CN114925880B - Virtual energy storage power plant distributed cooperation method based on inaccurate alternating direction multiplier method - Google Patents
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Abstract
The invention provides a distributed cooperation method of a virtual energy storage power plant based on a non-precise alternating direction multiplier method, wherein the virtual energy storage power plant is an effective means for realizing large-scale distributed energy storage grid-connected operation in a power distribution network, but large-scale equipment inside the virtual energy storage power plant is optimized and cooperated, so that the requirement of the power grid on response rapidity is often difficult to meet. The invention provides a virtual energy storage power plant distributed control framework for large-scale energy storage, which enables an energy storage polymer to be capable of avoiding power grid voltage out-of-limit while following a target load curve. Based on the non-precise alternate direction multiplier method, the closed-type analytic solution of the energy storage local optimization problem can be obtained in the original variable updating, the complexity of optimization calculation is obviously reduced, and the cooperative efficiency of the energy storage equipment is improved.
Description
Technical Field
The invention relates to the field of virtual energy storage, in particular to a distributed cooperation method of a virtual energy storage power plant based on a non-precise alternate direction multiplier method.
Background
Aiming at the aim that the carbon emission reaches the peak value before 2030 and the carbon neutralization is realized before 2060 in China, the national power grid accelerates the strategic layout, and the new energy power generation installation is guided to be driven to grid connection through a policy. Under the background of the utilization of renewable energy sources, energy storage becomes an important component and support technology of a smart grid and renewable energy source high-occupation specific energy source system, various auxiliary services such as frequency modulation, voltage regulation and the like are provided for the operation of an electric power system, and the level of the absorption of renewable energy sources such as wind, light and the like is effectively improved. Traditional centralized energy storage is limited by factors such as geographical conditions, and the increase of the installed capacity is in a year-by-year decreasing trend, which indicates that the demand-side distributed energy storage units are rapidly developed. In 2019, the national committee for improvement and the national energy bureau jointly release comments about the construction of the deepened electric power market, which clearly indicates that third parties such as energy storage facilities are encouraged to participate in auxiliary services of the electric power system.
The distributed energy storage has the characteristics of small single-machine capacity and huge quantity and scale, and the active and reactive power adjustment is more flexible. How to optimize and coordinate generalized energy storage devices with huge numbers and different characteristics in a power grid, and provide flexible and reliable power sources for the power grid, is a main target of research on a distributed energy storage control strategy. The virtual energy storage power plant is an extension of the concept of the virtual power plant and is an aggregation of a series of multi-element distributed energy storage systems, so that the virtual energy storage power plant has similar capacity and grid supporting capacity to those of a centralized energy storage power station, can be dispatched by a system operator like independent pumping and energy storage, solves the problem of the optimal utilization of energy storage resources in a covered wide area, and becomes an effective means for large-scale grid connection utilization of energy storage in a distribution network. Considering a large number of generalized distributed energy storage units in a future power grid, the traditional centralized control is difficult to bear corresponding communication cost, and the complete distributed control cannot achieve the effect of cooperative control through cooperation among the energy storage units. The distributed control adopting the adjacent communication principle has the advantages of strong interference resistance, good expansibility, plug and play, privacy protection and the like, is more suitable for the aggregation control of the distributed energy storage system in the current background, and also accords with the general trend of the decentralization of the power system.
The distributed optimization algorithm distributes the original centralized optimization problem to each agent for cooperative solution, so that the rapid optimal scheduling of resources in the system can be realized, but the difficulty is that an optimization problem distributed solution method with better convergence is obtained. A large number of existing researches use a distributed optimization method in frequency and voltage regulation of a power system, and the special optimization problem design can effectively utilize the sparsity characteristic of the power system structure by adopting an alternating direction multiplier algorithm (ALTERNATING DIRECTION METHOD OFMULTIPLIES, ADMM) and a dual decomposition method. Although the ADMM algorithm shows better convergence and robustness compared with the general first-order optimization method, considering that each iteration of the algorithm requires a new optimization problem, the problem of solving the optimal control by using the ADMM still needs to be improved in terms of rapidity and the like.
