CN107947175A

CN107947175A - A kind of micro-capacitance sensor economic load dispatching method and system based on Web control

Info

Publication number: CN107947175A
Application number: CN201711460098.9A
Authority: CN
Inventors: 夏海波; 李强; 王建国; 徐瑞林; 陈涛; 陈民铀; 李哲; 李俊杰
Original assignee: Chongqing University; Electric Power Research Institute of State Grid Chongqing Electric Power Co Ltd; State Grid Corp of China SGCC
Current assignee: Chongqing University; Electric Power Research Institute of State Grid Chongqing Electric Power Co Ltd; State Grid Corp of China SGCC
Priority date: 2017-12-28
Filing date: 2017-12-28
Publication date: 2018-04-20
Anticipated expiration: 2037-12-28
Also published as: CN107947175B

Abstract

The invention discloses a kind of micro-capacitance sensor economic load dispatching method and system based on Web control, it includes S1：According to economic load dispatching model, the theoretical optimal output formula of controllable distributed power generation DG is derived under constraints；S2：Communication network is constructed according to micro-capacitance sensor bilayer Controlling model；Micro-capacitance sensor bilayer Controlling model includes：Lower floor's micro-capacitance sensor and upper layer communication network；S3：Distributed AC servo system rule is obtained according to upper layer communication network design weight matrix, and according to weight matrix；S4：Setting value is calculated according to distributed AC servo system rule, according to the output of the setting value adjustment controllable distributed power generation DG calculated, realizes the economic optimization scheduling of micro-capacitance sensor.The beneficial effect that the present invention obtains is：Tiny increment by realizing all controllable DG is equal, can be achieved with the total cost of electricity-generating of micro-capacitance sensor and minimizes；Weight matrix is designed, facility is provided for algorithm design；In an iterative process, it can guarantee that system power balances.

Description

A kind of micro-capacitance sensor economic load dispatching method and system based on Web control

Technical field

The present invention relates to micro-capacitance sensor distributed control technology field, particularly a kind of micro- electricity based on Web control Net economic load dispatching method and system.

Background technology

In recent years, quickly grown by the distributed power generation (Distributed Generation, DG) of regenerative resource, And effective mode of management of the micro-capacitance sensor as distributed power generation, for improving renewable energy utilization rate, reduce environmental pollution, delay Energy crisis is solved, and lifting power supply reliability and stability are all of great significance.But renewable energy power generation depends on External environment, its output has obvious randomness, and workload demand has fluctuation, meanwhile, the inertia of micro-capacitance sensor in itself compared with It is small, and there is two-way flow etc., the interaction of these factors in trend so that the economic load dispatching of micro-capacitance sensor becomes to be stranded very much It is difficult.

At present, average homogeneity algorithm widely uses in the economic load dispatching of micro-capacitance sensor, and still, there are following two to ask Topic：1) in an iterative process, average homogeneity algorithm can destroy system balancing；2) still exist to have in distributed system and concentrate The node of sexual function, can not realize complete distribution.

The content of the invention

In view of the drawbacks described above of the prior art, it is an object of the invention to provide a kind of based on Web control Micro-capacitance sensor economic load dispatching method and system, the tiny increment by realizing all controllable DG is equal, can be achieved with the total hair of micro-capacitance sensor Electric cost minimization；And during algorithm iteration, total energy ensures system balancing.

An object of the present invention is that technical solution in this way is realized, a kind of based on the micro- of Web control Rational dispatching by power grids method, it includes：

S1：According to economic load dispatching model, the theoretical optimal output of controllable distributed power generation DG is derived under constraints Formula；

S2：Communication network is constructed according to micro-capacitance sensor bilayer Controlling model；The micro-capacitance sensor bilayer Controlling model includes：Lower floor Micro-capacitance sensor and upper layer communication network；

S3：Distributed AC servo system is obtained according to the upper layer communication network design weight matrix, and according to the weight matrix Rule；

S4：Setting value is calculated according to distributed AC servo system rule, controllable distributed power generation is adjusted according to the setting value calculated The output of DG, realizes the economic optimization scheduling of micro-capacitance sensor.

Further, under constraints, the theoretical optimal output of controllable distributed power generation DG is specially the step S1：

S11：It is each controllable if it is DG number of controllable distributed power generation to have m controllable distributed power generation DG, m in micro-capacitance sensor DG_iCost of electricity-generating function be C_i(p_i), Economic Dispatch Problem is expressed as all controllable DG_iThe sum of cost of electricity-generating minimum, i.e.,：

Equality constraint：

Inequality constraints condition：

Wherein, p_iIt is the output of generator i；WithThe minimum and maximum output of respectively generator i；P_loadFor Total loading demand, meetsC_i(p_i) be generator i cost of electricity-generating function；

S12：Cost function C_i(p_i) quadratic function can be expressed as,

Wherein a_i, b_iAnd c_iIt is the cost parameter of generator i；

S13：For simplified expression, it is assumed thatWithSo, cost function Again it is expressed as：

S14：To C_iA partial derivative is sought, show that incremental cost expression formula is：

S15：According to equal incremental rate criterion, as the incremental cost λ of all controllable distributed power generation DG_iWhen equal, always at this time Cost of electricity-generating it is minimum, i.e., the output of controllable distributed power generation DG at this time is theoretical optimal output.

