CN103761586A - Microgrid cluster concentrated-distributed type coordinated optimization scheduling method - Google Patents

Abstract

The invention discloses a microgrid cluster concentrated-distributed type coordinated optimization scheduling method. The method includes the steps that a microgrid cluster two-layer optimization scheduling model is built, and the microgrid cluster global goal and local goals of microgrids are all taken into consideration, wherein a microgrid cluster scheduling center is arranged on the upper layer, and a microgrid scheduling center is arranged on the lower layer; the cluster scheduling center on the upper layer calculates generating capacity of all micro power sources according to optimization of the global goal and sends the generating capacity to the microgrid scheduling center on the lower layer, wherein the generating capacity serves as decision variables; the microgrid scheduling center corrects the generating capacity of the micro power sources according to the local goals of the microgrids; the corrected generating capacity is sent to the cluster scheduling center on the upper layer, and correction and iteration are repeated until a coordination solution with which both the upper layer and the lower layer are satisfied is achieved. According to the microgrid cluster coordinated optimization scheduling method, inconsistency of the goal of the upper layer and the goals of the lower layer is taken into consideration, and through iterative optimization and interacted correction of the cluster scheduling center and the microgrid scheduling center, the global goal and the local goals can be coordinated. The method adapts to the distributed characteristic of a microgrid cluster, and coordinated operation between the microgrids can be achieved.

Description

Micro-electrical network group " concentrates-distributed " coordination optimization dispatching method

Technical field

The present invention relates to electric power Optimum Scheduling Technology field, relate in particular to a kind of micro-electrical network group coordination optimization dispatching method.

Background technology

In world wide, the Energy situation of constant tension has promoted application and the development of clean energy resource.Distributed generation technology based on regenerative resource has become realizes the diversified research emphasis of the energy.Micro-electrical network is connected to become a small grids by the relatively concentrated distributed power source in geographic position, for large-scale distributed power supply, controls a kind of effective ways are provided.

Along with distributed power source accesses on a large scale, for giving full play to distributed power source benefit, effectively solve the relevant issues that its randomness, undulatory property are brought, micro-electrical network group's concept has been proposed in recent years on the basis of micro-electrical network.Micro-electrical network group, containing the micro-electrical network of a plurality of sons, contains again the different resources such as load, distributed power source and energy storage of type in the micro-electrical network of every height.Each micro-electrical network in micro-electrical network group can be done as a whole participation system operation according to regional characteristics and self-demand, can effectively bring into play its inner distributed power source advantage and improve customer power supply reliability.But micro-electrical network group includes a large amount of distributed power sources, distributed power source is of a great variety and exert oneself unstablely, and power scheduling is had higher requirement and challenged.

Micro-electrical network group need set up overall operation target according to electrical network demand, realizes the coordinated operation between a plurality of micro-electrical networks; And each micro-electrical network in micro-electrical network group has self target, its inner different types of distributed power source has different operation characteristics, and the energy storage devices such as accumulator in micro-electrical network both can be used as power supply, also can be used as load; These diversity and dirigibility, greatly increased the difficulty that micro-electrical network self is dispatched.How to take into account micro-electrical network group's overall goal and each micro-electrical network localized target interests, how when micro-electrical network group moves, these distributed power sources and accumulator are carried out to rational management and running, in the different periods, can meet workload demand and obtain optimal operational efficiency of the economy guaranteeing, being the key issue of the micro-electric power network technique of research, is also the practical problems that micro-electrical network large-scale promotion application faces.

Compare with conventional electric power scheduling, micro-electrical network group scheduling has the features such as power supply diversity, scheduling multi-stratification, and conventional electric power scheduling structure is not also suitable for micro-electrical network group scheduling.Existing micro-electrical network group scheduling method is not yet carried out to systematic research, also do not propose corresponding Optimal Operation Model and algorithm.The present invention is directed to micro-electrical network troop in-distributed nature, a kind of coordination optimization scheduling model and algorithm are proposed.

