A distribution network voltage reduction and energy saving optimization method considering power spring

Abstract

The present invention relates to the technical field of energy-saving in distribution networks, and in particular to a method for optimizing voltage reduction and energy saving in distribution networks taking into account electric springs, comprising: establishing a node admittance matrix and an electric spring steady-state model of the distribution network; constructing a corresponding first-stage optimization model and a second-stage optimization model based on the node admittance matrix and the electric spring steady-state model of the distribution network; the first-stage optimization model generates a dispatching strategy for a corresponding discrete control device through the predicted data of load demand and distributed power generation output during a rolling period; the second-stage optimization model optimizes and updates the set value of the electric spring according to the predicted data of load demand and distributed power generation output during a rolling period and the dispatching strategy of the discrete control device, and generates the output reactive power of the electric spring. The method for optimizing voltage reduction and energy saving in distribution networks of the present invention can improve the regulating response efficiency of voltage reduction and energy saving in distribution networks, and can realize that the load on the demand side follows the change of power generation.

Description

Technical Field

[0001] The present invention relates to the technical field of energy-saving in distribution networks, and in particular to a voltage reduction and energy-saving optimization method for distribution networks taking power springs into consideration. Background Art

[0002] As the demand for electricity load gradually increases, the world's energy shortage problem is becoming more and more serious. At the same time, the rise in natural gas and coal prices has greatly increased the cost of power generation in power plants, resulting in a shortage of electricity supply and a sharp increase in electricity prices. At this time, energy conservation is an important means to alleviate this contradiction. Active distribution network voltage reduction energy conservation is a method for the distribution network to reduce the load voltage according to the static voltage characteristics of the load, while keeping the load voltage within the allowable range, thereby reducing energy consumption. Many developed countries have achieved different degrees of energy conservation through active distribution network voltage reduction energy conservation.

[0003] The existing active distribution network voltage reduction and energy saving are achieved through discrete control devices, for example, by controlling the on-load tap changer and capacitor group to reduce the feeder voltage. However, there are a large number of intermittent distributed power generation in the existing active distribution network of renewable energy, such as photovoltaic power generation and wind power generation. However, the discrete control device has a slow response speed and limited adjustment time, and it is difficult to effectively cope with the fluctuations of renewable energy, resulting in poor effect of voltage reduction and energy saving in the distribution network. The applicant found in the study that adding an electric spring to the distribution network can solve the above problems to a certain extent. The electric spring is connected in series with non-critical loads to form an intelligent load with strong load response capability, which can transfer distributed energy fluctuations to non-critical loads and overcome the shortcomings of ordinary reactive power compensation devices. Therefore, how to design a distribution network voltage reduction and energy saving optimization method considering electric springs is a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of the above-mentioned deficiencies of the prior art, the technical problem to be solved by the present invention is: how to provide a distribution network voltage reduction and energy-saving optimization method taking into account the power spring, so as to improve the regulation response efficiency of the distribution network voltage reduction and energy-saving and realize that the load on the demand side follows the change of power generation, thereby ensuring the effect of voltage reduction and energy saving in the distribution network.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0006] A method for optimizing voltage reduction and energy saving in a distribution network considering power springs comprises the following steps:

[0007] S1: Establish the node admittance matrix and power spring steady-state model of the distribution network;

[0008] S2: Construct the corresponding first-stage optimization model and second-stage optimization model based on the node admittance matrix and the power spring steady-state model of the distribution network;

[0009] S3: The first-stage optimization model generates the dispatch strategy for the corresponding discrete control devices through the forecast data of load demand and distributed generation output within the rolling period;

[0010] S4: The second-stage optimization model optimizes and updates the set value of the power spring according to the forecast data of load demand and distributed generation output in the rolling period and the scheduling strategy of the discrete control equipment, and generates the output reactive power of the power spring.

[0011] Preferably, step S1 specifically includes the following steps:

[0012] S101: Obtaining parameters of the distribution network and the position of the power spring;

[0013] S102: Construct an equivalent circuit of an electric spring;

[0014] S103: According to the parameters of the distribution network, the position of the electric spring and the equivalent circuit of the electric spring, a corresponding node admittance matrix and an electric spring steady-state model are established.

[0015] Preferably, step S3 specifically includes the following steps:

[0016] S301: In the time interval [t 0 , t 0 +T H -Δt], forecasting load demand and distributed generation output over the rolling period;

[0017] S302: Generate N S Predict error scenarios;

[0018] S303: The first stage optimization model generates a dispatching strategy for the corresponding discrete control device through the forecast data of load demand and distributed generation output within the rolling period.