Further, if constraints is the constraints not comprising inequality in the step S1, have：

S16：Optimal incremental cost is λ_i ^*, then the theoretical optimal output of controllable distributed power generation DG is：

Further, if constraints is the constraints comprising inequality in the step S1, have：

S17：Controllable distributed power generation DG for being unsatisfactory for inequality condition, this distributed power generation DG is set to by output valve Maximum output or minimum output；Controllable distributed power generation DG for meeting inequality condition, its theoretical optimal incremental cost For：

Wherein, Ω_PFor all set for being unsatisfactory for inequality condition distributed power generation DG；

Therefore, under the conditions of comprising inequality constraints, the theoretical optimal output of controllable distributed power generation DG is：

Further, micro-capacitance sensor bilayer Controlling model is typical micro-capacitance sensor linear quadratic control model in the step S2, i.e., under Layer micro-capacitance sensor includes：Wind turbine, photovoltaic, miniature gas turbine, these distributed power generations of energy storage DG.

Further, the rule of distributed AC servo system described in the step S3 includes weight matrix, according in the weight matrix Element, the output to the controllable distributed power generation DG are adjusted.

Further, the step S3 is specially：

S31：Communication network G (V, E) is made of n Agent, and n is Agent number, is established in communication network G (V, E) After, define an adjacency matrix A=[a_ij]_n×nConnection relation between Agent is described, if Agenti to Agentj has one Lian Bian, then matrix element a_ij=1, otherwise a_ij=0；Matrix A^TWhat is represented is the transposition of adjacency matrix A；The matrix in directed networks A and A^TIt is unsymmetrical matrix；

S32：Define an attribute matrix B=[b_ii]_n×nRepresent the type of Agent, B is diagonal matrix, its diagonal element b_iiFor 0 or 1, the type depending on Agent；If Agenti is controllable Agent, then b_ii=1, otherwise b_ii=0；

S33：Define a degree matrix D=[d_ii]_n×nRepresenting that Agent's goes out side number, i.e. out-degree, D matrix is diagonal matrix, The diagonal entry d of matrix D_iiRelation between the element of matrix A is：

S34：Define a weight matrix W=[w_ij]_n×nRepresent the relation between all Agent；If Agent i are arrived Agent j have even side, then it is w to connect the weights on side_ij=1/d_ii, wherein d_iiIt is that Agent i go out side number；In addition, for from ring On weight w_ii=1；Every a line of W matrixes and be 1；

S35：The power-balance of micro-grid system is defined as：In two adjacent moments, the summation of all load variation amounts, etc. In the summation of controllable distributed power generation output power variable quantity, plus the total of uncontrollable distributed power generation DG output power variable quantities Be shown below：

Wherein, P (t)=[p_i(t)]_n×1With Q (t)=[q_i(t)]_n×1What is represented respectively is that i-th of distributed power generation DG exists The active and reactive power output of t moment, P (t-1)=[p_i(t-1)]_n×1With Q (t-1)=[q_i(t-1)]_n×1Represent respectively It is that active and reactive powers of i-th of distributed power generation DG at the t-1 moment exports；L^p(t)=[l^p _i(t)]_n×1And L^p(t-1)= [l^p _i(t-1)]_n×1What is represented respectively is i-th of active and reactive power demand for being supported on t moment, L^q(t)=[l^q _i (t)]_n×1And L^q(t-1)=[l^q _i(t-1)]_n×1What is represented respectively is i-th of active and reactive power for being supported on the t-1 moment Demand；I is the unit matrix of n × n rank；

S36：According to communication network G (V, E), the control law for providing distributed power generation DG is：

Wherein ()^TComputing represents to carry out transposition computing to matrix；Matrix is W weight matrixs；

S37：According to communication network G (V, E), the control law for providing controllable distributed power generation DG is：

Wherein,Representing the active output of controllable distributed power generation DG, α represents cost parameter vector, (·)^TComputing represents to carry out transposition computing to matrix；Matrix H is weight matrix, is calculated as follows：

Wherein, h_ijIt is the off-diagonal element in weight matrix H, h_iiIt is the diagonal element in weight matrix H.

Further, in the step S4, economic optimization scheduling process is as follows：

Economic load dispatching under the conditions of comprising inequality, calculates setting value, when setting value is big according to distributed AC servo system rule When distributed power generation DG maximum capacities, design, which is crossed the border, handles rule, and following modification is made to above derived control law：

Further, distributed AC servo system rule is fully distributed control law in the step S3, i.e., each Agent only needs to receive Collect the information of neighbours Agent.

An object of the present invention is that technical solution in this way is realized, a kind of based on the micro- of Web control Rational dispatching by power grids system, it includes：

The output unit of controllable distributed power generation DG, controlled distribution is derived according to economic load dispatching model under constraints The theoretical optimal output formula of formula power generation DG；

Micro-capacitance sensor dual layer elements, communication network is constructed according to micro-capacitance sensor bilayer Controlling model；The double-deck control of the micro-capacitance sensor Model includes：Lower floor's micro-capacitance sensor and upper layer communication network；

Weight matrix unit, according to the exploited in communication weight matrix, and according to obtaining the weight matrix Distributed AC servo system is restrained；

The economic optimization scheduling unit of micro-capacitance sensor, calculates setting value according to distributed AC servo system rule, is set according to what is calculated Definite value adjustment controllable distributed power generation DG outputs, realize the economic optimization scheduling of micro-capacitance sensor.

