CN111884214B - Hierarchical optimization scheduling method and device suitable for park energy router cluster - Google Patents

Abstract

The invention discloses a hierarchical optimization scheduling method and a hierarchical optimization scheduling device suitable for a park energy router cluster, wherein the method comprises the steps of time period division and basic data acquisition; establishing an upper optimization model of the power distribution network energy router cluster; solving an upper-layer optimization problem of the power distribution network energy router cluster; establishing a lower-layer energy router local optimization model by taking the upper-layer optimization result as constraint; and solving the local optimization problem of each energy router, and scheduling the energy routers. The invention decomposes the unified optimization problem of the energy router cluster and various distributed energy sources in the park power distribution network into a double-layer optimization problem, effectively reduces the complexity of optimization solution, utilizes the schedulability of various energy sources to the maximum extent, and can effectively improve the economy and stability of the operation of the park power distribution network.

Description

Hierarchical optimization scheduling method and device suitable for park energy router cluster

Technical Field

The invention relates to the technical field of optimal scheduling of a park power distribution network, in particular to a hierarchical optimal scheduling method suitable for a park energy router cluster.

Background

A large amount of accesses of various distributed energy sources and novel loads in a park power distribution network bring a great deal of new challenges to operation and control of the park power distribution network. The energy router is used as novel equipment integrating a power electronic technology and an information communication technology, can provide an alternating current/direct current interface for various novel source loads, completes voltage and current conversion, collects various electric quantities in operation, actively performs power flow control, simultaneously has a certain communication function, and plays a key role in future power grid energy management and power control.

After the energy routers are connected to the power distribution network of the park, a plurality of distributed active/reactive power supplies are equivalently added in the power distribution network, the energy routers and the connected distributed energy sources are reasonably controlled, and the safety, the economy and the reliability of the operation of the power system can be improved. Compared with a single energy router, the controllable variables and data of the energy router cluster in the power distribution network of the park are multiplied, if the controllable variables and data are taken into consideration in a single optimization model, the complexity of the optimization problem is exponentially multiplied, and dimension disaster is caused.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides a hierarchical optimal scheduling method and a hierarchical optimal scheduling device suitable for a park energy router cluster, which are used for hierarchically decomposing an optimal scheduling target of a park power distribution network, simplifying the logic complexity of the optimal scheduling problem on the premise of confirming the optimal scheduling target, fully playing the local control function of an energy router and the schedulability of various distributed energy sources and improving the economical efficiency and the stability of the operation of the park power distribution network.

In order to achieve the purpose, the technical scheme adopted by the invention is as follows:

the invention provides a hierarchical optimization scheduling method suitable for a park energy router cluster, which comprises the following steps:

establishing an upper optimization model of the power distribution network energy router cluster; the upper layer optimization model aims at the maximum adjustable range of the active power of the energy router;

solving the upper layer optimization model;

taking the solution result of the upper-layer optimization model as constraint, and establishing a lower-layer energy router local optimization model; the local optimization model of the lower-layer energy router takes economic optimization as a target;

and solving the local optimization model of the lower-layer energy router, and scheduling the port power of the energy router based on the solved result.

Further, the method also comprises the following steps:

divide the whole day into equal timeNumber of segments, time-segments being N_T。

Further, the establishing of the power distribution network energy router cluster upper layer optimization model includes:

wherein F is the adjustable range of the active power of the energy router, P_ER,up(k, t) is the active power upper bound of the kth energy router at time t, P_ER,low(k, t) is the active power lower bound of the kth energy router at time t, k being 1,2, …, N_ER，t＝1,2,…,N_T，N_ERThe number of the energy routers in the power distribution network.