Disclosure of Invention
The invention provides a distributed cooperation method of a virtual energy storage power plant based on a non-precise alternating direction multiplier method, which mainly solves the problem of large-scale energy storage equipment cooperation optimization operation in the virtual energy storage power plant, reduces the calculation complexity of each energy storage intelligent body by adopting a non-precise dual optimization method, improves the cooperation efficiency among the energy storage equipment, and meets the requirements of load following and the response speed of a power grid to an energy storage polymer.
A virtual energy storage power plant distributed cooperation method based on a non-precise alternate direction multiplier method comprises the following steps:
step 1: determining distributed energy storage access positions in a power distribution network system, acquiring topology and structural parameters of the power distribution network system, and further acquiring a resistance and reactance matrix of a power distribution line of the system;
Step 2: determining voltage safety constraint of a power grid and operation constraint of energy storage equipment according to topology and structure parameters of the power distribution network system obtained in the step 1, and establishing an optimal scheduling model of a virtual energy storage power plant, wherein the optimal scheduling model aims at realizing target load curve following and reducing equipment use cost;
Step 3: aiming at each distributed energy storage intelligent agent in the virtual energy storage power plant, converting the optimized scheduling model of the virtual energy storage power plant established in the step 2 into a standard optimized model, and highlighting coupling and non-coupling constraint conditions in the standard optimized model by adopting a vectorization characterization method;
Step 4: converting the standard optimization model obtained in the step 3 into a form of a dual problem, and determining a distributed solving method of the standard optimization model in the dual problem: and constructing an augmented Lagrangian function of the standard optimization model, deducing an iterative updating method of original and dual variables by using a non-precise alternative direction multiplier method, distributing the iterative updating method to each energy storage intelligent agent for calculation, further realizing the distributed solution of the standard optimization model, and when the algorithm reaches a given maximum circulation number, using the iterative result of the original variable as the optimal power setting of the distributed energy storage equipment to guide the charge and discharge management of the actual equipment.
Further, for the power distribution network system in the step 1, the topology and structural parameters, the resistance and reactance matrix of the power distribution line of the system are obtained, and based on the topology and structural parameters, the resistance and reactance matrix, distFlow tide models of the system are obtained:
V＝RlPn+XlQn+V0 (1)
Rl＝M-TDrM-1,Xl＝M-TDxM-1 (2)
In formula (1), V represents a column vector composed of voltages of all nodes, and P n,Qn represents a column vector composed of injection power of all nodes, i.e All nodes in the system are defined by the set/>V 0＝1Nv0,v0 is the voltage at the common connection point of the distribution network, 1 N is the column vector with length N and 1 for all elements; equation (2) gives the resistance, reactance matrix/>, of the power distribution networkM is the system diagram description/>Is obtained by a system topological structure, D r、Dx is a diagonal matrix formed by all line resistances and reactances in epsilon respectively, and M -T is the inverse and transposed of M.
Further, the optimizing scheduling model in the step 2 is expressed as follows:
In the formula (3): active power and reactive power at time t of the ith energy storage device; the running cost of the energy storage is represented by quadratic form, and the parameter is/> Wherein/>Quadratic term coefficient representing active use cost of ith energy storage device tOne-time term coefficient representing active use cost of ith energy storage device tThe primary and secondary term coefficients of reactive power use cost at the moment t of the ith energy storage device are respectively represented;
In DistFlow trend model of the system according to equation (4), Column i of the system resistance and reactance matrix R l,Xl,/>, respectivelyLoad power of respective node i,/>A set of pure load nodes representing no stored energy;
In the formula (5), the amino acid sequence of the compound, The energy storage polymer has the ability of participating in power grid dispatching along with the instruction;
Equation (6) represents a capacity limit of the energy storage device, Reactive power of energy storage device,/>The apparent power capacity of the energy storage converter;
equation (7) represents the active power constraint of the energy storage device, where The minimum and maximum active capacity of energy storage;
equation (8) represents the charge capacity constraint of the stored energy, where SoC min,SoCmax is the minimum and maximum charge capacity constraint of the stored energy, The charge capacity at the time t of the ith energy storage is represented by eta, and the charge and discharge efficiency of the energy storage is represented by eta.