By adopting the above-described technical solution, the present invention has the advantage that：

(1) by realizing that the tiny increment of all controllable DG is equal, it can be achieved with the total cost of electricity-generating of micro-capacitance sensor and minimize；

(2) the unified design procedure of micro-capacitance sensor economic load dispatching method is provided, according to upper layer communication network design weight matrix, Facility is provided to design the computational methods of economic load dispatching；

(3) if Agent controls the output of controllable DG according to control law, then, controllable DG can be realized by setting mesh Mark output, also, ensure during algorithm iteration, system power is always to maintain balance.

Other advantages, target and the feature of the present invention will be illustrated in the following description to a certain extent, and And to a certain extent, based on will be apparent to those skilled in the art to investigating hereafter, Huo Zheke To be instructed from the practice of the present invention.The target and other advantages of the present invention can be wanted by following specification and right Book is sought to realize and obtain.

Brief description of the drawings

The brief description of the drawings of the present invention is as follows：

Fig. 1 is the flow diagram of the micro-capacitance sensor economic load dispatching method based on Web control.

Fig. 2 is that micro-capacitance sensor bilayer Controlling model is illustrated in the micro-capacitance sensor economic load dispatching method based on Web control Figure.

Fig. 3 is the warp for not including inequality constraints condition in the micro-capacitance sensor economic load dispatching method based on Web control The active power of controllable DG and the output schematic diagram of reactive power of Ji Optimized Operation.

Fig. 4 is the warp for not including inequality constraints condition in the micro-capacitance sensor economic load dispatching system based on Web control The system frequency and line voltage schematic diagram of Ji Optimized Operation.

Fig. 5 is the warp for not including inequality constraints condition in the micro-capacitance sensor economic load dispatching method based on Web control The output result of half controllable DG power of Ji Optimized Operation and controllable DG incremental costs schematic diagram.

Fig. 6 is the economy for including inequality constraints condition in the micro-capacitance sensor economic load dispatching method based on Web control The active power of controllable DG after Optimized Operation and the output schematic diagram of reactive power.

Fig. 7 is the economy for including inequality constraints condition in the micro-capacitance sensor economic load dispatching system based on Web control System frequency and line voltage schematic diagram after Optimized Operation.

Fig. 8 is the economy for including inequality constraints condition in the micro-capacitance sensor economic load dispatching method based on Web control The output result of half controllable DG power after Optimized Operation and controllable DG incremental costs schematic diagram.

Embodiment

The invention will be further described with reference to the accompanying drawings and examples.

Embodiment：As shown in Figures 1 to 8；A kind of micro-capacitance sensor economic load dispatching method based on Web control, it is wrapped Include：

More specifically, step 1) has in micro-capacitance sensor Economic Dispatch Problem：

S11：If there is m controllable DG (m is controllable DG number) in micro-capacitance sensor, each controllable DG_iCost of electricity-generating function be F_i (p_i), Economic Dispatch Problem is expressed as all controllable DG_iThe sum of cost of electricity-generating minimum, i.e.,：

Equality constraint：

Inequality constraints condition：

S12：According to document, cost function C_i(p_i) quadratic function can be expressed as,

Wherein a_i, b_iAnd c_iIt is the cost parameter of generator i；

S13：For simplified expression, it is assumed thatWithSo, cost function weight Newly it is expressed as：

S15：According to equal incremental rate criterion, as the incremental cost λ of all controllable distributed power generation DG_iWhen equal, total hair Electric cost minimization.

S16：It is assumed that under the conditions of not comprising inequality, theoretical optimal incremental cost is λ_i ^*, then the theory of controllable DG at this time Optimal output is：

S17：It is assumed that under the conditions of comprising inequality constraints, the controllable DG for being unsatisfactory for inequality condition, by output valve It is set to the maximum output of this DG or minimum output；Controllable DG for meeting inequality condition, its theoretical optimal incremental cost are：

Wherein Ω_PFor all set for being unsatisfactory for inequality condition DG.

Therefore, under the conditions of comprising inequality constraints, the theoretical optimal output of controllable DG is：

Micro-capacitance sensor bilayer Controlling model described in step 2), its lower floor is micro-capacitance sensor and upper strata is communication network.Lower floor is micro- Power grid is made of polytype DG such as wind turbine, photovoltaic, miniature gas turbine, energy storage.

Since wind turbine, photovoltaic etc. use the DG of regenerative resource, its output depends on external environmental condition, has uncertain Property, therefore, it is defined as uncontrollable DG.And the DG such as miniature gas turbine, its output can be adjusted according to control command, because This, is defined as controllable DG.

In isolated island micro-capacitance sensor, an energy-storage system of accumulator being operated under V/F control models generally is used as whole The electric voltage frequency support of a system, and it is defined as half controllable DG.To make full use of renewable energy power generation, wind turbine, photovoltaic etc. Uncontrollable DG uses PQ control models using the controllable DG such as MPPT maximum power point tracking pattern, miniature gas turbine.

In upper layer communication network, the Agent that is connected with the controllable DG of uncontrollable DG and half is referred to as uncontrollable The controllable Agent of Agent and half, and it is known as controllable Agent with the controllable DG Agent being connected.

Agent collects connected DG and the information loaded, as shown in Fig. 2, wherein uncontrollable by the line between two layers The controllable Agent ellipse representations of Agent and half, and controllable Agent is represented with diamond shape, even the direction on side represents the side of information transmission To.

