CN111768311A - Micro-grid energy management system based on two-stage optimal charging strategy - Google Patents

Micro-grid energy management system based on two-stage optimal charging strategy Download PDF

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CN111768311A
CN111768311A CN202010563452.6A CN202010563452A CN111768311A CN 111768311 A CN111768311 A CN 111768311A CN 202010563452 A CN202010563452 A CN 202010563452A CN 111768311 A CN111768311 A CN 111768311A
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颜钢锋
丁俐夫
黎为
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Zhejiang University ZJU
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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    • H—ELECTRICITY
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    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract

The invention discloses a micro-grid energy management system based on a two-stage optimal charging strategy. Aiming at the current intelligent micro-grid architecture, the invention designs an energy management system model which can be widely applied to micro-grids of novel communities, novel parking lots, public charging stations and the like provided with plug-in hybrid electric vehicle charging facilities. In order to realize the load curve and economic distribution requirement of the energy management of the microgrid, a two-stage optimal charging strategy is adopted, a load curve 'peak clipping and valley filling' taken by an upper-level algorithm model is taken as an optimization target, a lower-level algorithm model is used for minimizing the charging cost of all users, the combined solution of the load curve of the microgrid and the charging cost of the users is realized by introducing a decision variable and a method for assigning algorithm priority, and a feasible scheme is provided for the energy management of the microgrid provided with a charging facility of a plug-in hybrid electric vehicle to the maximum extent.

