CN114925880A - Virtual energy storage power plant distributed cooperation method based on non-precise alternative 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 alternative 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 in the virtual energy storage power plant is optimized and cooperated, and the requirement of the power grid on the rapidity of response is often difficult to meet. The invention provides a distributed control framework of a virtual energy storage power plant for large-scale energy storage, which enables an energy storage polymer to follow a target load curve and simultaneously can avoid the out-of-limit of the voltage of a power grid. The method is based on the non-precise alternative direction multiplier method, the closed 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 cooperation 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 alternative direction multiplier method.
Background
Under the background of renewable energy utilization, energy storage becomes an important composition and support technology of a smart grid and a renewable energy high-occupancy energy system, various auxiliary services such as frequency modulation and voltage regulation are provided for the operation of a power system, and the consumption level of renewable energy such as wind and light is effectively improved. The traditional centralized energy storage is limited by factors such as geographical conditions, the installed capacity acceleration is in a descending trend year by year, and the demand-side distributed energy storage units are rapidly developed. In 2019, the national institutes of development and improvement and the national energy resource bureau jointly release opinions about deepening the construction of the electric power market, and clearly show 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 large quantity and scale, and the active and reactive adjustment of the distributed energy storage is more flexible. How to optimize and coordinate the large-quantity and different-characteristic generalized energy storage devices in the distribution network provides a flexible and reliable power source for the power grid is a main target of distributed energy storage control strategy research. The virtual energy storage power plant is an extension of the concept of the virtual power plant and is the aggregation of a series of multi-element distributed energy storage systems, so that the virtual energy storage power plant has the capacity and the power grid supporting capacity similar to those of a centralized energy storage power station, can be scheduled by a system operator like independent water pumping energy storage, solves the problem of optimal utilization of energy storage resources covering a large area, and becomes an effective means for large-scale grid-connected 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 the corresponding communication cost, and the completely distributed control cannot achieve the effect of cooperative control through the cooperation among the energy storage units. The distributed control adopting the adjacent communication principle has the advantages of strong anti-interference performance, 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 conforms to the general trend of decentralization of the power system.
The distributed optimization algorithm allocates the original centralized optimization problem to each intelligent agent for cooperative solution, can realize quick optimization scheduling of resources in the system, and has the difficulty of obtaining the distributed solution method of the optimization problem with better convergence. In a large number of existing researches, a distributed optimization method is used for frequency and voltage regulation of a power system, and special optimization problem design can effectively utilize the sparsity characteristic of the structure of the power system by adopting an Alternating Direction Method of Multiplies (ADMM) and a dual decomposition method. Although the ADMM algorithm embodies better convergence and robustness than a general first-order optimization method, considering that each iteration of the algorithm needs to solve a new optimization problem, the speed and the like of solving the optimal control problem by using the ADMM are still to be improved.
Disclosure of Invention
The invention provides a distributed cooperation method of a virtual energy storage power plant based on a non-precise alternative direction multiplier method, which mainly solves the problem of cooperative optimization operation of large-scale energy storage equipment 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 a power grid on the response speed of an energy storage polymer at the moment of load follow.
A virtual energy storage power plant distributed cooperation method based on a non-precise alternative direction multiplier method comprises the following steps:
step 1: determining a distributed energy storage access position in a power distribution network system, and acquiring topological and structural parameters of the power distribution network system so as to acquire a resistance matrix and a reactance matrix of a system power distribution line;
and 2, step: according to the topology and structure parameters of the power distribution network system obtained in the step 1, determining the voltage safety constraint of the power grid and the operation constraint of the energy storage equipment, and establishing an optimized scheduling model of the virtual energy storage power plant, wherein the optimized scheduling model aims at realizing target load curve following and reducing equipment use cost;
and step 3: aiming at each distributed energy storage 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 optimization model, and highlighting coupling and non-coupling constraint conditions in the standard optimization model by adopting a vectorization characterization method;
and 4, step 4: converting the standard optimization model obtained in the step 3 into a dual problem form, and determining a distributed solving method of the standard optimization model in the dual problem: and when the algorithm reaches a given maximum cycle number, the iteration result of the original variable is used as the optimal power setting of the distributed energy storage equipment to guide the charging and discharging management of the actual equipment.
Further, for the power distribution network system in the step 1, topology and structure parameters of the power distribution network system and resistance and reactance matrixes of system power distribution lines are obtained, and on the basis of the topology and structure parameters, a DistFlow power flow model of the system is obtained:
V＝R l P n +X l Q n +V 0 (1)
R l ＝M -T D r M -1 ,X l ＝M -T D x M -1 (2)
in formula (1), V represents a column vector consisting of all node voltages, P n ,Q n Representing a column vector formed by the injected power of all nodes, i.e.