In communication network G (V, E), the controllable Agent of uncontrollable Agent or half only believe the DG oneself collected and load Breath is transferred to controllable Agent, and controllable Agent not only can be with receive information, but also can transmit information.Therefore, communication network G (V, E in), the controllable Agent of uncontrollable Agent or half only go out when not entering, and controllable Agent it is existing go out while have into while. Meanwhile by collecting the DG and load information that oneself are connected from ring.It follows that communication network G (V, E) is one oriented Figure.

In addition, in isolated island micro-capacitance sensor, the energy storage (DG4) of V/F control models is operated in, V/F controls are ensuring that output The magnetic flux of motor can be made to keep certain the voltage control directly proportional with frequency, avoid the production of weak magnetic and magnetic saturation phenomenon It is raw, it is chiefly used in wind turbine, pump class energy-saving type frequency conversion device is realized with voltage controlled oscillator.In micro-capacitance sensor voltage frequency departure normal value, So as to ensure that system power balances, and electric voltage frequency can be kept to stablize immediately to system injection or absorbed power.

But if energy storage exports for a long time, its state-of-charge (SOC) may be too low or excessive, so as to influence next time Regulation and control to system.Therefore, using V/F control energy storage and corresponding half controllable Agent between add a parameter γ=- 1, the output of energy storage is thought of as loading.In this way, after can be achieved with energy storage transient compensation system vacancy, it is exported by controllable DG Share so that the output of energy storage is gradually restored to zero.

Step 3) designs weight matrix, and according to weight matrix, provide distributed control after the foundation of upper layer communication network Make the building method of rule.If Agent controls the output of controllable DG according to control law, then, controllable DG can be realized by setting Set the goal output, also, ensures that system power is always to maintain balance in an iterative process.

S31：Communication network G (V, E) forms (n is Agent number) by n Agent, is established in communication network G (V, E) After, define an adjacency matrix A=[a_ij]_n×nConnection relation between Agent is described, if Agenti to Agentj has one Lian Bian, then matrix element a_ij=1, otherwise a_ij=0；Matrix A^TWhat is represented is the transposition of adjacency matrix A；The matrix in directed networks A and A^TIt is unsymmetrical matrix；

S35：The power-balance of micro-grid system is defined as：In two adjacent moments, the summation of all load variation amounts, etc. In the summation of controllable distributed power generation (Distributed Generation, DG) output power variable quantity, plus uncontrollable DG The summation of output power variable quantity, is shown below：

Wherein, P (t)=[p_i(t)]_n×1With Q (t)=[q_i(t)]_n×1What is represented respectively is that i-th of distributed power generation DG exists The active and reactive power output of t moment, P (t-1)=[p_i(t-1)]_n×1With Q (t-1)=[q_i(t-1)]_n×1Represent respectively It is that active and reactive powers of i-th of distributed power generation DG at the t-1 moment exports；L^p(t)=[l^p _i(t)]_n×1And L^q(t)= [l^q _i(t)]_n×1What is represented respectively is i-th of active and reactive power demand for being supported on t moment, L^p(t-1)=[l^p _i(t- 1)]_n×1And L^q(t-1)=[l^q _i(t-1)]_n×1What is represented respectively is that i-th of active and reactive power for being supported on the t-1 moment needs Ask；I is the unit matrix of n × n rank.

S36：According to communication network G (V, E), the control law for providing DG is：

Wherein, ()^TComputing represents to carry out transposition computing to matrix；Matrix W is weight matrix.

S37：According to communication network G (V, E), the control law for providing controllable DG is：

Wherein, ()^TComputing represents to carry out transposition computing to matrix；Matrix H is weight matrix, is calculated as follows Arrive：

Economic load dispatching of the step 4) under the conditions of comprising inequality, following modification is made to above derived control law (13)：

It can be achieved with the economic load dispatching of the micro-capacitance sensor under the conditions of comprising inequality.

More specifically, in FIG. 2, it is assumed that the DG and load parameter of lower floor's micro-capacitance sensor are set as shown in table 1：

1 distributed generation resource of table (DG) and the parameter setting of load

Power supply	Capacity	Control mode	Load	Load greatest requirements
					DG₁	50kW, 40kVar	PQ	Load₁	20kW, 0kVar
DG₂	30kW, 0kVar	MPPT	Load₂	35kW, 0kVar
					DG₃	60kW, 25kVar	PQ	Load₃	10kW, 20kVar
DG₄	30Ah	V/F	Load₄	30kW, 0kVar
					DG₅	55kW, 20kVar	PQ	Load₅	20kW, 20kVar
DG₆	65kW, 30kVar	PQ	Load₆	10kW, 10kVar
					DG₇	50kW, 0kVar	MPPT	Load₇	20kW, 0kVar
DG₈	35kW, 0kVar	MPPT	Load₈	30kW, 15kVar
					DG₉	45kW, 38kVar	PQ	Load₉	40kW, 10kVar
DG₁₀	45kW, 0kVar	MPPT	Load₁₀	20kW, 15kVar
					DG₁₁	70kW, 28kVar	PQ	Load₁₁	15kW, 20kVar
DG₁₂	50kW, 0kVar	MPPT	Load₁₂	20kW, 0kVar

For cost of electricity-generating minimum problem, usually, only consider the active power of controllable DG, and without considering photovoltaic, The cost of electricity-generating of wind turbine and reactive power.Under the conditions of not comprising inequality, cost function C_i(p_i) inside cost coefficient a_i, b_iAnd c_iAs shown in table 2：

2 controlled distribution formula power supply cost parameter of table is set

Power supply	a_i	b_i	c _i
				DG1	0.059	6.71	80
DG3	0.047	7.08	56
				DG5	0.066	6.29	43
DG6	0.031	7.53	35
				DG9	0.069	4.57	48
DG11	0.038	5.86	91

The active power of controllable DG is controlled to export using formula (13), its simulation result is as shown in Figures 3 to 5.Fig. 3 The active power and reactive power output that middle #1 is controllable DG, #2 and #3 in Fig. 4 are system frequency and line voltage respectively, Fig. 5 In #4 and #5 be half controllable DG power outputs result and controllable DG incremental costs respectively.