Further, solving the upper layer optimization model, wherein constraint conditions are required to be met:

energy router operating range constraints:

P_ER,min(k,t)≤P_ER,low(k,t)，P_ER,up(k,t)≤P_ER,max(k,t)；

wherein, P_ER,max(k, t) and P_ER,min(k, t) are the upper limit and the lower limit of the active power actually reached by the kth energy router at the moment t, P_ch,max(k)、P_dis,max(k) Maximum charging power and discharging power, P, of energy storage devices respectively connected to the kth energy router_{EV_A}(k) Average output power, P, of charging pile connected to kth energy router_PV(k, t) is photovoltaic power generation prediction data connected with the kth energy router at the moment t;

and (3) power distribution network voltage constraint:

and respectively substituting the upper boundary or the lower boundary of active power of all energy routers into the topology of the power distribution network in the park and load prediction data, wherein the calculated voltage values of the nodes of the whole network all meet the following requirements:

wherein, V_i(t) is the voltage, V, of node i at time t in the distribution network₀For the rated voltage of the distribution network, alpha is the ratio of the maximum allowable voltage deviation to the rated voltage, N_nodeThe number of nodes of the power distribution network.

Further, with the solution result of the upper layer optimization model as a constraint, establishing a lower layer energy router local optimization model, including:

wherein C is the total cost, P_Grid(k, t) is the active power transmitted to the kth energy router grid-connected point at the moment t of the power distribution network, C_Grid(t) is the time-of-use electricity price at time t, C_PVFor photovoltaic on-line electricity prices, N_TFor the number of divided periods throughout the day, Δ T is the duration of each period.

Further, solving the local optimization model of the lower-layer energy router needs to satisfy the constraint conditions:

and (4) energy storage device restraint:

-P_dis,max(k)≤P_B(k,t)≤P_ch,max(k)；

SOC_min≤SOC(k,t)≤SOC_max；

wherein, P_B(k, t) and SOC (k, t) are the active power and state of charge of the energy storage device connected to the kth energy router at time t, P_ch,max(k) And P_dis,max(k) Maximum charging power and discharging power, SOC, of energy storage device connected to kth energy router respectively_maxAnd SOC_minRespectively representing the upper limit and the lower limit of the state of charge of the energy storage device in normal operation;

photovoltaic grid connection constraint:

P_Grid(k,t)≥-P_PV(k,t)；

wherein, P_PV(k, t) isPhotovoltaic power generation prediction data connected with the kth energy router at the time t;

electric vehicle charging scheduling constraint:

P_EV(k,t)＝δ(k,t)P_{EV_A}；

δ(k,t)＝0 or 1；

wherein, P_EV(k, t) is the active power flowing to the charging pile by the kth energy router at the moment t, delta (k, t) is the switching value of the charging pile connected with the kth energy router at the moment t, 1 is on, 0 is off, and P is_{EV_A}For average output power of the charging pile, T_S,e，T_F,eAnd E_eDividing the charging behavior into the starting time, the ending time and the amount to be charged of the e-th charging behavior, wherein e is 1,2, …, and M is the number of the charging behaviors in the scheduling process;

active upper and lower boundary constraints:

P_ER,low(k,t)≤P_ER(k,t)≤P_ER,up(k,t)

wherein, P_ER(k, t) is the active power exchanged between the kth energy router and the distribution network at time t, P_ER,up(k, t) and P_ER,low(k, t) is an active power upper boundary and an active power lower boundary of the exchange between the kth energy router and the power distribution network at the moment t;

and power balance constraint:

P_ER(k,t)＝P_B(k,t)-P_PV(k,t)+P_EV(k,t)

P_Grid(k,t)＝P_Load(k,t)+P_ER(k,t)

Q_Grid(k,t)＝Q_Load(k,t)+Q_ER(k,t)

wherein Q is_ER(k, t) is the reactive power exchanged between the kth energy router and the distribution network at time t, Q_Grid(k, t) is the reactive power of the distribution network flowing to the kth energy router grid-connected point at the moment t, P_Load(k, t) and Q_Load(k, t) are respectively the first at time tThe normal load active power and reactive power of the grid-connected points of the k energy routers;

capacity constraint:

wherein S is_ER(k) The capacity of the grid-connected inverter in the kth energy router.