[0019] Preferably, step S4 specifically includes the following steps:

[0020] S401: In the time interval [t 0 , t 0 +Δt], determine the scheduling strategy of the corresponding discrete control device, and set t = t 0 ;

[0021] S402: at the time interval [t, t+Δτ], forecast the load demand and distributed generation output within the rolling period;

[0022] S403: The second stage optimization model optimizes and updates the set value of the power spring through the forecast data of load demand and distributed generation output in the rolling period and the scheduling strategy of the discrete control device to generate the output reactive power of the power spring.

[0023] Preferably, in step S404, after generating the output reactive power of the electric spring, let t=t+Δτ: if t>t 0 +Δt, then let t 0 =t 0 +Δt, and execute step S301; otherwise, return to step S402.

[0024] Preferably, in step S2, the first stage optimization model is expressed as:

[0025]

[0026] x∈l(x);

[0027] y t ∈Ω(x,y t );

[0028]

[0029]

[0030] Where: Ω and l represent y t and x constraint set; g(·) represents y t Function equation of α t Indicates the tap position of on-load voltage regulation; Indicates the number of capacitor banks running on the bus; V i t represents the voltage of the bus at time t; and Respectively represent the active power and reactive power of the bus at time t; represents the reactive power of the power spring at time t; represents the reactive power of the capacitor bank at time t; and f i t Represent the real and imaginary parts of the bus voltage respectively; Indicates the unit value of substation voltage.

[0031] Preferably, in step S2, the first stage optimization model is:

[0032] The objective function is:

[0033] Where: represents the substation active power at time t in the first stage optimization model;

[0034] The constraints are:

[0035] The load model is:

[0036] Where: and Respectively represent the active power and reactive power of the bus at time t; and Indicates active power and reactive power at rated voltage; Indicates voltage; a p，σ , b p，σ and c p，σ The model coefficient representing the active power; a q，σ , b q，σ and c q，σ Model coefficients representing reactive power;

[0037] The electric spring model is:

[0038] Where: and Indicates the minimum reactive power and maximum reactive power of the electric spring;

[0039] The on-load voltage regulation and capacitor model is:

[0040]

[0041] α t ∈{-5,-4,…,-1,0,1,…,4,5};

[0042]

[0043]

[0044]

[0045]

[0046]

[0047] Where: Indicates the unit value of substation voltage; α t Indicates the tap position of on-load voltage regulation; V Tap Indicates the voltage difference between two adjacent locations; OLTC max and Respectively represent the time range [t 0 , t 0 +T H -Δt] and the maximum tap position change time within period t; Indicates the number of capacitor banks operating on the bus; Indicates the maximum number of capacitor banks; q CB Indicates the reactive power of the capacitor bank; CBmax Indicates the maximum switch change time allowed within the time range;

[0048] The network security constraints are:

[0049]

[0050]

[0051] Where: V i min and V i max Indicates the minimum and maximum bus voltage; and Indicates minimum and maximum non-critical load voltages; and f i t Represent the real and imaginary parts of the bus voltage respectively; and represent the real and imaginary parts of the non-critical load voltage respectively.

[0052] Preferably, in step S2, the electric spring operation mode of the second stage optimization model is expressed as:

[0053]

[0054] y t ∈Ω(x,y t ).

[0055] Preferably, the first-stage optimization model and the second-stage optimization model are linearized by a sequence bound tightening algorithm.

[0056] Preferably, the dispatching strategy of the discrete control device includes the tap position of the on-load voltage regulation and the capacitor output.

[0057] Compared with the prior art, the power distribution network voltage reduction and energy-saving optimization method of the present invention has the following beneficial effects:

[0058] The present invention changes the operation mode of the existing power system in which the power generation follows the load changes by adding an electric spring to the distribution network, and can achieve the change of the demand side load following the power generation, thereby effectively solving the problems caused by the power generation of new energy.

[0059] The present invention adopts a two-stage optimization strategy to perform equivalent processing on the power spring, so that the set value of the power spring can be optimized and updated based on the predicted data of load demand and distributed power generation output in the rolling period and the scheduling strategy of the discrete control device, and the output reactive power of the power spring can be generated, thereby improving the regulation response efficiency of the distribution network voltage reduction and energy saving and realizing the demand side load following the change of power generation, thereby ensuring the effect of voltage reduction and energy saving of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to make the purpose, technical solution and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0061] Figure 1 The logic block diagram of the voltage reduction and energy saving optimization method of the distribution network considering the power spring;

[0062] Figure 2 Flow chart of the distribution network voltage reduction and energy saving optimization method considering power spring. DETAILED DESCRIPTION

[0063] The following is a further detailed description through specific implementation methods:

[0064] Example:

[0065] This embodiment discloses a distribution network voltage reduction and energy-saving optimization method considering power springs.