Under the conditions of comprising inequality, cost function C_i(p_i) inside cost coefficient a_i, b_iAnd c_iAs shown in table 3：

3 controlled distribution formula power supply cost parameter of table is set

Power supply	a_i	b_i	c _i
				DG1	0.059	6.71	80
DG3	0.047	7.08	56
				DG5	0.066	6.29	43
DG6	0.031	7.53	35
				DG9	0.05	4.57	48
DG11	0.038	5.86	91

Control the active power of controllable DG to export using formula (16), that is, carry out economic optimization scheduling, its simulation result As shown in Figure 6 to 8.#1 is that the active power of controllable DG and reactive power export after economic optimization is dispatched in Fig. 6, #2 in Fig. 7 It is system frequency and line voltage after economic optimization is dispatched respectively with #3；In Fig. 8 #4 and #5 be respectively economic optimization scheduling it is later half can Control DG power outputs result and controllable DG incremental costs.

According to simulation result it can be found that designing the weight matrix H in distributed AC servo system rule, it is possible to realize that micro-capacitance sensor is sent out Electric cost minimization.

The present invention's also provides a kind of micro-capacitance sensor economic load dispatching system based on Web control, it includes：

Micro-capacitance sensor dual layer elements, the construction rule of communication network is determined according to micro-capacitance sensor bilayer Controlling model；Micro- electricity The double-deck Controlling model of net includes：Lower floor's micro-capacitance sensor and upper layer communication network；

Weight matrix unit, after the foundation of upper layer communication network, designs weight matrix, and according to the weight matrix, give Go out the building method of distributed AC servo system rule, obtain the distributed AC servo system rule；

The economic optimization scheduling unit of micro-capacitance sensor, calculates setting value according to distributed AC servo system rule, divides when setting value is more than During cloth power generation DG maximum capacities, the DG outputs of adjustment controllable distributed power generation, realize the economic optimization scheduling of micro-capacitance sensor.

Finally illustrate, the above embodiments are merely illustrative of the technical solutions of the present invention and it is unrestricted, although with reference to compared with The present invention is described in detail in good embodiment, it will be understood by those of ordinary skill in the art that, can be to the skill of the present invention Art scheme technical scheme is modified or replaced equivalently, without departing from the objective and scope of the technical program, it should all cover in the present invention Right among.

Claims

A kind of 1. micro-capacitance sensor economic load dispatching method based on Web control, it is characterised in that the described method includes following Step：

S1：According to economic load dispatching model, the theoretical optimal output formula of controllable distributed power generation DG is derived under constraints；

S2：Communication network is constructed according to micro-capacitance sensor bilayer Controlling model；The micro-capacitance sensor bilayer Controlling model includes：The micro- electricity of lower floor Net and upper layer communication network；

S3：Distributed AC servo system rule is obtained according to the upper layer communication network design weight matrix, and according to the weight matrix；

S4：Setting value is calculated according to distributed AC servo system rule, according to the setting value adjustment controllable distributed power generation DG's calculated Output, realizes the economic optimization scheduling of micro-capacitance sensor.
2. the micro-capacitance sensor economic load dispatching method based on Web control as claimed in claim 1, it is characterised in that described Under constraints, the optimal output of controllable distributed power generation DG is specially step S1：

S11：If it is DG number of controllable distributed power generation to have m controllable distributed power generation DG, m in micro-capacitance sensor, each controllable DG_i's Cost of electricity-generating function is C_i(p_i), Economic Dispatch Problem is expressed as all controllable DG_iThe sum of cost of electricity-generating minimum, i.e.,：

<mrow> <mi>min</mi> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <msub> <mi>C</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>;</mo> </mrow>

Equality constraint：

<mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>=</mo> <msub> <mi>P</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> </mrow> </msub> <mo>;</mo> </mrow>

Inequality constraints condition：

<mrow> <msubsup> <mi>p</mi> <mi>i</mi> <mi>min</mi> </msubsup> <mo>&le;</mo> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>&le;</mo> <msubsup> <mi>p</mi> <mi>i</mi> <mi>max</mi> </msubsup> <mo>;</mo> </mrow>

Wherein, p_iIt is the output of generator i；WithThe minimum and maximum output of respectively generator i；P_loadTo be total Loading demand, meetsC_i(p_i) be generator i cost of electricity-generating function；

S12：Cost function C_i(p_i) quadratic function can be expressed as,

<mrow> <msub> <mi>C</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>a</mi> <mi>i</mi> </msub> <msubsup> <mi>p</mi> <mi>i</mi> <mn>2</mn> </msubsup> <mo>+</mo> <msub> <mi>b</mi> <mi>i</mi> </msub> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>c</mi> <mi>i</mi> </msub> <mo>;</mo> </mrow>