Further, in the above-mentioned case,

solving an upper-layer optimization model of the power distribution network energy router cluster and a local optimization model of the lower-layer energy router by adopting a genetic algorithm;

the solving result of the upper-layer optimization model of the power distribution network energy router cluster is as follows: active power upper boundary P of kth energy router at time t_ER,up(k, t) and a lower boundary P_ER,low(k, t) adding P_ER,up(k, t) and P_ER,low(k, t) sending to the corresponding energy router;

the solving result of the local optimization model of the lower-layer energy router is as follows: active power P transmitted to kth energy router grid-connected point at moment t of power distribution network_Grid(k, t) and active power P of the kth energy router flowing to the charging pile at the moment t_EV(k, t) and the active power P of the energy storage device to which the kth energy router is connected at time t_B(k, t); and according to the solving result, scheduling the active power transmitted to the grid-connected point of the energy router by the power distribution network, the power flowing to the charging pile by the energy router and the power of the energy storage device connected with the energy router.

The invention also provides a hierarchical optimization scheduling device suitable for the park energy router cluster, which comprises:

the upper layer scheduling module is used for establishing a power distribution network energy router cluster upper layer optimization model; the upper layer optimization model aims at the maximum adjustable range of the active power of the energy router;

the upper layer calculation module is used for solving the upper layer optimization model;

the lower layer scheduling module is used for establishing a lower layer energy router local optimization model by taking the solution result of the upper layer optimization model as constraint; the local optimization model of the lower-layer energy router takes economic optimization as a target;

and the number of the first and second groups,

and the lower layer calculation module is used for solving the lower layer energy router local optimization model and scheduling the port power of the energy router based on the solved result.

Further, the upper layer scheduling module is specifically configured to establish:

wherein F is the adjustable range of the active power of the energy router, P_ER,up(k, t) is the active power upper bound of the kth energy router at time t, P_ER,low(k, t) is the active power lower bound of the kth energy router at time t, k being 1,2, …, N_ER，t＝1,2,…,N_T，N_ERThe number of energy routers in the distribution network, N_TThe number of time segments divided for the whole day.

Further, the lower layer scheduling module is specifically configured to establish:

wherein C is the total cost, P_Grid(k, t) is the active power transmitted to the kth energy router grid-connected point at the moment t of the power distribution network, C_Grid(t) is the time-of-use electricity price at time t, C_PVFor photovoltaic on-line electricity prices, N_TFor the number of divided periods throughout the day, Δ T is the duration of each period.

The invention achieves the following beneficial effects:

the invention provides a hierarchical optimal scheduling method and a hierarchical optimal scheduling device suitable for a park energy router cluster, which are used for hierarchically decomposing an optimal scheduling target of a park power distribution network, simplifying the logic complexity of an optimal scheduling problem on the premise of ensuring the optimal target, fully playing the local control function of an energy router and the schedulability of various distributed energy sources and improving the economical efficiency and the stability of the operation of the park power distribution network.

Drawings

FIG. 1 is a flow chart of a hierarchical optimal scheduling method for a campus energy router cluster according to the present invention;

fig. 2 is a 33-node distribution network campus energy router cluster structure in the embodiment of the present invention;

FIG. 3 is a flow chart of a genetic algorithm in an embodiment of the present invention;

FIG. 4 shows the result of upper optimization of an energy router cluster in an embodiment of the present invention;

FIG. 5 illustrates an energy router-connected energy storage device optimization result in an embodiment of the present invention;

fig. 6 shows the result of link power optimization of an energy router according to an embodiment of the present invention;

fig. 7 shows the optimization result of the charging pile connected to a certain energy router in the embodiment of the present invention.

Detailed Description

The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.

Referring to fig. 1, the present invention provides a hierarchical optimal scheduling method for a campus energy router cluster, including:

step 1: dividing the whole day into time intervals with equal length, and acquiring conventional load prediction data connected with the distribution network nodes of the garden in each time interval of the next day and photovoltaic power generation prediction data connected with the energy router.