Description

Micro-grid energy management system based on two-stage optimal charging strategy
Technical Field
The invention belongs to the field of comprehensive control of micro-grid energy, and particularly relates to a micro-grid energy management system based on a two-stage optimal charging strategy.
Background
In recent years, due to the problems caused by the shortage of fossil energy and environmental pollution, the plug-in hybrid vehicles have been receiving much attention. However, when the plug-in hybrid vehicle is connected to the grid in large quantities and out of order, new peak loads may be added or generated, resulting in overheating and overloading of the transformer, thereby compromising the safe and stable operation of the grid. With the gradual step-in of plug-in hybrid electric vehicles into the public view, the energy management problem of the micro-grid of the novel community equipped with the charging equipment, the parking lot and other environments can face new challenges.
In order to enable the optimal charging strategy of the electric automobile to be widely applied to micro-grid energy management, researchers carry out detailed research on an economically optimal coordinated charging algorithm. For example: patent document CN109094381A discloses an algorithm for integrating charging demand and grid load curves, which aims at peak clipping and valley filling, and incorporates user response to intervene in a time-of-use electricity price period by using a heuristic algorithm. Patent document CN110015090A realizes formulation of power sharing, thereby guiding the transition of charging behavior to orderliness. Patent document CN110733370A proposes a double-layer optimization algorithm capable of processing economic optimization and load curves, but the algorithm first specifies an optimal scheduling plan and then determines whether the load curves meet the requirements, and the interactivity between the two layers of algorithms is not well reflected. Therefore, in the current research, there is not yet a complete energy management system model, which is suitable for the current intelligent microgrid architecture. For the multi-objective optimization problem of simultaneously considering the load curve of the micro-grid and the charging cost of a user, no efficient solution is provided.
Disclosure of Invention
The invention provides a micro-grid energy management system based on a two-stage optimal charging strategy aiming at the defects of the existing energy management system architecture, and provides a feasible energy management scheme aiming at a micro-grid provided with a plug-in hybrid electric vehicle charging facility.
In order to achieve the above object, the technical solution of the present invention is as follows:
a micro-grid energy management system based on a two-stage optimal charging strategy comprises a hardware layer, an algorithm layer and an interaction part.
The hardware layer comprises an energy storage module, a scheduling module and a load module.
The energy storage module comprises a distributed power supply and configurable energy storage equipment in the microgrid. Storage battery, portable storage power station etc. energy storage equipment with fill electric pile can directly be used for inserting in the charging of electric formula hybrid vehicle.
The dispatching module comprises a micro-grid network frame and a power distribution station, a transformer and the like inside the micro-grid network frame.
The load module comprises a charging pile required by charging of the plug-in hybrid electric vehicle and other loads in the micro-grid.
The algorithm layer realizes the functions of two algorithms, namely a load balancing algorithm and an economic dispatching algorithm, and is used for solving the problem of a micro-grid load curve caused by disordered charging.
The load balancing algorithm is used for solving the problem of the load curve of the micro-grid, and the electric energy charged by the electric vehicle is distributed through the optimization algorithm, so that the total load curve at the side of the low-voltage transformer, namely the total curve of the charging load of the electric vehicle and other loads, is balanced as much as possible, and the purpose of 'peak clipping and valley filling' of the load curve is achieved.
The load balancing algorithm is as follows: within the time windows 1 to N-1, for m electric vehicle charging nodes, to make the curve of the electric vehicle charging schedule plan P with the sum of other loads Q the flattest, the problem can be described as a finite time domain optimization problem as follows:
Figure BDA0002546878050000021
wherein XkAll the electric energy, Q, provided to m electric vehicle charging nodes for the microgrid at time kkFor the other loads at time k, the load,
Figure BDA0002546878050000022
for the upper limit of the load of the charging node of the ith electric automobile, gamma is an ideal balanced power curve.
The economic dispatch algorithm is used to solve the electric vehicle charging dispatch plan that minimizes the charging cost for all users, which can be described as the following optimization problem:
Figure BDA0002546878050000023
wherein P isi(k) Electric energy supplied to the ith electric vehicle charging node for the microgrid at time k, diFor the electric automobile i in the time period [0, N-1]The electric energy required to be charged in the device,
Figure BDA0002546878050000024
and iP(k) the upper limit and the lower limit of the load of the charging node of the ith electric automobile are respectively set.
Further, all electric energy provided by the microgrid at the moment k to the m electric vehicle charging nodes
Figure BDA0002546878050000025
As an intermediate decision variable. The priority of the two optimization targets is defined as F optimization problem with high priority and G optimization problem with low priority. The load balancing and economic scheduling optimization problem can be described as a joint optimization problem as follows:
Figure BDA0002546878050000026
the joint optimization problem can be used for describing joint solution of a microgrid load curve and user charging cost, and the solution steps are as follows:
step (1): initializing the charge and discharge power and the iteration speed of an optimization algorithm;
step (2): loading a charging load into the system;
and (3): solving by a high-priority algorithm; obtaining a current optimal load curve;
and (4): acquiring the output of each unit and the total loss of the network under the condition of only considering the total number of the electric automobiles participating in the interaction;
and (5): when constraint conditions are considered, the result obtained by the high-priority problem changes, so that the low-priority algorithm needs to determine the sum of the active power loss of each node;
and (6): carrying out low-priority algorithm solution;
and (7): determining the optimal power;
and (8): judging whether the result of high priority is met, if so, entering the step (8), otherwise, updating the charge and discharge power, and returning to the step (2);
and (8): and outputting the result.
And (5): and judging whether the total loss is reduced or not. If yes, entering step (6); if not, entering the step (8);
and (6): carrying out low-priority algorithm solution; calculating the optimal power;
and (7): updating the charge and discharge power, and returning to the step (2);
and (8): and judging whether the power meets the result of high priority. If yes, outputting the result. If not, returning to the step (7).
The interaction part comprises interaction of a hardware layer and an algorithm layer, interaction of distributed energy sources in the microgrid and an external centralized power generation network, and interaction of the energy storage equipment and a charging user.
The invention has the beneficial effects that:
(1) the current intelligent micro-grid architecture is fully considered, and an energy management system model is designed aiming at the scenes of novel communities, novel parking lots, public charging stations and the like provided with plug-in hybrid electric vehicle charging facilities, so that the customization is simple and the application range is wide;