All nodes in the system are aggregated
Is shown by V 0 ＝1 N v 0 ，v 0 For voltage at the point of common connection of the distribution network, 1 N Represents a column vector of length N with all elements 1; the formula (2) gives the resistance and reactance matrix of the power distribution network
M is a system diagram description
Is derived from the system topology, D r 、D x Respectively, a diagonal matrix formed by all the line resistances and reactances in epsilon, M -T Representing the inverse of M and transpose.
Further, the optimized scheduling model in step 2 is represented as:
in formula (3):
the active power and the reactive power of the ith energy storage equipment at the moment t; the running cost of stored energy is expressed by quadratic form with the parameter of
Wherein
A quadratic term coefficient representing the active use cost of the ith energy storage device at the moment t,
a first term coefficient representing the active use cost of the ith energy storage device at the moment t,
primary and secondary term coefficients respectively representing the reactive power use cost of the ith energy storage device at the moment t;
in the DistFlow power flow model according to the system in equation (4),
respectively representing system resistance and reactance matrix R l ,X l The (c) th column of (a),
the load power of the respective node i,
indicating that there is no set of pure load nodes storing energy;
in the formula (5), the reaction mixture is,
the target load curve represents that the energy storage polymer has the capacity of participating in power grid dispatching along with the instruction;
equation (6) represents the capacity limit of the energy storage device,
is the reactive power of the energy storage device,
apparent power capacity of the energy storage converter;
equation (7) represents the active power constraint of the energy storage device, where
Minimum and maximum active capacity for energy storage;
equation (8) represents the charge capacity constraint of the stored energy, where SoC min ,SoC max For the minimum and maximum charge capacity limits of energy storage,
the charging capacity at the ith energy storage time t is shown, and eta is the charging and discharging efficiency of the energy storage.
Further, the optimal scheduling model in step 2 converts the quadratic constraint of equation (6) into a linear constraint to facilitate subsequent solution:
where the parameter k takes 8.
Further, the step 3 is that
Wherein x is i,t Is the decision variable, Δ x, at the ith moment of energy storage t i,t And representing a decision variable of each energy storage unit in the form of the sum of the power output at the last moment and the power offset at the next moment, wherein the standard optimization model is represented as:
equation (11) is a coupling constraint condition, equation (12) is a non-coupling constraint condition, and Δ r in equation (12) i,t For the introduced relaxation variables, the conversion from inequality constraint to equality constraint is realized;
parameter matrices and vectors A of equations (10) - (12) i 、E、C、b 0 、e 0 、d i Are obtained according to the optimization models (3) - (9), i.e.
Wherein
Further, in the step 4, firstly, a standard optimization model of the virtual energy storage power plant is processed, and manual constraint is introduced to realize distributed solution:
in the formula (18), in the above,
g i,t (Δx i,t ) And h i,t (Δx i,t ) Respectively, smooth and non-smooth portions, y, of the objective function (10) i,t ,z i,t Respectively of formula (11) and formula (12)Lagrange multipliers of constraints; t is t ij Y representing adjacent nodes as auxiliary variables i,t Equal; l i The auxiliary variables are introduced to ensure the convexity of the subsequent model.
Further, the standard optimization model obtained by the equations (18) to (20) in the step 4 can be in a distributed solving form, and the augmented lagrangian function is constructed as follows:
wherein
Lagrange multipliers of the constraints (19) and (20);
aiming at the augmented Lagrangian function of the formula (21), the following non-precise alternative direction multiplier method is utilized to realize the complete distributed solution of the optimization model:
where k denotes the number of iterations, [ ■ ]] + Max (0, ■), given parameters of the algorithm are σ, τ, c,
is the introduced algorithm state variable.
Furthermore, aiming at the original variable update described by the formula (21), according to the non-precise minimization thought, an analytic solution of the local optimization problem of each energy storage intelligent agent is deduced, and the formula (21) is converted into:
wherein
To represent
To x and is at
A gradient value of (a) and
the optimal solution for equation (26) is:
thereby, according to what is obtained
And obtaining the optimal power setting of each energy storage device at the current moment.
The invention can avoid the out-of-limit of the power grid voltage while the energy storage polymer follows the target load curve, and can obtain a closed analytic solution of the energy storage local optimization problem in the original variable update based on the non-precise alternative direction multiplier method, thereby obviously reducing the complexity of optimization calculation and improving the cooperative efficiency of energy storage equipment.