Step 2: and establishing an upper optimization model of the power distribution network energy router cluster.

And step 3: and solving an upper-layer optimization problem of the power distribution network energy router cluster.

And 4, step 4: and establishing a local optimization model of the lower-layer energy router by taking the solution result of the upper-layer optimization model as constraint.

And 5: and solving the local optimization model of each energy router, and scheduling the port power of the energy router based on the solving result.

Examples

With 33 nodes (N) as shown in FIG. 2_nodeFor example, 33) park radial distribution network, the invention is applied to access 5 energy routers (N)_ER5) carrying out hierarchical optimization scheduling on the cluster, wherein the hierarchical optimization scheduling comprises the following steps:

step 1: divide the whole day into equal-length N_TIn each time period, N in the power distribution network of the garden in each time period of the next day is acquired_nodeRegular load prediction data, N, for individual node connections_ERAnd the photovoltaic power generation prediction data connected with the energy routers serve as basic data for hierarchical optimization scheduling.

The whole day is divided into equal-length N_TThe time intervals are divided into average time intervals, and the duration of each time interval is delta T-24 h/N_TWhere h is an hour unit.

Step 2: and establishing an upper-layer optimization model of the power distribution network energy router cluster, wherein the upper-layer optimization model comprises decision variables, objective functions and constraint conditions.

The decision variable is N in the park distribution network_ERThe next day N of the energy router_TActive power upper boundary P exchanged with power distribution network in each time period_ER,up(k, t) and a lower boundary P_ER,low(k,t)，

Wherein k is 1,2, …, N_ER；t＝1,2,…,N_T，P_ER,up(k, t) is the active power upper bound of the kth energy router at time t, P_ER,low(k, t) is the active power lower boundary of the kth energy router at the time t, and the total is 2 XN_T×N_ERAnd (4) determining a variable.

The maximum adjustable range of the active power of the energy router is the target of the objective function, and the objective function is as follows:

the constraint conditions comprise energy router operation range constraint and distribution network voltage constraint:

the energy router operating range constraint is as follows:

P_ER,min(k,t)≤P_ER,low(k,t)，P_ER,up(k,t)≤P_ER,max(k,t)

wherein, P_ER,max(k, t) and P_ER,min(k, t) are respectively the upper limit and the lower limit of the active power which can be practically achieved by the kth energy router at the moment t, and the expression is as follows:

wherein, P_ch,max(k)、P_dis,max(k) Maximum charging power and discharging power, P, of energy storage devices respectively connected to the kth energy router_{EV_A}(k) For the charging pile average output power connected with the kth energy router, the above 3 variables are fixed values for the kth energy router; p_PV(k, t) is photovoltaic power generation prediction data of the kth energy router connection at the time t, and changes along with time.

The voltage constraint of the power distribution network is as follows: will N_T×N_ERThe active power value of the upper boundary or the lower boundary of the operation of each energy router is respectively substituted into the topology of the power distribution network in the park and the load prediction data, and the obtained N of the whole network is calculated_nodeThe voltage values of all the nodes meet the following conditions:

wherein V_i(t) the voltage of the node i in the power distribution network at the moment t is obtained by load flow calculation; v₀The voltage is the rated voltage of the distribution network, generally the initial end voltage of the distribution network; α is the ratio of the maximum permissible voltage deviation to the rated voltage (initial voltage of the distribution network), and can be 10% generally.

And step 3: solving the upper optimization problem of the power distribution network energy router cluster, comprising the following steps:

the genetic algorithm shown in fig. 3 is used to solve as follows:

randomizing an initial population, randomizing the upper and lower active power boundaries of each energy router in the invention, and digitally encoding the individual in the population;

calculating the individual fitness of the population, wherein the maximum adjustable range of the active power of the energy router is used as a fitness function;

and performing cross and variation operation on the individuals to obtain a new generation of population, calculating the individual fitness again until an iteration termination condition is reached, and outputting a final result.