Wherein a_i, b_iAnd c_iIt is the cost parameter of generator i；

S13：For simplified expression, it is assumed thatWithSo, cost function table again It is shown as：

<mrow> <msub> <mi>C</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>&alpha;</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mrow> <mn>2</mn> <msub> <mi>&beta;</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>+</mo> <msub> <mi>&gamma;</mi> <mi>i</mi> </msub> <mo>;</mo> </mrow>

S14：To C_iA partial derivative is sought, show that incremental cost expression formula is：

<mrow> <msub> <mi>&lambda;</mi> <mi>i</mi> </msub> <mo>=</mo> <mfrac> <mrow> <mo>&part;</mo> <msub> <mi>C</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <mo>&part;</mo> <msub> <mi>p</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>=</mo> <mfrac> <mrow> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>&alpha;</mi> <mi>i</mi> </msub> </mrow> <msub> <mi>&beta;</mi> <mi>i</mi> </msub> </mfrac> <mo>;</mo> </mrow>

S15：According to equal incremental rate criterion, as the incremental cost λ of all controllable distributed power generation DG_iWhen equal, power generation total at this time Cost minimization, i.e., the output of controllable distributed power generation DG at this time is theoretical optimal output.
3. the micro-capacitance sensor economic load dispatching method based on Web control as claimed in claim 2, it is characterised in that if institute It is the constraints not comprising inequality to state constraints in step S1, then has：

S16：Optimal incremental cost is λ_i ^*, then the theoretical optimal output of controllable distributed power generation DG is：

<mrow> <msubsup> <mi>p</mi> <mi>i</mi> <mo>*</mo> </msubsup> <mo>=</mo> <msub> <mi>&beta;</mi> <mi>i</mi> </msub> <msup> <mi>&lambda;</mi> <mo>*</mo> </msup> <mo>-</mo> <msub> <mi>&alpha;</mi> <mi>i</mi> </msub> <mo>.</mo> </mrow>
4. the micro-capacitance sensor economic load dispatching method based on Web control as claimed in claim 2, it is characterised in that if institute It is the constraints comprising inequality to state constraints in step S1, then has：

S17：Controllable distributed power generation DG for being unsatisfactory for inequality condition, this distributed power generation DG is set to most by output valve Big output or minimum output；Controllable distributed power generation DG for meeting inequality condition, its theoretical optimal incremental cost are：

<mrow> <msup> <mi>&lambda;</mi> <mo>*</mo> </msup> <mo>=</mo> <mfrac> <mrow> <msub> <mi>P</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>a</mi> <mi>d</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>&Element;</mo> <msub> <mi>&Omega;</mi> <mi>P</mi> </msub> </mrow> </msub> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>+</mo> <msub> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>&NotElement;</mo> <msub> <mi>&Omega;</mi> <mi>P</mi> </msub> </mrow> </msub> <msub> <mi>&alpha;</mi> <mi>i</mi> </msub> </mrow> <mrow> <msub> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>&NotElement;</mo> <msub> <mi>&Omega;</mi> <mi>P</mi> </msub> </mrow> </msub> <msub> <mi>&beta;</mi> <mi>i</mi> </msub> </mrow> </mfrac> <mo>,</mo> <mrow> <mo>(</mo> <mi>i</mi> <mo>&NotElement;</mo> <msub> <mi>&Omega;</mi> <mi>P</mi> </msub> <mo>)</mo> </mrow> <mo>;</mo> </mrow>

Wherein, Ω_PFor all set for being unsatisfactory for inequality condition distributed power generation DG；

Therefore, under the conditions of comprising inequality constraints, the theoretical optimal output of controllable distributed power generation DG is：

<mrow> <msubsup> <mi>p</mi> <mi>i</mi> <mo>*</mo> </msubsup> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>&beta;</mi> <mi>i</mi> </msub> <msup> <mi>&lambda;</mi> <mo>*</mo> </msup> <mo>-</mo> <msub> <mi>&alpha;</mi> <mi>i</mi> </msub> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>i</mi> <mo>&NotElement;</mo> <msub> <mi>&Omega;</mi> <mi>P</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mi>p</mi> <mi>i</mi> <mi>min</mi> </msubsup> <msubsup> <mi>orp</mi> <mi>i</mi> <mi>max</mi> </msubsup> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>i</mi> <mo>&Element;</mo> <msub> <mi>&Omega;</mi> <mi>P</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>.</mo> </mrow>
5. the micro-capacitance sensor economic load dispatching method based on Web control as claimed in claim 1, it is characterised in that described Micro-capacitance sensor bilayer Controlling model is typical micro-capacitance sensor linear quadratic control model in step S2, i.e. lower floor's micro-capacitance sensor includes：Wind turbine, Photovoltaic, miniature gas turbine, these distributed power generations of energy storage DG.
6. the micro-capacitance sensor economic load dispatching method based on Web control as claimed in claim 1, it is characterised in that described The rule of distributed AC servo system described in step S3 includes weight matrix, according to the element in the weight matrix, to the controlled distribution The output of formula power generation DG is adjusted.
7. the micro-capacitance sensor economic load dispatching method based on Web control as claimed in claim 4, it is characterised in that described Step S3 is specially：

S31：Communication network G (V, E) is made of n Agent, and n is Agent number, after communication network G (V, E) is established, Define an adjacency matrix A=[a_ij]_n×nConnection relation between Agent is described, if Agenti to Agentj has a company side, Then matrix element a_ij=1, otherwise a_ij=0；Matrix A^TWhat is represented is the transposition of adjacency matrix A；Matrix A and A in directed networks^T It is unsymmetrical matrix；