Drawings
FIG. 1 is a schematic flow diagram of the non-exact alternative direction multiplier method of the present invention;
FIG. 2 is a diagram of a distributed control architecture for a virtual energy storage plant in an IEEE33 node power distribution network in accordance with the present invention;
FIG. 3 is a diagram of the effect of virtual energy storage plant control based on the imprecise alternative direction multiplier method in an IEEE33 node system;
FIG. 4 is a diagram of the effect of virtual energy storage plant control based on the inaccurate alternative direction multiplier method in an IEEE69 node system.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The embodiment of the invention provides a virtual energy storage power plant distributed cooperation method based on a non-precise alternative direction multiplier method, which comprises the following steps:
step 1: the structure of the power distribution network required to implement energy storage distributed voltage support is shown in fig. 2, topology and structure parameters of the power distribution network system are obtained, and a resistance matrix and a reactance matrix of the power distribution network system are obtained according to the formulas (1) - (2).
Step 2: determining voltage safety constraints of an actual power grid and operation constraints of energy storage system equipment, and constructing an optimal scheduling model of a virtual energy storage power plant according to formulas (3) - (8);
and step 3: and (3) defining coupling and non-coupling constraint conditions of each energy storage agent in the virtual energy storage power plant, converting the optimized scheduling model constructed in the step (2) into standard optimized models in the formulas (10) - (12), and determining a parameter matrix according to the formulas (14) - (16).
And 4, step 4: using non-exact alternative direction multipliersThe method (as shown in fig. 1) performs distributed solution on the standard optimization model, selects the allowed voltage range of the power distribution network as 0.95-1.05 p.u., the voltage at the public connection point of the power distribution network as 1p.u., and commonly accesses 20 energy storage devices and 4 photovoltaic devices in the power distribution network, and for the distributed optimization algorithm adopted by the invention, selects σ as 0.01, τ as 0.05, β as 2e3, c as 1,
the power rating of the stored energy is randomly selected in the range of 0.5MW to 1 MW.
Finally, the voltage support effect of distributed energy storage in this example is shown in fig. 3, and fig. 3(a) shows the aggregate power of the virtual energy storage power plants, so that the virtual energy storage power plants can accurately follow the target load curve under the distributed and centralized control framework; fig. 3(b) shows the operation cost of the virtual energy storage plant under the centralized and distributed frameworks, and it can be seen that the operation cost of the virtual energy storage plant under the distributed control is slightly higher than that under the centralized control; fig. 3(c) and fig. 3(d) compare the voltage support effect of centralized control and distributed control, and it can be seen that in the two control modes, the virtual energy storage power plant successfully avoids the voltage out-of-limit while following the target load curve. Fig. 4 shows that the virtual energy storage plant in the IEEE69 node system achieves similar control effect 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 effect within 10 seconds.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A distributed cooperation method of a virtual energy storage power plant based on a non-precise alternative direction multiplier method is characterized by comprising the following steps:
step 1: determining a distributed energy storage access position in a power distribution network system, and acquiring topological and structural parameters of the power distribution network system so as to acquire a resistance matrix and a reactance matrix of a system power distribution line;
step 2: according to the topology and structure parameters of the power distribution network system obtained in the step 1, determining the voltage safety constraint of a power grid and the operation constraint of energy storage equipment, and establishing an optimized scheduling model of a virtual energy storage power plant, wherein the optimized scheduling model aims at realizing target load curve following and reducing equipment use cost;
and 3, step 3: aiming at each distributed energy storage 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 optimization model, and highlighting coupling and non-coupling constraint conditions in the standard optimization model by adopting a vectorization characterization method;
and 4, step 4: converting the standard optimization model obtained in the step 3 into a dual problem form, and determining a distributed solving method of the standard optimization model in the dual problem: and when the algorithm reaches a given maximum cycle number, the iteration result of the original variable is used as the optimal power setting of the distributed energy storage equipment to guide the charging and discharging management of the actual equipment.
2. The virtual energy storage power plant distributed cooperation method based on the imprecise alternative direction multiplier method as defined in claim 1, wherein topology and structure parameters of the power distribution network system in the step 1 and resistance and reactance matrixes of system power distribution lines are obtained, and based on the topology and structure parameters, a DistFlow power flow model of the system is obtained:
V＝R l P n +X l Q n +V 0 (1)
R l ＝M -T D r M -1 ，X l ＝M -T D x M -1 (2)
in formula (1), V represents a column vector consisting of all node voltages, P n ，Q n Representing a column vector formed by the injected power of all nodes, i.e.