In the invention, the iteration termination condition is as follows: the difference value of the optimal individual fitness in the two generations of populations is smaller than a certain preset minimum value or reaches a preset iteration number.

The final solution result is P_ER,up(k, t) and P_ER,low(k, t), the solution result is sent to the corresponding energy router, and the solution result is shown in fig. 4. Fig. 4 is a schematic diagram of the adjustable range of the active power of the energy routers on the nodes 11, 14, 24, 26, and 32 of the campus at different times of the day, where the abscissa represents the different times of the day and the ordinate represents the active output range of the energy routers on the nodes.

And 4, step 4: establishing a local optimization model of a lower-layer energy router, comprising the following steps: based on the solving result of the optimization problem at the upper layer of the power distribution network energy router cluster, N_ERRespectively constructing N by each energy router_EREach local optimization model comprises decision variables, an objective function and constraint conditions.

The decision variable is the power of each port of the energy router k at the time t (k is 1,2, …, N)_ER；t＝1,2,…,N_T)。

The objective function takes economic optimization as a target, and comprises the following steps:

wherein C is the total cost, P_Grid(k, t) is the active power transmitted from the distribution network at the time t to the grid-connected point of the kth energy router, C_Grid(t) is the time-of-use electricity price at time t, C_PVFor photovoltaic power on-lineAnd (4) price.

The constraint conditions comprise energy storage device constraint, photovoltaic grid-connected constraint, electric vehicle charging scheduling constraint, active upper and lower boundary constraint, power balance constraint and capacity constraint.

The energy storage device constraints are:

-P_dis,max(k)≤P_B(k,t)≤P_ch,max(k)

SOC_min≤SOC(k,t)≤SOC_max

wherein, P_B(k, t) and SOC (k, t) are the active power and state of charge, P, of the energy storage device connected to the kth energy router at time t_ch,max(k) And P_dis,max(k) Maximum charging power and discharging power, SOC, of energy storage device connected to kth energy router respectively_maxAnd SOC_minRespectively, the upper limit and the lower limit of the state of charge of the energy storage device in normal operation.

The photovoltaic grid connection constraint is as follows:

P_Grid(k,t)≥-P_PV(k,t)

wherein, P_Grid(k, t) is the active power transmitted from the power distribution network at the time t to the grid-connected point of the kth energy router, P_PV(k, t) is photovoltaic power generation prediction data of the kth energy router connection at the time t.

The charging scheduling constraint of the electric automobile is as follows:

P_EV(k,t)＝δ(k,t)P_{EV_A}

δ(k,t)＝0 or 1

wherein, P_EV(k, t) is the active power flowing to the charging pile by the kth energy router at the moment t, delta (k, t) is the switching value of the charging pile connected with the kth energy router at the moment t, 1 is on, 0 is off, and P is_{EV_A}For the average output power of the charging pile, M charging behaviors, T, are assumed to exist in the scheduling process_S,e，T_F,eAnd E_eDivided into e (e is 1,2, …, M) th chargingA start time, an end time, and a to-be-charged amount of the action.

The active upper and lower boundary constraints are:

P_ER,low(k,t)≤P_ER(k,t)≤P_ER,up(k,t)

wherein, P_ER(k, t) is the active power exchanged between the kth energy router and the distribution network at time t, P_ER,up(k, t) and P_ER,lowAnd (k, t) is an active power upper boundary and an active power lower boundary exchanged between the kth energy router and the power distribution network at the moment t, and the active power upper boundary and the active power lower boundary are obtained and issued by an upper-layer optimization model.