(2) a two-stage optimal charging strategy is adopted, a load curve 'peak clipping and valley filling' taken by an upper-level algorithm model is taken as an optimization target, and a lower-level algorithm model is used for minimizing the charging cost of all users, so that the energy balance of the micro-grid in the process of the disordered access of the electric vehicle is realized, and the charging cost of the users is reduced;
(3) decision variables and algorithm priorities are assigned in a two-stage optimal charging strategy, so that economic optimization and a load curve can be efficiently solved in a combined manner, which cannot be realized by the existing energy management method.
Drawings
FIG. 1 is a micro grid energy management system architecture based on a two-stage optimal charging strategy according to the present invention;
fig. 2 is an algorithm level flow diagram of the present invention implementing a dual-stage optimal charging strategy.
Detailed Description
The objects and effects of the present invention will become more apparent from the following detailed description of the present invention with reference to the accompanying drawings.
Fig. 1 is a diagram of a micro-grid energy management system architecture based on a dual-stage optimal charging strategy according to the present invention, the system comprising:
1. and a hardware layer. The method is designed based on the current intelligent micro-grid architecture, and is applied to scenes such as novel communities, novel parking lots, public charging stations and the like provided with plug-in hybrid electric vehicle charging facilities, and the scenes are simulated by adopting an open-source dynamic link library, so that customization is easy; the hardware layer specifically includes:
an energy storage module. The energy generation or storage facility of the microgrid energy management system comprises a distributed power supply in a microgrid and energy storage equipment which can be used for charging plug-in hybrid electric vehicles, such as a public storage battery, a mobile charging station and the like, wherein the energy storage module can transmit electric energy to a scheduling module for scheduling or directly used for charging the vehicles;
and a scheduling module. The micro-grid electric energy transmission system comprises a distribution station, a transformer, a direct current and alternating current grid frame and the like, and an electric energy transmission part in the micro-grid can realize the functions of information interaction, real-time energy processing and the like;
and a load module. The method comprises various loads in the micro-grid, wherein the problems of large peak-valley difference, power distribution network disturbance and the like brought by charging piles need to be processed by an algorithm layer;
2. and (4) an algorithm layer. The functions of load balancing and economic dispatching are realized by adopting a two-stage optimal charging strategy, so that the aim of efficiently solving two problems in a combined manner is fulfilled; the algorithm layer construction steps are as follows:
step (1): the method comprises the following specific steps of constructing and adopting a two-stage optimal charging strategy combined optimization target:
step (1-1): the microgrid load curve problem is described as a finite time domain optimization problem, and the definition of an objective function F of the microgrid on the energy X provided by all nodes is
Figure BDA0002546878050000041
XkAll the electric energy, Q, provided to m electric vehicle charging nodes for the microgrid at time kkThe other loads at time k. To make γ an ideal equilibrium power curve, the objective function F should be taken to be the minimum. The problem satisfies the following constraint conditions
Figure BDA0002546878050000042
That is, the micro-grid provides all the electric energy to the m electric automobile charging nodes at any time with upper limit
Figure BDA0002546878050000043
Step (1-2): describing the economic dispatching of the microgrid as an optimization problem, and defining an objective function G of the optimization problem as energy P provided by a variable microgrid to a certain node
Figure BDA0002546878050000044
In order to make the microgrid economically optimal, i.e. to minimize the user charging cost, the objective function G should be the minimum. The problem satisfies the following constraints:
Figure BDA0002546878050000045
the electric energy provided by the micro-grid to any electric vehicle charging node at any time is limited;
Figure BDA0002546878050000046
i.e. during the time period [0, N-1 ]]The inner micro-grid needs to provide electric energy d required by the electric automobile ii；
Step (1-3): defining decision variables
Figure BDA0002546878050000051
Two optimization objectives are combined as a joint optimization problem as follows:
Figure BDA0002546878050000052
Figure BDA0002546878050000053
Figure BDA0002546878050000054
step (1-4): adding the decision variables as constraint terms to the joint optimization problem;
step (1-5): defining the priority of two optimization targets, and setting the F optimization problem as high priority and the G optimization problem as low priority because the optimization variables of the F optimization problem are equal to the constraint of the G optimization problem;
step (1-6): an optimization problem that can be solved jointly is obtained, which is described as follows:
Figure BDA0002546878050000055
step (2): an algorithm is constructed to perform joint solution based on a two-stage optimal charging strategy, fig. 2 is an algorithm layer flow chart for implementing the two-stage optimal charging strategy, and the specific solution steps are as follows:
step (2-1): initializing the charge and discharge power of each electric vehicle charging node and the iteration speed of an optimization algorithm;
step (2-2): loading each node load using the V2G technique;
step (2-3): calculating an optimal load curve with high priority;
step (2-4): acquiring the output of each unit and the total loss of the network under the condition of only considering the total number of the electric automobiles participating in the interaction;
step (2-5): determining whether the sum of the active power loss of each node is continuously reduced or not so as to determine the convergence condition of the algorithm; if the total loss is reduced, namely the algorithm is not converged, entering the step (2-6), otherwise entering the step (2-8);
step (2-6): carrying out low-priority economic dispatching operation, and determining the optimal power of the electric automobile with each node participating in V2G interaction in different periods;
step (2-7): updating the charge and discharge power and returning to the step (2-2);
step (2-8): judging whether a result of high priority is met, if so, outputting the result, and if not, returning to the step (2-7);
solving the termination condition that the sum of the active power losses is not reduced continuously, namely the algorithm is converged, and the final scheduling plan meets the requirement of a load curve;
3. the interaction part comprises interaction between a hardware layer and an algorithm layer of the microgrid energy management system and interaction between the hardware layer and the outside, and specifically comprises the following 3 parts of interaction:
(1) the micro-grid energy management system adopts a distributed architecture in a hardware layer, information transmission among nodes is carried out through a communication module, the same communication protocol is adopted by all the nodes, an energy information stream is transmitted to an algorithm layer by the hardware layer to carry out operation solution, and a scheduling plan control quantity is returned by the algorithm layer;
(2) distributed energy in the micro-grid can interact with an external centralized power generation network to realize energy complementation;
(3) energy storage equipment such as public storage battery, portable charging station with fill electric pile and insert electric formula hybrid vehicle user and carry out the interaction, plan user's power consumption cost to satisfy user's charging demand.