[0066] like Figure 1 As shown, the distribution network voltage reduction energy-saving optimization method considering the power spring includes the following steps:

[0067] S1: Establishing a node admittance matrix and a power spring steady-state model of the distribution network; in this embodiment, the node admittance matrix and the power spring steady-state model can be established by existing means, which will not be described in detail here.

[0068] S2: Construct the corresponding first-stage optimization model and second-stage optimization model based on the node admittance matrix and the power spring steady-state model of the distribution network;

[0069] S3: The first-stage optimization model generates a dispatching strategy for the corresponding discrete control device through the forecast data of load demand and distributed generation output during the rolling period; in this embodiment, the dispatching strategy for the discrete control device includes the tap position of the on-load voltage regulation and the capacitor output.

[0070] S4: The second-stage optimization model optimizes and updates the set value of the power spring according to the forecast data of load demand and distributed generation output in the rolling period and the scheduling strategy of the discrete control equipment, and generates the output reactive power of the power spring.

[0071] The present invention changes the operation mode of the existing power system in which the power generation follows the load change by adding an electric spring to the distribution network, and can realize that the demand side load follows the power generation change, thereby effectively solving the problems caused by the power generation of new energy. At the same time, the present invention adopts a two-stage optimization strategy to perform equivalent processing on the electric spring, so that the set value of the electric spring can be optimized and updated based on the predicted data of the load demand and distributed power generation output in the rolling period and the scheduling strategy of the discrete control device, and the output reactive power of the electric spring can be generated, thereby improving the regulation response efficiency of the distribution network voltage reduction and energy saving and realizing that the demand side load follows the power generation change, thereby ensuring the effect of voltage reduction and energy saving of the distribution network.

[0072] In the specific implementation process, Figure 2 As shown:

[0073] Step S1 specifically includes the following steps:

[0074] S101: Obtaining parameters of the distribution network and the position of the power spring;

[0075] S102: Construct an equivalent circuit of an electric spring;

[0076] S103: According to the parameters of the distribution network, the position of the electric spring and the equivalent circuit of the electric spring, a corresponding node admittance matrix and an electric spring steady-state model are established.

[0077] Step S3 specifically includes the following steps:

[0078] S301: In the time interval [t 0 , t 0 +T H -Δt], forecasting load demand and distributed generation output over the rolling period;

[0079] S302: Generate N S Prediction error scenario; In this embodiment, N can be generated by existing mature means S Predict error scenarios, the specific process will not be repeated here.

[0080] S303: The first stage optimization model generates a dispatching strategy for the corresponding discrete control device through the forecast data of load demand and distributed generation output within the rolling period.

[0081] Step S4 specifically includes the following steps:

[0082] S401: In the time interval [t 0 , t 0 +Δt], determine the scheduling strategy of the corresponding discrete control device, and set t = t 0 ;

[0083] S402: at the time interval [t, t+Δτ], forecast the load demand and distributed generation output within the rolling period;

[0084] S403: The second stage optimization model optimizes and updates the set value of the power spring through the forecast data of load demand and distributed generation output in the rolling period and the scheduling strategy of the discrete control device to generate the output reactive power of the power spring.

[0085] After generating the output reactive power of the electric spring, let t = t + Δτ: If t > t 0 +Δt, then let t 0 =t 0 +Δt, and execute step S301; otherwise, return to step S402.

[0086] The present invention can repeatedly execute the two-stage optimization strategy through the above steps, and can effectively perform equivalent processing on the power spring, so that it can better optimize and update the set value of the power spring based on the predicted data of load demand and distributed power generation output during the rolling period and the scheduling strategy of the discrete control device, generate the output reactive power of the power spring, and further improve the regulation response efficiency of the distribution network voltage reduction and energy saving, and can better realize the demand side load following the change of power generation.

[0087] In the specific implementation process, the first stage optimization model is expressed as:

[0088]

[0089] x∈l(x);

[0090] y t ∈Ω(x,y t );

[0091]

[0092]

[0093] Where: Ω and l represent y t and x constraint set; g(·) represents y t Function equation of α t Indicates the tap position of on-load voltage regulation; Indicates the number of capacitor banks running on the bus; V i t represents the voltage of the bus at time t; and Respectively represent the active power and reactive power of the bus at time t; represents the reactive power of the power spring at time t; represents the reactive power of the capacitor bank at time t; and f i t Represent the real and imaginary parts of the bus voltage respectively; Indicates the unit value of substation voltage.