The power balance constraint is:

P_ER(k,t)＝P_B(k,t)-P_PV(k,t)+P_EV(k,t)

P_Grid(k,t)＝P_Load(k,t)+P_ER(k,t)

Q_Grid(k,t)＝Q_Load(k,t)+Q_ER(k,t)

wherein, P_ER(k, t) and Q_ER(k, t) are respectively the active power and the reactive power exchanged between the kth energy router and the power distribution network at the moment t, P_Grid(k, t) and Q_Grid(k, t) are respectively the active power and the reactive power of the distribution network flowing to the kth energy router grid-connected point at the moment t, P_Load(k, t) and Q_LoadAnd (k, t) respectively representing the active power and the reactive power of the conventional load of the kth energy router grid-connected point at the moment t.

The capacity constraint is:

wherein S is_ER(k) The capacity of the grid-connected inverter in the kth energy router.

And 5: each energy router solves a local optimization problem; the optimal day-ahead scheduling plan for the power of each port of the output energy router comprises the following steps: p_B(k,t)，P_Grid(k, t) and P_EV(k,t)。

The local optimization problem can be solved by adopting a genetic algorithm, and 5 scheduling strategies are set under the same condition for verifying the economy of the proposed optimized scheduling strategy:

(1) strategy 1: the system is free of an ER and an energy storage device, the photovoltaic power is locally consumed, the residual power is on line, and an EV (charging pile) is charged in an unordered mode;

(2) strategy 2: the energy storage device only carries out peak clipping and valley filling (the energy storage device is charged with constant power in a valley price period and outputs power to supply a load in a peak price period), the photovoltaic local energy is consumed, the residual electricity is on line, and the EV disordered charging is carried out;

(3) strategy 3: the structure and the optimization strategy provided by the invention have no voltage constraint of the power distribution network;

(4) strategy 4: according to the structure and the optimization strategy provided by the invention, the voltage constraint alpha of the power distribution network is 10%;

(5) strategy 5: according to the structure and the optimization strategy provided by the invention, the voltage constraint alpha of the power distribution network is 5%;

the local optimization results of a certain energy router are shown in fig. 5, 6 and 7. In the figure, the strategies 1,2,3,4 and 5 represent the above 5 strategies respectively, fig. 5 is an optimal day-ahead scheduling plan of the energy storage device connected with the energy router, and the solid line and the dotted line in the figure represent the charge and discharge power and the charge state of the energy storage device under different strategies respectively; FIG. 6 is an optimal day-ahead scheduling plan for the energy router by the distribution network; fig. 7 is an optimal day-ahead dispatch plan for a charging pile connected to an energy router.

The invention also provides a hierarchical optimization scheduling device suitable for the park energy router cluster, which comprises:

the upper layer scheduling module is used for establishing a power distribution network energy router cluster upper layer optimization model; the upper layer optimization model aims at the maximum adjustable range of the active power of the energy router;

the upper layer calculation module is used for solving the upper layer optimization model;

the lower layer scheduling module is used for establishing a lower layer energy router local optimization model by taking the solution result of the upper layer optimization model as constraint; the local optimization model of the lower-layer energy router takes economic optimization as a target;

and the number of the first and second groups,

and the lower layer calculation module is used for solving the lower layer energy router local optimization model and scheduling the port power of the energy router based on the solved result.

Further, the upper layer scheduling module is specifically configured to establish:

wherein F is the adjustable range of the active power of the energy router, P_ER,up(k, t) is the active power upper bound of the kth energy router at time t, P_ER,low(k, t) is the active power lower bound of the kth energy router at time t, k being 1,2, …, N_ER，t＝1,2,…,N_T，N_ERThe number of energy routers in the distribution network, N_TThe number of time segments divided for the whole day.

Further, the lower layer scheduling module is specifically configured to establish:

wherein C is the total cost, P_Grid(k, t) is the active power transmitted to the kth energy router grid-connected point at the moment t of the power distribution network, C_Grid(t) is the time-of-use electricity price at time t, C_PVFor photovoltaic on-line electricity prices, N_TFor the number of divided periods throughout the day, Δ T is the duration of each period.