S32：Define an attribute matrix B=[b_ii]_n×nRepresent the type of Agent, B is diagonal matrix, its diagonal element b_iiFor 0 Or 1, the type depending on Agent；If Agenti is controllable Agent, then b_ii=1, otherwise b_ii=0；

S33：Define a degree matrix D=[d_ii]_n×nRepresent that Agent's goes out side number, i.e. out-degree, D matrix is diagonal matrix, matrix D Diagonal entry d_iiRelation between the element of matrix A is：

S34：Define a weight matrix W=[w_ij]_n×nRepresent the relation between all Agent；If Agent i to Agent j There is even side, then it is w to connect the weights on side_ij=1/d_ii, wherein d_iiIt is that Agent i go out side number；In addition, for from the power on ring Value w_ii=1；Every a line of W matrixes and be 1；

S35：The power-balance of micro-grid system is defined as：In two adjacent moments, the summation of all load variation amounts, equal to can The summation of distributed power generation output power variable quantity is controlled, plus the summation of uncontrollable distributed power generation DG output power variable quantities, It is shown below：

<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mi>&Sigma;</mi> <mo>&lsqb;</mo> <mi>B</mi> <mo>&CenterDot;</mo> <mi>P</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>&rsqb;</mo> <mo>=</mo> <mi>&Sigma;</mi> <msup> <mi>L</mi> <mi>p</mi> </msup> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>-</mo> <mi>&Sigma;</mi> <mo>&lsqb;</mo> <mo>(</mo> <mi>I</mi> <mo>-</mo> <mi>B</mi> <mo>)</mo> <mo>&CenterDot;</mo> <mi>P</mi> <mo>(</mo> <mi>t</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> <mo>&rsqb;</mo> </mtd> </mtr> <mtr> <mtd> <mi>&Sigma;</mi> <mo>&lsqb;</mo> <mi>B</mi> <mo>&CenterDot;</mo> <mi>Q</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>&rsqb;</mo> <mo>=</mo> <mi>&Sigma;</mi> <msup> <mi>L</mi> <mi>q</mi> </msup> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>-</mo> <mi>&Sigma;</mi> <mo>&lsqb;</mo> <mo>(</mo> <mi>I</mi> <mo>-</mo> <mi>B</mi> <mo>)</mo> <mo>&CenterDot;</mo> <mi>Q</mi> <mo>(</mo> <mi>t</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> <mo>&rsqb;</mo> </mtd> </mtr> </mtable> </mfenced> <mo>;</mo> </mrow>

Wherein, P (t)=[p_i(t)]_n×1With Q (t)=[q_i(t)]_n×1What is represented respectively is i-th of distributed power generation DG in t The active and reactive power output at quarter, P (t-1)=[p_i(t-1)]_n×1With Q (t-1)=[q_i(t-1)]_n×1What is represented respectively is i-th Active and reactive powers of a distributed power generation DG at the t-1 moment exports；L^p(t)=[l^p _i(t)]_n×1And L^q(t)=[l^q _i (t)]_n×1What is represented respectively is i-th of active and reactive power demand for being supported on t moment, L^p(t-1)=[l^p _i(t-1)]_n×1 And L^q(t-1)=[l^q _i(t-1)]_n×1What is represented respectively is i-th of active and reactive power demand for being supported on the t-1 moment；I is The unit matrix of one n × n rank；

S36：According to communication network G (V, E), the control law for providing distributed power generation DG is：

<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mi>B</mi> <mo>&CenterDot;</mo> <mi>P</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>=</mo> <mi>B</mi> <mo>&CenterDot;</mo> <mi>P</mi> <mo>(</mo> <mi>t</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> <mo>+</mo> <msup> <mi>W</mi> <mi>T</mi> </msup> <mo>&CenterDot;</mo> <mo>&lsqb;</mo> <msup> <mi>L</mi> <mi>p</mi> </msup> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>-</mo> <mi>P</mi> <mo>(</mo> <mi>t</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> <mo>&rsqb;</mo> </mtd> </mtr> <mtr> <mtd> <mi>B</mi> <mo>&CenterDot;</mo> <mi>Q</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>=</mo> <mi>B</mi> <mo>&CenterDot;</mo> <mi>Q</mi> <mo>(</mo> <mi>t</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> <mo>+</mo> <msup> <mi>W</mi> <mi>T</mi> </msup> <mo>&CenterDot;</mo> <mo>&lsqb;</mo> <msup> <mi>L</mi> <mi>q</mi> </msup> <mo>(</mo> <mi>t</mi> <mo>)</mo> <mo>-</mo> <mi>Q</mi> <mo>(</mo> <mi>t</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> <mo>&rsqb;</mo> </mtd> </mtr> </mtable> </mfenced> <mo>;</mo> </mrow>

Wherein ()^TComputing represents to carry out transposition computing to matrix；Matrix is W weight matrixs；

S37：According to communication network G (V, E), the control law for providing controllable distributed power generation DG is：

<mrow> <mover> <mi>P</mi> <mo>~</mo> </mover> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msup> <mi>H</mi> <mi>T</mi> </msup> <mo>&CenterDot;</mo> <mrow> <mo>(</mo> <mover> <mi>P</mi> <mo>~</mo> </mover> <mo>(</mo> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> <mo>+</mo> <mi>&alpha;</mi> <mo>)</mo> </mrow> <mo>-</mo> <mi>&alpha;</mi> <mo>;</mo> </mrow>