Summary of the invention

In order to solve the problems of the technologies described above, the invention provides a kind of micro-electrical network group coordination optimization dispatching method.

Wherein, for micro-each decision maker's of electrical network group levels feature, set up micro-electrical network group bilevel optimization scheduling model, wherein

Upper strata is micro-electrical network group dispatching center, according to electrical network demand, sets up overall regulation goal;

Lower floor is micro-grid dispatching center, according to each micro-electrical network own characteristic, sets up respective objects;

Each decision maker of levels adopts levels iterative strategy to realize the coordinated operation between a plurality of micro-electrical networks by decision variable separately.

Wherein, micro-electrical network group dispatching center, upper strata sets up overall regulation goal, this layer considers environment and economy benefit, take cost of electricity-generating minimum and disposal of pollutants minimum sets up micro-electrical network group Multiobjective Optimal Operation model as target, and constraint condition comprises overall electric energy balance constraint, each micro-power supply generated output constraint and distribution power flow constraint.

Wherein, the micro-grid dispatching center of lower floor, according to each micro-electrical network own characteristic, is set up corresponding objective function, and wherein the micro-electrical network of cost type be take cost of electricity-generating minimum as target; The micro-electrical network of environment-friendly type be take disposal of pollutants minimum as target; It is target with large interconnecting ties power minimum that the micro-electrical network of Autonomous Model be take micro-electrical network, and the constraint condition that described micro-grid dispatching center is considered comprises micro-electrical network internal electric energy Constraints of Equilibrium and each micro-power supply generated output constraint.

Wherein, adopt levels interactive iteration to solve bi-level optimization model, described micro-electrical network group dispatching center is upper strata, and it take all micro-power supply generated outputs in micro-electrical network group is decision variable; Described micro-grid dispatching center is lower floor, its take its inner each micro-power supply and and large electrical network between interconnection power be decision variable, the initial solution of decision variable is obtained in upper strata first, input lower floor; Each decision maker of lower floor be take initial solution as initial reference value, is optimized and solves, and revises initial solution, obtains revising decision-making solution, returns to upper strata; Revised decision-making solution be take as initial reference value in upper strata, is optimized and solves, the decision-making solution that obtains again revising, input lower floor; Upper and lower two-layerly so iterate, until meet each layer of stopping criterion for iteration, finally obtains each decision maker's balanced satisfactory solution, realizes the coordinated operation between a plurality of micro-electrical networks.

Beneficial effect of the present invention is for micro-electrical network group's characteristic distributions and hierarchy optimization theoretical, micro-electrical network group's bilevel optimization scheduling model has been proposed, break through the power scheduling pattern of traditional single level, attempted many decision maker's dispatching algorithm, can realize the coordinated operation between a plurality of micro-electrical networks.In addition, this optimization method also can effectively improve distributed power source utilization factor and customer power supply reliability, improves micro-electrical network and external electrical network performance driving economy, has important practical significance.

Accompanying drawing explanation

The micro-electrical network group of Fig. 1 bilevel optimization scheduling flow figure.

Embodiment

With reference to the accompanying drawings the present invention is described in further detail.

During micro-electrical network group coordination optimization scheduling involved in the present invention is trooped for micro-electrical network-and distributed nature, set up micro-electrical network group bilevel optimization scheduling model, upper strata is micro-electrical network group dispatching center, according to electrical network demand, sets up overall regulation goal; Lower floor is micro-grid dispatching center, according to each micro-electrical network own characteristic, sets up respective objects.Upper and lower two-layer decision maker is influenced each other, is interacted by decision variable separately, adopts levels iterative strategy to realize the coordinated operation between a plurality of micro-electrical networks, comprising:

Micro-electrical network group dispatching center, upper strata sets up overall regulation goal according to electrical network demand, in the present invention, consider environment and economy benefit, the total power production cost minimum of take is integration objective with gross contamination discharge minimum, with micro-constraint conditions such as power-balance constraint, storage battery energy constraints, set up micro-electrical network group's Multiobjective Optimal Operation model.