It is to be noted that the apparatus embodiment corresponds to the method embodiment, and the implementation manners of the method embodiment are all applicable to the apparatus embodiment and can achieve the same or similar technical effects, so that the details are not described herein.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (5)

1. A hierarchical optimization scheduling method suitable for a campus energy router cluster is characterized by comprising the following steps:

establishing an upper optimization model of the power distribution network energy router cluster; the upper layer optimization model aims at the maximum adjustable range of the active power of the energy router:

wherein F is the adjustable range of the active power of the energy router, P_ER,up(k, t) is the active power upper bound of the kth energy router at time t, P_ER,low(k, t) is the active power lower bound of the kth energy router at time t, k being 1,2, …, N_ER，t＝1,2,…,N_T，N_TNumber of time segments divided for the whole day, N_ERThe number of energy routers in the power distribution network is;

solving the upper layer optimization model;

taking the solution result of the upper-layer optimization model as constraint, and establishing a lower-layer energy router local optimization model; the local optimization model of the lower-layer energy router takes economic optimization as a target:

wherein C is the total cost, P_Grid(k, t) is the active power transmitted to the kth energy router grid-connected point at the moment t of the power distribution network, C_Grid(t) is the time-of-use electricity price at time t, C_PVFor photovoltaic grid-access electricity prices, Δ T is the duration of each time period;

the constraint conditions need to be satisfied:

and (4) energy storage device restraint:

-P_dis,max(k)≤P_B(k,t)≤P_ch,max(k)；

SOC_min≤SOC(k,t)≤SOC_max；

wherein, P_B(k, t) and SOC (k, t) are the active power and state of charge of the energy storage device connected to the kth energy router at time t, P_ch,max(k) And P_dis,max(k) Maximum charging power and discharging power, SOC, of energy storage device connected to kth energy router respectively_maxAnd SOC_minRespectively representing the upper limit and the lower limit of the state of charge of the energy storage device in normal operation;

photovoltaic grid connection constraint:

P_Grid(k,t)≥-P_PV(k,t)；

wherein, P_PV(k, t) is photovoltaic power generation prediction data connected with the kth energy router at the moment t;

electric vehicle charging scheduling constraint:

P_EV(k,t)＝δ(k,t)P_{EV_A}；

δ(k,t)＝0or1；

wherein, P_EV(k, t) is the active power flowing to the charging pile by the kth energy router at the moment t, delta (k, t) is the switching value of the charging pile connected with the kth energy router at the moment t, 1 is on, 0 is off, and P is_{EV_A}For average output power of the charging pile, T_S,e，T_F,eAnd E_eDividing the charging behavior into the starting time, the ending time and the amount to be charged of the e-th charging behavior, wherein e is 1,2, …, and M is the number of the charging behaviors in the scheduling process;

active upper and lower boundary constraints:

P_ER,low(k,t)≤P_ER(k,t)≤P_ER,up(k,t)；

wherein, P_ER(k, t) is the active power exchanged between the kth energy router and the distribution network at time t, P_ER,up(k,t)And P_ER,low(k, t) is an active power upper boundary and an active power lower boundary of the exchange between the kth energy router and the power distribution network at the moment t;

and power balance constraint:

P_ER(k,t)＝P_B(k,t)-P_PV(k,t)+P_EV(k,t)；

P_Grid(k,t)＝P_Load(k,t)+P_ER(k,t)；

Q_Grid(k,t)＝Q_Load(k,t)+Q_ER(k,t)；

wherein Q is_ER(k, t) is the reactive power exchanged between the kth energy router and the distribution network at time t, Q_Grid(k, t) is the reactive power of the distribution network flowing to the kth energy router grid-connected point at the moment t, P_Load(k, t) and Q_Load(k, t) respectively representing the active power and the reactive power of the conventional load of the kth energy router grid-connected point at the moment t;

capacity constraint:

wherein S is_ER(k) The capacity of a grid-connected inverter in the kth energy router is obtained;

and solving the local optimization model of the lower-layer energy router, and scheduling the port power of the energy router based on the solved result.