Claims (6)

1. A micro-grid energy management system based on a two-stage optimal charging strategy is characterized in that: the system comprises a hardware layer, an algorithm layer and an interaction part;
the hardware layer is designed based on the current intelligent micro-grid architecture and is used for communities, parking lots and public charging stations with plug-in hybrid electric vehicle charging facilities; the energy storage system specifically comprises an energy storage module, a scheduling module and a load module;
the energy storage module comprises a distributed power supply in a microgrid and energy storage equipment for charging the plug-in hybrid electric vehicle, and the energy storage module transmits electric energy to the scheduling module for scheduling and is directly used for charging the plug-in hybrid electric vehicle;
the scheduling module is a part of grid structure for scheduling and transmitting electric energy in the microgrid, and comprises a power distribution station and a transformer in the microgrid grid structure;
the load module comprises various loads in a micro-grid in which the charging pile is arranged;
the algorithm layer uses a two-stage optimal charging strategy to realize the functions of load balancing and economic dispatching through a middle decision variable and an algorithm priority;
the interaction part comprises interaction between a hardware layer and the outside, and information transmission between nodes is carried out through a communication module, so that interaction between the hardware layer and an algorithm layer of the microgrid energy management system is realized.
2. The microgrid energy management system of claim 1, based on a dual-stage optimal charging strategy, characterized in that: the construction of the two-stage optimal charging strategy algorithm comprises the following steps:
2.1, constructing a joint optimization target, describing the micro-grid load curve problem as a limited time domain optimization problem, and describing the micro-grid economic dispatching as an optimization problem;
2.2 defining decision variables
Figure FDA0002546878040000011
The physical meaning of the electric energy is that the micro-grid provides all electric energy for m electric vehicle charging nodes at the moment k;
2.3 adding the decision variables as constraint items to the joint optimization problem, defining the priority of two optimization targets, and describing the two problems as a joint optimization problem solved as follows;
Figure FDA0002546878040000012
wherein XkAll the electric energy, Q, provided to m electric vehicle charging nodes for the microgrid at time kkFor other loads at time k, γ is the ideal balanced power curve, Pi(k) Electric energy supplied to the ith electric vehicle charging node for the microgrid at time k, diFor the electric automobile i in the time period [0, N-1]The electric energy required to be charged in the device,
Figure FDA0002546878040000013
and iP(k) the upper limit and the lower limit of the load of the charging node of the ith electric automobile are respectively set.
3. The microgrid energy management system of claim 2, wherein the microgrid energy management system is based on a two-stage optimal charging strategy, and is characterized in that: the method for describing the microgrid load curve problem as a finite time domain optimization problem comprises the following steps: within the time windows 1 to N-1, for m electric vehicle charging nodes, in order to make the curve of the electric vehicle charging schedule plan P and the sum of other loads Q the flattest, the following finite time domain optimization problem will be described:
Figure FDA0002546878040000021
wherein XkAll the electric energy, Q, provided to m electric vehicle charging nodes for the microgrid at time kkFor the other loads at time k, the load,
Figure FDA0002546878040000022
for the upper limit of the load of the charging node of the ith electric automobile, gamma is an ideal balanced power curve.
4. The microgrid energy management system of claim 2, wherein the microgrid energy management system is based on a two-stage optimal charging strategy, and is characterized in that: the method for describing the economic dispatch of the microgrid as an optimization problem comprises the following steps:
Figure FDA0002546878040000023
wherein P isi(k) Electric energy supplied to the ith electric vehicle charging node for the microgrid at time k, diFor the electric automobile i in the time period [0, N-1]The electric energy required to be charged in the device,
Figure FDA0002546878040000024
and iP(k) the upper limit and the lower limit of the load of the charging node of the ith electric automobile are respectively set.
5. The microgrid energy management system of claim 2, wherein the microgrid energy management system is based on a two-stage optimal charging strategy, and is characterized in that: the solving process of the two-stage optimal charging strategy algorithm is as follows:
5.1 initializing the charge and discharge power of each electric vehicle charging node and the iteration speed of an optimization algorithm;
5.2 load each node load using V2G technology;
5.3, solving the optimal load curve with high priority;
5.4 obtaining the output of each unit and the total loss of the network under the condition of only considering the total number of the electric automobiles participating in the interaction;
5.5, determining whether the sum of the active power loss of each node is continuously reduced, if so, entering a step 5.6, and if not, entering a step 5.8;
5.6, carrying out low-priority economic dispatching operation, and determining the optimal power of the electric automobile with each node participating in V2G interaction in different periods;
5.7 updating the charge and discharge power and returning to the step 5.2;
and 5.8, judging whether the dispatching plan meets the optimal load curve condition, if so, outputting a result, and if not, returning to the step 5.7.
6. The microgrid energy management system of claim 5, wherein the microgrid energy management system is based on a two-stage optimal charging strategy, and is characterized in that: the solving cycle termination condition of the two-stage optimal charging strategy algorithm is as follows: the sum of the active power loss is not reduced continuously, namely the algorithm is converged, and the final dispatching plan meets the requirement of an optimal load curve.
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