Objective function:

minf(x)＝min{f ₁(x),f ₂(x)} (1)

$f_1\left(x\right)=\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\underset{r_{\mathrm{ij}}}{\overset{r_i}{\mathrm{\Σ}}}{C}_{r_{\mathrm{ij}}}{P}_{r_{\mathrm{ij}}}^t---\left(2\right)$

$f_2\left(x\right)=\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\underset{r_{\mathrm{ij}=1}}{\overset{r_i}{\mathrm{\Σ}}}{10}^{-2}({\mathrm{\α}}_{r_{\mathrm{ij}}}+{\mathrm{\β}}_{r_{\mathrm{ij}}}+P_{r_{\mathrm{ij}}}^t+{\mathrm{\γ}}_{r_{\mathrm{ij}}}{P}_{r_{\mathrm{ij}}}^{t^2})+{\mathrm{\ξ}}_{r_{\mathrm{ij}}}\exp \left({\mathrm{\λ}}_{r_{\mathrm{ij}}}{P}_{r_{\mathrm{ij}}}^t\right)---\left(3\right)$

Wherein, f ₁(x) be cost of electricity-generating, f ₂(x) be gaseous contamination discharge, the T.T. hop count of T for calculating, N is micro-electrical network number, t is the time period, r _ijbe j micro-power supply in i micro-electrical network, r _ibe micro-power supply sum in i micro-electrical network, for micro-power supply r _ijunit cost of electricity-generating,

be micro-power supply r _ijdusty gas emission factor,

for micro-power supply r _ijat t generated energy constantly.

(2) constraint condition:

Distribution power flow constraint:

$P_l=P_{\mathrm{Dl}}+U_l\underset{m=1}{\overset{M}{\mathrm{\Σ}}}{U}_m(G_{\mathrm{lm}}\cos {\mathrm{\δ}}_{\mathrm{lm}}+B_{\mathrm{lm}}\sin {\mathrm{\θ}}_{\mathrm{lm}})---\left(4\right)$

$Q_l=Q_{\mathrm{Dl}}+U_l\underset{m=1}{\overset{M}{\mathrm{\Σ}}}{U}_m(G_{\mathrm{lm}}\sin {\mathrm{\δ}}_{\mathrm{lm}}-B_{\mathrm{lm}}\cos {\mathrm{\θ}}_{\mathrm{lm}})---\left(5\right)$

Wherein, P _l, Q _lfor injecting meritorious, the reactive power at micro-electrical network group node l place; P _dl, Q _dlmeritorious, reactive power for node l place load; U _l, U _mvoltage for node l, m place; G _lm, B _lm, δ _lmfor the electricity between node l, m is led, susceptance and phase angle difference; θ _lmphase angle difference for admittance between line node l, m; The way that M is connected with node l.

The exert oneself upper and lower limit constraint of micro-power supply:

$P_{r_{\mathrm{ij}}}^{\min }\≤P_{r_{\mathrm{ij}}}^t\≤P_{r_{\mathrm{ij}}}^{\max }---\left(6\right)$

Wherein

for micro-power supply r _ijthe meritorious lower limit of exerting oneself, for micro-power supply r _ijthe meritorious upper limit of exerting oneself.

The constraint of accumulator cell charging and discharging power upper and lower limit:

$P_{i,\mathrm{BT}\_\mathrm{ch}}^{\min }\≤P_{i,\mathrm{BT}\_\mathrm{ch}}^t\≤P_{i,\mathrm{BT}\_\mathrm{ch}}^{\max }---\left(7\right)$

$P_{i,\mathrm{BT}\_\mathrm{disch}}^{\min }\≤P_{i,\mathrm{BT}\_\mathrm{disch}}^t\≤P_{i,\mathrm{BT}\_\mathrm{disch}}^{\max }---\left(8\right)$

SOC ⁱ _min≤SOC _i ^t≤SOC ⁱ _max (9)

Wherein,

for the charge and discharge constantly of the accumulator t in micro-electrical network i power; for the minimum charge and discharge power of the accumulator in micro-electrical network i;

for the maximum charge and discharge power of the accumulator in micro-electrical network i; SOC _i ^tfor the memory capacity constantly of the accumulator t in micro-electrical network i, SOC ⁱ _minfor the accumulators store capacity minimum value in micro-electrical network i, SOC ⁱ _maxfor the accumulators store maximum capacity in micro-electrical network i.