2. The method of claim 1, further comprising:

dividing the whole day into equal time intervals with the number of the time intervals being N_T。

3. The hierarchical optimization scheduling method for the campus energy router cluster according to claim 1, wherein the upper layer optimization model is solved to satisfy constraint conditions:

energy router operating range constraints:

P_ER,min(k,t)≤P_ER,low(k,t)，P_ER,up(k,t)≤P_ER,max(k,t)；

wherein, P_ER,max(k, t) and P_ER,min(k, t) are the upper limit and the lower limit of the active power actually reached by the kth energy router at the moment t, P_ch,max(k)、P_dis,max(k) Maximum charging power and discharging power, P, of energy storage devices respectively connected to the kth energy router_{EV_A}(k) Average output power, P, of charging pile connected to kth energy router_PV(k, t) is photovoltaic power generation prediction data connected with the kth energy router at the moment t;

and (3) power distribution network voltage constraint:

and respectively substituting the upper boundary or the lower boundary of active power of all energy routers into the topology of the power distribution network in the park and load prediction data, wherein the calculated voltage values of the nodes of the whole network all meet the following requirements:

wherein, V_i(t) is the voltage, V, of node i at time t in the distribution network₀For the rated voltage of the distribution network, alpha is the ratio of the maximum allowable voltage deviation to the rated voltage, N_nodeThe number of nodes of the power distribution network.

4. The hierarchical optimization scheduling method for campus energy router cluster as claimed in claim 1,

solving an upper-layer optimization model of the power distribution network energy router cluster and a local optimization model of the lower-layer energy router by adopting a genetic algorithm;

the solving result of the upper-layer optimization model of the power distribution network energy router cluster is as follows: time tActive power upper bound P of kth energy router_ER,up(k, t) and a lower boundary P_ER,low(k, t) adding P_ER,up(k, t) and P_ER,low(k, t) sending to the corresponding energy router;

the solving result of the local optimization model of the lower-layer energy router is as follows: active power P transmitted to kth energy router grid-connected point at moment t of power distribution network_Grid(k, t) and active power P of the kth energy router flowing to the charging pile at the moment t_EV(k, t) and the active power P of the energy storage device to which the kth energy router is connected at time t_B(k, t); and according to the solving result, scheduling the active power transmitted to the grid-connected point of the energy router by the power distribution network, the power flowing to the charging pile by the energy router and the power of the energy storage device connected with the energy router.

5. A hierarchical optimization scheduling apparatus for a campus energy router cluster, comprising:

the upper layer scheduling module is used for establishing a power distribution network energy router cluster upper layer optimization model; the upper layer optimization model aims at the maximum adjustable range of the active power of the energy router, and specifically comprises the following steps:

wherein F is the adjustable range of the active power of the energy router, P_ER,up(k, t) is the active power upper bound of the kth energy router at time t, P_ER,low(k, t) is the active power lower bound of the kth energy router at time t, k being 1,2, …, N_ER，t＝1,2,…,N_T，N_ERThe number of energy routers in the distribution network, N_TThe number of time segments divided for the whole day;

the upper layer calculation module is used for solving the upper layer optimization model;

the lower layer scheduling module is used for establishing a lower layer energy router local optimization model by taking the solution result of the upper layer optimization model as constraint; the local optimization model of the lower-layer energy router takes economic optimization as a target, and specifically comprises the following steps:

wherein C is the total cost, P_Grid(k, t) is the active power transmitted to the kth energy router grid-connected point at the moment t of the power distribution network, C_Grid(t) is the time-of-use electricity price at time t, C_PVFor photovoltaic on-line electricity prices, N_TThe number of time periods divided for the whole day, Δ T being the duration of each time period;

and the number of the first and second groups,

and the lower layer calculation module is used for solving the lower layer energy router local optimization model and scheduling the port power of the energy router based on the solved result.

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