Further, the optimized scheduling model in the step 2 converts the quadratic constraint of the formula (6) into a linear constraint, so as to facilitate the subsequent solution:
Wherein the parameter κ is 8.
Further, the step 3 causesWhere x i,t is the decision variable at the i-th energy storage t time, Δx i,t represents the decision variable of each energy storage unit, expressed as the sum of the power output at the previous time and the power offset at the next time, and the standard optimization model is expressed as:
the formula (11) is a coupling constraint condition, the formula (12) is a non-coupling constraint condition, the Deltar i,t in the formula (12) is an introduced relaxation variable, and the conversion from inequality constraint to equality constraint is realized;
The parameter matrices and vectors A i、E、C、b0、e0、di of formulas (10) - (12) are obtained according to the optimization model (3) - (9), i.e
Wherein the method comprises the steps of
Further, in the step 4, firstly, a standard optimization model of the virtual energy storage power plant is processed, and artificial constraint is introduced to realize distributed solving:
In the formula (18), the components are as follows, G i,t(Δxi,t) and h i,t(Δxi,t) are the smooth and non-smooth portions of the objective function (10), respectively, and y i,t,zi,t is the lagrangian multiplier for the constraints of equation (11) and equation (12), respectively; t ij is an auxiliary variable, representing that y i,t of adjacent nodes are equal; l i is an introduced auxiliary variable for ensuring convexity of the subsequent model.
Further, in the step 4, the standard optimization model obtained by the formulas (18) - (20) can be solved in a distributed manner, and the augmentation Lagrangian function is constructed as follows:
Wherein the method comprises the steps of Lagrangian multipliers for constraint (19), (20);
Aiming at the extended Lagrangian function of the formula (21), the following imprecise alternating direction multiplier method is utilized to realize the complete distributed solving of the optimization model:
Where k represents the number of iterations, The given parameters of the algorithm are sigma, tau, c,/>Is an introduced algorithm state variable.
Further, for the original variable update described by the formula (21), according to the concept of imprecise minimization, deducing an analytical solution of the local optimization problem of each energy storage agent, and converting the formula (21) into:
Wherein the method comprises the steps of Representation/>For x and at/>Gradient values at, and
The optimal solution for equation (26) is:
Thus, according to the obtained And obtaining the optimal power setting of each energy storage device at the current moment.
The invention can ensure that the energy storage polymer can avoid the power grid voltage out-of-limit while following the target load curve, can obtain a closed-type analytic solution of the energy storage local optimization problem in the original variable updating based on the inaccurate alternating direction multiplier method, obviously reduces the complexity of optimization calculation and improves the cooperative efficiency of energy storage equipment.
Drawings
FIG. 1 is a schematic flow chart of the non-precise alternate direction multiplier method of the present invention;
FIG. 2 is a distributed control architecture diagram of a virtual energy storage power plant in an IEEE 33 node distribution network of the present invention;
FIG. 3 is a graph of virtual energy storage plant control effects based on the imprecise alternating direction multiplier method in an IEEE33 node system;
FIG. 4 is a graph of virtual energy storage plant control effects based on the imprecise alternating direction multiplier method in an IEEE69 node system.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention provides a virtual energy storage power plant distributed cooperation method based on a non-precise alternate direction multiplier method, which comprises the following steps:
step 1: the structure of the power distribution network for implementing energy storage distributed voltage support is shown in fig. 2, topology and structural parameters of the power distribution network system are obtained, and a resistance and reactance matrix of the power distribution network system is obtained according to formulas (1) - (2).
Step 2: defining the voltage safety constraint of an actual power grid and the operation constraint of energy storage system equipment, and constructing an optimized scheduling model of the virtual energy storage power plant according to formulas (3) - (8);
Step 3: and (3) defining the coupling and uncoupled constraint conditions of each energy storage intelligent agent in the virtual energy storage power plant, converting the optimized scheduling model constructed in the step (2) into a standard optimized model in the formulas (10) - (12), and determining a parameter matrix according to the formulas (14) - (16).