All nodes in the system are aggregated
Is represented by V 0 ＝1 N v 0 ，v 0 For voltages at common connection points of distribution networks, 1 N Represents a column vector of length N with all elements 1; the resistance and reactance matrix R of the power distribution network is shown in the formula (2),
M is a system diagram description
Is obtained from the system topology, D r 、D x Is a diagonal matrix formed by all the line resistances and reactances in epsilon, M -T Representing the inverse of M and transpose.
3. The distributed coordination method for virtual energy storage power plant based on non-precise alternative direction multiplier method according to claim 2, characterized in that said optimal scheduling model in step 2 is expressed as:
in formula (3):
the active power and the reactive power of the ith energy storage equipment at the moment t; the running cost of the stored energy is represented by a quadratic form with the parameters of
Wherein
A quadratic term coefficient representing the active use cost of the ith energy storage device at the moment t,
a first term coefficient representing the active use cost of the ith energy storage device at the moment t,
respectively representing the first time and the second time of the reactive use cost of the ith energy storage equipment at the moment tA term coefficient;
in the DistFlow power flow model according to the system in equation (4),
respectively represent a system resistance matrix and a reactance matrix R l ，X l The (c) th column of (a),
the load power of the respective node i,
representing the absence of a set of energy-storing pure load nodes;
in the formula (5), the reaction mixture is,
the target load curve represents that the energy storage polymer has the capacity of participating in power grid dispatching along with the instruction;
equation (6) represents the capacity limit of the energy storage device,
is the reactive power of the energy storage device,
apparent power capacity of the energy storage converter;
equation (7) represents the active power constraint of the energy storage device, where
Minimum and maximum active capacity for energy storage;
equation (8) represents the charge capacity constraint of the stored energy, where SoC min ，SoC max For the minimum and maximum charge capacity limits of energy storage,
the charging capacity at the ith energy storage time t is shown, and eta is the charging and discharging efficiency of the energy storage.
4. The distributed cooperation method of the virtual energy storage power plant based on the imprecise alternating direction multiplier method as defined in claim 3, wherein the optimal scheduling model in step 2 converts quadratic constraint of equation (6) into linear constraint to facilitate subsequent solution:
where the parameter k takes 8.
5. The distributed coordination method for virtual energy storage power plant based on non-precise alternative direction multiplier method according to claim 4, characterized in that said step 3 order
Wherein x i，t Is the decision variable, Δ x, at the ith moment of energy storage t i，t And representing a decision variable of each energy storage unit in a form of the sum of the power output at the last moment and the power offset at the next moment, wherein the standard optimization model is represented as follows:
equation (11) is a coupling constraint condition, equation (12) is a non-coupling constraint condition, and Δ r in equation (12) i，t For the introduced relaxation variables, the conversion from inequality constraint to equality constraint is realized;
parameter matrices and vectors A of equations (10) - (12) i 、E、C、b 0 、e 0 、d i Are obtained according to the optimization models (3) - (9), i.e.
Wherein
6. The distributed collaborative method of the virtual energy storage plant based on the imprecise alternative direction multiplier method of claim 5, wherein in step 4, a standard optimization model of the virtual energy storage plant is first processed, and artificial constraints are introduced to realize distributed solution:
in the formula (18), the reaction mixture is,
g i，t (Δx i，t ) And h i，t (Δx i，t ) Respectively smooth and non-smooth portions, y, in the objective function (10) i，t ，Z i，t Lagrange multipliers which are constraints of equations (11) and (12), respectively; t is t ij Y representing adjacent nodes as auxiliary variables i，t Equal; l. the i The auxiliary variables are introduced to ensure the convexity of the subsequent model.
7. The distributed cooperative method for the virtual energy storage power plant based on the imprecise alternative direction multiplier method as defined in claim 6, wherein the standard optimization model obtained in step 4 through equations (18) - (20) can be in a distributed solution form, and an augmented Lagrangian function of the standard optimization model is constructed as follows:
wherein
Lagrange multipliers with constraint conditions of (19) and (20);
aiming at the augmented Lagrangian function of the formula (21), the following non-precise alternative direction multiplier method is utilized to realize the complete distributed solution of the optimization model:
where k denotes the number of iterations, [ ■ ]] + Max (0, ■), given parameters of the algorithm are σ, τ, c,
is the introduced algorithm state variable.
8. The distributed coordination method for virtual energy storage power plant based on non-precise alternative direction multiplier method according to claim 7, characterized in that, for the original variable update described by equation (21), according to the non-precise minimization thought, the analytic solution of local optimization problem of each energy storage agent is derived, and equation (21) is converted into:
wherein
Represent
To x and in
A gradient value of (a) and
the optimal solution for equation (26) is:
thereby, according to what is obtained
And obtaining the optimal power setting of each energy storage device at the current moment.
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