Wherein,Represent the active output of controllable distributed power generation DG, α represents cost parameter vector, ()^T Computing represents to carry out transposition computing to matrix；Matrix H is weight matrix, is calculated as follows：

<mrow> <msub> <mi>h</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mi>i</mi> </mrow> </msub> </mfrac> <mo>-</mo> <mfrac> <mn>1</mn> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mi>i</mi> </mrow> </msub> </mfrac> <mo>&CenterDot;</mo> <mfrac> <mrow> <msub> <mi>&beta;</mi> <mi>i</mi> </msub> <mo>/</mo> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mi>i</mi> </mrow> </msub> </mrow> <mrow> <msub> <mi>&beta;</mi> <mi>i</mi> </msub> <mo>/</mo> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mi>i</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>&beta;</mi> <mi>j</mi> </msub> <mo>/</mo> <msub> <mi>d</mi> <mrow> <mi>j</mi> <mi>j</mi> </mrow> </msub> </mrow> </mfrac> <mo>,</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>&Element;</mo> <msub> <mi>N</mi> <mi>i</mi> </msub> <mo>,</mo> <mi>j</mi> <mo>&NotEqual;</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>;</mo> </mrow>

<mrow> <msub> <mi>h</mi> <mrow> <mi>i</mi> <mi>i</mi> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mn>1</mn> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mi>i</mi> </mrow> </msub> </mfrac> <mo>&CenterDot;</mo> <mi>&Sigma;</mi> <mfrac> <mrow> <msub> <mi>&beta;</mi> <mi>j</mi> </msub> <mo>/</mo> <msub> <mi>d</mi> <mrow> <mi>j</mi> <mi>j</mi> </mrow> </msub> </mrow> <mrow> <msub> <mi>&beta;</mi> <mi>i</mi> </msub> <mo>/</mo> <msub> <mi>d</mi> <mrow> <mi>i</mi> <mi>i</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>&beta;</mi> <mi>j</mi> </msub> <mo>/</mo> <msub> <mi>d</mi> <mrow> <mi>j</mi> <mi>j</mi> </mrow> </msub> </mrow> </mfrac> <mo>,</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>&Element;</mo> <msub> <mi>N</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>;</mo> </mrow>

Wherein, h_ijIt is the off-diagonal element in weight matrix H, h_iiIt is the diagonal element in weight matrix H.
8. the micro-capacitance sensor economic load dispatching method based on Web control as claimed in claim 7, it is characterised in that described In step S4, economic optimization scheduling process is as follows：

Economic load dispatching under the conditions of comprising inequality, calculates setting value according to distributed AC servo system rule, divides when setting value is more than During cloth power generation DG maximum capacities, design, which is crossed the border, handles rule, and following modification is made to above derived control law：

<mrow> <mover> <mi>P</mi> <mo>~</mo> </mover> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msup> <mi>H</mi> <mi>T</mi> </msup> <mo>&CenterDot;</mo> <mrow> <mo>(</mo> <mover> <mi>P</mi> <mo>~</mo> </mover> <mo>(</mo> <mrow> <mi>t</mi> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> <mo>+</mo> <mi>&alpha;</mi> <mo>)</mo> </mrow> <mo>-</mo> <mi>&alpha;</mi> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>i</mi> <mi>f</mi> </mrow> </mtd> <mtd> <mrow> <mi>i</mi> <mo>&NotElement;</mo> <msub> <mi>&Omega;</mi> <mi>P</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mtable> <mtr> <mtd> <msubsup> <mi>p</mi> <mi>i</mi> <mi>min</mi> </msubsup> </mtd> <mtd> <mrow> <mi>o</mi> <mi>r</mi> </mrow> </mtd> <mtd> <mrow> <msubsup> <mi>p</mi> <mi>i</mi> <mi>max</mi> </msubsup> <mo>,</mo> </mrow> </mtd> </mtr> </mtable> </mtd> <mtd> <mrow> <mi>i</mi> <mi>f</mi> </mrow> </mtd> <mtd> <mrow> <mi>i</mi> <mo>&Element;</mo> <msub> <mi>&Omega;</mi> <mi>P</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>.</mo> </mrow>
9. the micro-capacitance sensor economic load dispatching method based on Web control as claimed in claim 7, it is characterised in that described Distributed AC servo system rule is fully distributed control law in step S3, i.e., each Agent need to only collect the information of neighbours Agent.
10. a kind of micro-capacitance sensor economic load dispatching system based on Web control, it is characterised in that the system comprises have：

The output unit of controllable distributed power generation DG, derives that controlled distribution formula is sent out according to economic load dispatching model under constraints The theoretical optimal output formula of electric DG；

Micro-capacitance sensor dual layer elements, communication network is constructed according to micro-capacitance sensor bilayer Controlling model；The micro-capacitance sensor bilayer Controlling model Including：Lower floor's micro-capacitance sensor and upper layer communication network；

Weight matrix unit, the distribution is obtained according to the exploited in communication weight matrix, and according to the weight matrix Formula control law；

The economic optimization scheduling unit of micro-capacitance sensor, calculates setting value, according to the setting value calculated according to distributed AC servo system rule Controllable distributed power generation DG outputs are adjusted, realize the economic optimization scheduling of micro-capacitance sensor.