The micro-grid dispatching center of lower floor is set up respective objects according to each micro-electrical network own characteristic, and each micro-electrical network inside can be realized different targets according to the actual requirements.

(1) the micro-electrical network of cost type (1～J)

Objective function is that in micro-electrical network, total power production cost is minimum, and its expression formula is:

$\min C_F=\min \underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{r_{\mathrm{ij}}=1}{\overset{r_i}{\mathrm{\Σ}}}{C}_{r_{\mathrm{ij}}}{P}_{r_{\mathrm{ij}}}^t,i=\mathrm{1,2}...J---\left(10\right)$

Constraint condition has:

I sub micro-electrical network internal power Constraints of Equilibrium:

$\underset{r_{\mathrm{ij}}=1}{\overset{r_j}{\mathrm{\Σ}}}{P}_{r_{\mathrm{ij}}}^t+P_{i,\mathrm{BT}\_\mathrm{ch}}^t\·{\mathrm{\η}}_{i,\mathrm{BT}}^{\mathrm{ch}}-P_{i,\mathrm{ex}}^t=P_{i,d}^t---\left(11\right)$

$\underset{r_{\mathrm{ij}}=1}{\overset{r_j}{\mathrm{\Σ}}}{P}_j^t+P_{i,\mathrm{BT}\_\mathrm{disch}}^t\·{\mathrm{\η}}_{i,\mathrm{BT}}^{\mathrm{disch}}-P_{i,\mathrm{ex}}^t=P_{i,d}^t---\left(12\right)$

Micro-power supply units limits in i sub micro-electrical network:

$P_{r_{\mathrm{ij}}}^{\min }\≤P_{r_{\mathrm{ij}}}^t{P}_{r_{\mathrm{ij}}}^{\max },r_{\mathrm{ij}}=\mathrm{1,2},...r_i---\left(13\right)$

I sub micro-electrical network retrains with the mutual power transit line bound of large electrical network:

$P_{i,\mathrm{ex}}^{\min }\≤{P_{i,\mathrm{ex}}}^t\≤P_{i,\mathrm{ex}}^{\max }---\left(14\right)$

Accumulator cell charging and discharging constraint in i sub micro-electrical network:

$P_{i,\mathrm{BT}\_\mathrm{ch}}^{\min }\≤P_{i,\mathrm{BT}\_\mathrm{ch}}^t\≤P_{i,\mathrm{BT}\_\mathrm{ch}}^{\max }---\left(15\right)$

$P_{i,\mathrm{BT}\_\mathrm{disch}}^{\min }\≤P_{i,\mathrm{BT}\_\mathrm{disch}}^t\≤P_{i,\mathrm{BT}\_\mathrm{disch}}^{\max }---\left(16\right)$

SOC ⁱ _min≤SOC _i ^t≤SOC ⁱ _max (17)

Wherein,

for the mutual power between micro-electrical network i of the t moment and large electrical network,

for the charge and discharge constantly of the accumulator t in micro-electrical network i power,

for the charge and discharge constantly of the accumulator t in micro-electrical network i efficiency, all the other each symbolic significances all as previously mentioned.