Step 4: the standard optimization model is distributed and solved by using a non-precise alternating direction multiplier method (shown in figure 1), the allowable voltage range of the power distribution network is selected to be 0.95-1.05 p.u., the voltage at the public connection point of the power distribution network is set to be 1p.u., 20 energy storage devices and 4 photovoltaic devices are connected into the power distribution network, sigma=0.01, tau=0.05, beta=2e3 and c=1 are adopted for the distributed optimization algorithm adopted by the invention, The rated power of the stored energy is randomly selected in the range of 0.5MW-1 MW.
Finally, the voltage supporting effect of the distributed energy storage in the example is shown in fig. 3, fig. 3 (a) shows the aggregate power of the virtual energy storage power plant, and the virtual energy storage power plant can accurately follow the target load curve under the distributed and centralized control framework; FIG. 3 (b) shows the operating costs of the virtual energy storage power plant under a centralized and distributed framework, it can be seen that the operating costs of the virtual energy storage power plant under distributed control are slightly higher than those of the centralized control; fig. 3 (c) compares the voltage supporting effect of the centralized and distributed control with fig. 3 (d), and it can be seen that the virtual energy storage power plant successfully avoids the voltage out-of-limit while following the target load curve in two control modes. FIG. 4 shows that the virtual energy storage power plant in the IEEE 69 node system achieves similar control effects as the IEEE33 node system, wherein the centralized control strategy can give the optimal power setting of the energy storage device within 1 minute, and the proposed distributed control can achieve similar energy storage synergistic effects within 10 seconds.
The foregoing is merely illustrative embodiments of the present invention, and the present invention is not limited thereto, and any changes or substitutions that may be easily contemplated by those skilled in the art within the scope of the present invention should be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (5)
1. The distributed cooperation method of the virtual energy storage power plant based on the inaccurate alternating direction multiplier method is characterized by comprising the following steps of:
step 1: determining distributed energy storage access positions in a power distribution network system, acquiring topology and structural parameters of the power distribution network system, and further acquiring a resistance and reactance matrix of a power distribution line of the system;
Step 2: determining voltage safety constraint of a power grid and operation constraint of energy storage equipment according to topology and structure parameters of the power distribution network system obtained in the step 1, and establishing an optimal scheduling model of a virtual energy storage power plant, wherein the optimal scheduling model aims at realizing target load curve following and reducing equipment use cost;
Step 3: aiming at each distributed energy storage intelligent agent in the virtual energy storage power plant, converting the optimized scheduling model of the virtual energy storage power plant established in the step 2 into a standard optimized model, and highlighting coupling and non-coupling constraint conditions in the standard optimized model by adopting a vectorization characterization method;
Step 4: converting the standard optimization model obtained in the step 3 into a form of a dual problem, and determining a distributed solving method of the standard optimization model in the dual problem: constructing an augmented Lagrangian function of a standard optimization model, deducing an iterative updating method of original and dual variables by using a non-precise alternative direction multiplier method, distributing the iterative updating method to each energy storage intelligent agent for calculation, further realizing distributed solving of the standard optimization model, and when an algorithm reaches a given maximum circulation number, using an iterative result of the original variable as optimal power setting of distributed energy storage equipment to guide charge and discharge management of actual equipment;
The optimized scheduling model in the step2 is expressed as follows:
In the formula (3): active power and reactive power at time t of the ith energy storage device; the running cost of the energy storage is represented by quadratic form, and the parameter is/> Wherein/>Quadratic term coefficient representing active use cost of ith energy storage device tOne-time term coefficient representing active use cost of ith energy storage device tThe primary and secondary term coefficients of reactive power use cost at the moment t of the ith energy storage device are respectively represented;
In DistFlow trend model of the system according to equation (4), Column i of the system resistance and reactance matrix R l,Xl,/>, respectivelyLoad power of respective node i,/>A set of pure load nodes representing no stored energy;
