein: The specific steps of step (3) include the following steps: (3) In the optimization sub-model on the side of the shared energy storage provider, with the goal of minimizing the investment and construction costs of centralized shared energy storage and the costs of the charging and discharging power of shared energy storage, calculate the supply volume of the shared energy storage capacity on the side of the shared energy storage provider Supply volume of the shared energy storage charging power and the supply volume of the shared energy storage discharging power (3a) Initialization: Assign the iteration number k as 1, given the original residual tolerance upper limit ε, penalty factors ρ, ρ1, ρ2, and the initial value λ of the Lagrange multiplier coefficient 0 , Set the initial value of the shared energy storage capacity demand on the grid side Initial value of the charging and discharging power demand on the grid side (3b) Call the YALMIP toolbox and the GUROBI commercial solver. In the shared energy storage provider-side optimization sub-model, calculate and solve to obtain the initial value of the shared energy storage capacity supply on the shared energy storage provider side Initial value of the shared energy storage charging power supply Initial value of the shared energy storage discharging power supply (3c) Perform iterative calculations until the shared variables: shared energy storage capacity supply Shared energy storage charging power supply Shared energy storage discharging power supply Converge, at which point the shared energy storage capacity supply Shared energy storage charging power supply Shared energy storage discharging power supply 6. The centralized shared energy storage optimization configuration method based on the alternating direction multiplier method according to claim 1, wherein: The specific steps of step (5) include the following steps: (5a) The grid side receives the supply quantity of the shared energy storage capacity The supply quantity of the charging power of the shared energy storage and the supply quantity of the discharging power of the shared energy storage (5b) Call the YALMIP toolbox and the GUROBI commercial solver to calculate the grid-side optimization sub-model and obtain the required capacity of the shared energy storage Required charging power of the shared energy storage Required discharging power of the shared energy storage 7. The centralized shared energy storage optimization configuration method based on the alternating direction multiplier method according to claim 1, wherein: The specific meaning of step (6) is: Judge the convergence according to the following formula: where ε is the upper limit of the original residual tolerance; is the required capacity of the shared energy storage for the k-th iteration; is the supplied capacity of the shared energy storage for the k-th iteration; is the required grid-side charging power for the k-th iteration; is the supplied grid-side charging power for the k-th iteration; is the required grid-side discharging power for the k-th iteration; is the supplied grid-side discharging power for the k-th iteration; If the convergence criterion holds, stop the calculation and output the optimal value of the shared energy storage capacity; otherwise, enter step (7) for the next round of iterative optimization calculation until convergence; The specific meaning of step (7) is: Update the Lagrange multiplier coefficient λ according to the following formula k , In the formula, ρ, ρ1, and ρ2 are penalty factors, Update the iteration coefficient k = k + 1, and return to step (3) for the next round of iterative optimization calculation until formula (28) converges.

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