(2) the micro-electrical network of environment-friendly type (1～K)

Objective function is that in micro-electrical network, gross contamination discharge is minimum, and its expression formula is:

$\min E=\min \underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\underset{r_{\mathrm{ij}=1}}{\overset{r_i}{\mathrm{\Σ}}}{10}^{-2}({\mathrm{\α}}_{r_{\mathrm{ij}}}+{\mathrm{\β}}_{r_{\mathrm{ij}}}{P}_{r_{\mathrm{ij}}}^t+{\mathrm{\γ}}_{r_{\mathrm{ij}}}{P}_{r_{\mathrm{ij}}}^{t^2})+{\mathrm{\ξ}}_{r_{\mathrm{ij}}}\exp \left({\mathrm{\λ}}_{r_{\mathrm{ij}}}{P}_{r_{\mathrm{ij}}}^t\right)i=\mathrm{1,2},...K---\left(18\right)$

Constraint condition has:

I sub micro-electrical network internal power Constraints of Equilibrium;

Micro-power supply units limits in i sub micro-electrical network;

I sub micro-electrical network retrains with the mutual power transit line bound of large electrical network;

Accumulator cell charging and discharging constraint in i sub micro-electrical network.

Wherein, above various in expression in each symbolic significance and constraint condition all as previously mentioned.

(3) the micro-electrical network of Autonomous Model (1～L)

Objective function is that the interconnection power minimum between micro-electrical network and large electrical network is target, and its expression formula is:

$\min \mathrm{\Δ}{P}_{\mathrm{ex}}=\min (\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{r_{\mathrm{ij}}=1}{\overset{r_i}{\mathrm{\Σ}}}{P}_{r_{\mathrm{ij}}}^t-\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{P}_{i,d}^t),i=\mathrm{1,2}...L---\left(19\right)$

Wherein, in objective function each symbolic significance all as previously mentioned, and for micro-power supply r _ijat t+1 generated output constantly,

for micro-power supply r _ijat t generated energy constantly.

Constraint condition has:

1) i sub micro-electrical network internal power Constraints of Equilibrium:

2) micro-power supply units limits in i sub micro-electrical network:

3) accumulator cell charging and discharging constraint in i sub micro-electrical network:

Wherein, the expression in above constraint condition all as previously mentioned.

For the Optimized model of above-mentioned foundation, each layer of decision maker, according to own characteristic, sets up corresponding decision variable.All micro-power supply generated outputs in cluster, accumulator be take in upper strata (micro-electrical network group layer), and to fill (sending out) electric weight be decision variable; It is decision variable that the interconnection power that the inner micro-power supply generated output of each micro-electrical network self, accumulator fill between (sending out) electric weight, micro-electrical network and large electrical network be take in lower floor's (micro-electrical network layer).

Adopt levels interactive iteration to solve bilevel optimization scheduling model.Micro-electrical network group dispatching center (upper strata) first obtains initial solution according to global object and the constraint condition considered, inputs each micro-grid dispatching center (lower floor); Each micro-grid dispatching center is usingd upper strata input as initial value, and the local localized target of foundation and the constraint condition of considering are obtained to revise and separated, and return to colony dispatching center (upper strata); The optimization solution that meets global object using the correction solution of returning as initial value, is obtained again again in colony dispatching center, input lower floor micro-grid dispatching center; Each micro-grid dispatching center, again as initial value, is asked to revise and is separated, and returns to upper strata; Upper and lower two-layerly so iterate, until meet each layer of stopping criterion for iteration, takes into account overall situation and partial situation's target, realizes the coordinated operation between a plurality of micro-electrical networks.

In brief, step of the present invention is roughly as follows.

1) according to micro-electrical network group bilevel optimization scheduling flow figure as shown in Figure 1, micro-electrical network group dispatching center, upper strata according to electrical network demand take total power production cost minimum with gross contamination discharge minimum be target, set up upper strata Multiobjective Optimal Operation model.

2) the micro-grid dispatching center of lower floor is according to each micro-electrical network own characteristic, and set up and maximize objective function and the constraint condition that meets self needss, formation lower floor Optimal Operation Model, wherein the micro-electrical network of cost type be take cost minimization as target; The micro-electrical network of environment-friendly type be take disposal of pollutants minimum as target; It is target with large interconnecting ties exchange power minimum that the micro-electrical network of Autonomous Model be take micro-electrical network.