In the formula (5), the amino acid sequence of the compound, The energy storage polymer has the ability of participating in power grid dispatching along with the instruction;
Equation (6) represents a capacity limit of the energy storage device, Reactive power of energy storage device,/>The apparent power capacity of the energy storage converter;
equation (7) represents the active power constraint of the energy storage device, where The minimum and maximum active capacity of energy storage;
equation (8) represents the charge capacity constraint of the stored energy, where SoC min,SoCmax is the minimum and maximum charge capacity constraint of the stored energy, The charge capacity at the time t of the ith energy storage is represented by eta, and the charge and discharge efficiency of the energy storage is represented by eta;
Said step 3 is to Where x i,t is the decision variable at the i-th energy storage t time, Δx i,t represents the decision variable of each energy storage unit, expressed as the sum of the power output at the previous time and the power offset at the next time, and the standard optimization model is expressed as:
the formula (11) is a coupling constraint condition, the formula (12) is a non-coupling constraint condition, the Deltar i,t in the formula (12) is an introduced relaxation variable, and the conversion from inequality constraint to equality constraint is realized;
The parameter matrices and vectors A i、E、C、b0、e0、di of formulas (10) - (12) are obtained according to the optimization model (3) - (9), i.e
Wherein the method comprises the steps of
In the step 4, firstly, a standard optimization model of the virtual energy storage power plant is processed, and artificial constraint is introduced to realize distributed solving:
In the formula (18), the components are as follows, And h i,t(Δxi,t) are the smooth and non-smooth portions of the objective function (10), respectively, and y i,t,zi,t is the Lagrangian multiplier for the constraints of equation (11) and equation (12), respectively; t ij is an auxiliary variable, representing that y i,t of adjacent nodes are equal; l i is an introduced auxiliary variable for ensuring convexity of the subsequent model.
2. The distributed cooperation method of the virtual energy storage power plant based on the non-precise alternate direction multiplier method according to claim 1, wherein, for the power distribution network system in the step 1, topology and structural parameters thereof and resistance and reactance matrixes of power distribution lines of the system are obtained, and based on the topology and structural parameters, a DistFlow tide model of the system is obtained:
V＝RlPn+XlQn+V0 (1)
Rl＝M-TDrM-1,Xl＝M-TDxM-1 (2)
Formula 91) where V represents a column vector composed of voltages of all nodes and P n,Qn represents a column vector composed of injection power of all nodes, i.e All nodes in the system are defined by the set/>V 0＝1Nv0,v0 is the voltage at the common connection point of the distribution network, 1 N is the column vector with length N and 1 for all elements; the formula (2) gives the resistance and reactance matrix R,/>, of the power distribution networkM is the system diagram description/>Is obtained by a system topological structure, D r、Dx is a diagonal matrix formed by all line resistances and reactances in epsilon respectively, and M -T is the inverse and transposed of M.
3. The distributed collaborative method for a virtual energy storage power plant based on a non-precise alternating direction multiplier method according to claim 1, wherein the optimized scheduling model in step2 converts quadratic constraints of equation (6) into linear constraints to facilitate subsequent solutions:
Wherein the parameter κ is 8.
4. The distributed collaborative method for a virtual energy storage power plant based on a non-precise alternating direction multiplier method according to claim 1, wherein the standard optimization model obtained in step 4 for equations (18) - (20) can be solved in a distributed manner, and the augmented lagrangian function is constructed as follows:
Wherein the method comprises the steps of Lagrangian multipliers for constraint (19), (20);
Aiming at the extended Lagrangian function of the formula (21), the following imprecise alternating direction multiplier method is utilized to realize the complete distributed solving of the optimization model:
Where k represents the number of iterations, The given parameters of the algorithm are sigma, tau, c,/>Is an introduced algorithm state variable.
5. The virtual energy storage power plant distributed cooperation method based on the imprecise alternating direction multiplier method according to claim 4, wherein for the original variable update described by the formula (21), according to the imprecise minimization idea, the analytic solution of the local optimization problem of each energy storage agent is derived, and the formula (21) is converted into:
Wherein the method comprises the steps of Representation/>For x and at/>Gradient values at, and
The optimal solution for equation (26) is:
Thus, according to the obtained And obtaining the optimal power setting of each energy storage device at the current moment.
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