3), according to model feature, set up levels decision variable.All micro-power supply generated outputs in cluster, accumulator be take in upper strata (micro-electrical network group layer), and to fill (sending out) electric weight be decision variable; It is decision variable that the interconnection power that the inner micro-power supply generated output of each micro-electrical network self, accumulator fill between (sending out) electric weight, micro-electrical network and large electrical network be take in lower floor's (micro-electrical network layer).

4) micro-electrical network group dispatching center (upper strata) first obtains the initial solution of decision variable, inputs micro-grid dispatching center (lower floor);

5) each micro-grid dispatching center (lower floor) be take initial solution as initial reference value, is optimized and solves, and revises initial solution, obtains revising decision-making solution, returns to upper strata (micro-electrical network group dispatching center);

6) revised decision-making solution be take as initial reference value in upper strata, is optimized and solves, the decision-making solution that obtains again revising, input lower floor;

7) upper and lower two-layerly so iterate, until meet each layer of stopping criterion for iteration; If do not meet and return to 6) continue iteration, otherwise finishing iteration calculating obtains final decision value.

Although illustrated and described embodiments of the invention, those having ordinary skill in the art will appreciate that: in the situation that not departing from principle of the present invention and aim, can carry out multiple variation, modification, replacement and modification to these embodiment, scope of the present invention is limited by claim and equivalent thereof.

Claims (4)

1. a micro-electrical network group coordination optimization dispatching method, it is characterized in that: for micro-each decision maker's of electrical network group levels feature, set up micro-electrical network group bilevel optimization scheduling model, wherein: upper strata is micro-electrical network group dispatching center, according to electrical network demand, sets up overall regulation goal;

Lower floor is micro-grid dispatching center, according to each micro-electrical network own characteristic, sets up respective objects;

Each decision maker of levels adopts levels iterative strategy to realize the coordinated operation between a plurality of micro-electrical networks by decision variable separately.

2. micro-electrical network group coordination optimization dispatching method as claimed in claim 1, it is characterized in that: micro-electrical network group dispatching center, upper strata sets up overall regulation goal, this layer considers environment and economy benefit, take cost of electricity-generating minimum and disposal of pollutants minimum sets up micro-electrical network group Multiobjective Optimal Operation model as target, and constraint condition comprises overall electric energy balance constraint, each micro-power supply generated output constraint and distribution power flow constraint.

3. micro-electrical network group coordination optimization dispatching method as claimed in claim 1, is characterized in that: the micro-grid dispatching center of lower floor, according to each micro-electrical network own characteristic, is set up corresponding objective function, and wherein the micro-electrical network of cost type be take cost of electricity-generating minimum as target; The micro-electrical network of environment-friendly type be take disposal of pollutants minimum as target; It is target with large interconnecting ties power minimum that the micro-electrical network of Autonomous Model be take micro-electrical network, and the constraint condition that described micro-grid dispatching center is considered comprises micro-electrical network internal electric energy Constraints of Equilibrium and each micro-power supply generated output constraint.

4. micro-electrical network group coordination optimization dispatching method as claimed in claim 1, it is characterized in that: adopt levels interactive iteration to solve bi-level optimization model, described micro-electrical network group dispatching center is upper strata, and it take all micro-power supply generated outputs in micro-electrical network group is decision variable; Described micro-grid dispatching center is lower floor, its take its inner each micro-power supply and and large electrical network between interconnection power be decision variable, the initial solution of decision variable is obtained in upper strata first, input lower floor; Each decision maker of lower floor be take initial solution as initial reference value, is optimized and solves, and revises initial solution, obtains revising decision-making solution, returns to upper strata; Revised decision-making solution be take as initial reference value in upper strata, is optimized and solves, the decision-making solution that obtains again revising, input lower floor; Upper and lower two-layerly so iterate, until meet each layer of stopping criterion for iteration, finally obtains each decision maker's balanced satisfactory solution, realizes the coordinated operation between a plurality of micro-electrical networks.

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