[0094] In the specific implementation process, in step S2, the first stage optimization model is:

[0095] The objective function is:

[0096] Where: represents the substation active power at time t in the first stage optimization model;

[0097] The constraints are:

[0098] The constraints are substation voltage, OLTC tap position, time interval as an integer variable between -5 and 5, time period t and maximum time of OLTC tap position change within the time range, reactive power of capacitor bank, time period t and maximum capacitor bank switch change time within the time range;

[0099] The load model is:

[0100] Where: and Respectively represent the active power and reactive power of the bus at time t; and Indicates active power and reactive power at rated voltage; Indicates voltage; a p，σ , b p，σ and c p，σ The model coefficient representing the active power; a q，σ , b q，σ and c q，σ Model coefficients representing reactive power;

[0101] The electric spring model is:

[0102] Where: and Indicates the minimum reactive power and maximum reactive power of the electric spring;

[0103] The on-load voltage regulation and capacitor model is:

[0104]

[0105] α t ∈{-5,-4,…,-1,0,1,…,4,5};

[0106]

[0107]

[0108]

[0109]

[0110]

[0111] Where: Indicates the unit value of substation voltage; α t Indicates the tap position of on-load voltage regulation; V Tap Indicates the voltage difference between two adjacent locations; OLTC max and Respectively represent the time range [t 0 , t 0 +T H -Δt] and the maximum tap position change time within period t; Indicates the number of capacitor banks operating on the bus; Indicates the maximum number of capacitor banks; q CB Indicates the reactive power of the capacitor bank; CB max Indicates the maximum switch change time allowed within the time range;

[0112] The network security constraints are:

[0113]

[0114]

[0115] Where: V i min and V i max Indicates the minimum and maximum bus voltage; and Indicates minimum and maximum non-critical load voltages; and f i t Represent the real and imaginary parts of the bus voltage respectively; and represent the real and imaginary parts of the non-critical load voltage respectively.

[0116] In the specific implementation process, the operation mode of the electric spring in the second stage optimization model is expressed as:

[0117]

[0118] y t ∈Ω(x,y t ).

[0119] In the specific implementation process, the first-stage optimization model and the second-stage optimization model are linearized by the sequence bound tightening algorithm. The first-stage optimization model and the second-stage optimization model are non-convex mixed integer nonlinear programming problems, so it is difficult to obtain their optimal solutions and the calculation is very large.

[0120] The present invention linearizes the optimization model through a sequence bound tightening algorithm, and can transform the original nonlinear equations of the first-stage optimization model and the second-stage optimization model into mixed integer linear programming and linear programming problems, thereby reducing the amount of calculation of the optimization problem and improving the convergence of the model.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A distribution network voltage reduction and energy saving optimization method considering power spring, characterized in that: The following steps are involved: S1: Establish the node admittance matrix and power spring steady-state model of the distribution network; S2: Construct the corresponding first-stage optimization model and second-stage optimization model based on the node admittance matrix and the power spring steady-state model of the distribution network; In step S2, the first stage optimization model is expressed as: x∈l(x); and t ∈Ω(x,y t ); Where: Ω and l represent y t and x constraint set; g(·) represents y t Function equation of α t Indicates the tap position of on-load voltage regulation; Indicates the number of capacitor banks operating on the bus; represents the voltage of the bus at time t; and Respectively represent the active power and reactive power of the bus at time t; represents the reactive power of the power spring at time t; represents the reactive power of the capacitor bank at time t; and Represent the real and imaginary parts of the bus voltage respectively; Indicates the unit value of substation voltage; The first stage optimization model: The objective function is: Where: represents the substation active power at time t in the first stage optimization model; The constraints are: The load model is: Where: and Respectively represent the active power and reactive power of the bus at time t; and Indicates active power and reactive power at rated voltage; Indicates voltage; a p,σ 、b p,σ and c p,σ The model coefficient representing the active power; a q,σ 、b q,σ and c q,σ Model coefficients representing reactive power; The electric spring model is: Where: and Indicates the minimum reactive power and maximum reactive power of the electric spring; represents the reactive power of the power spring at time t; The on-load voltage regulation and capacitor model is: α t ∈{-5,-4,…,-1,0,1,…,4,5}； Where: Indicates the unit value of substation voltage; α t Indicates the tap position of on-load voltage regulation; V Tap Indicates the voltage difference between two adjacent locations; OLTC max and Respectively represent the time range [t0,t0+T H -Δt] and the maximum tap position change time within period t; Indicates the number of capacitor banks operating on the bus; Indicates the maximum number of capacitor banks; q CB Indicates the reactive power of the capacitor bank; CB max Indicates the maximum switch change time allowed within the time range; The network security constraints are: Where: and Indicates the minimum and maximum bus voltage; and Indicates minimum and maximum non-critical load voltages; and Represent the real and imaginary parts of the bus voltage respectively; and Represent the real and imaginary parts of the non-critical load voltage respectively; The operation mode of the electric spring in the second stage optimization model is expressed as: and t ∈Ω(x,y t ); S3: The first-stage optimization model generates the dispatch strategy for the corresponding discrete control devices through the forecast data of load demand and distributed generation output within the rolling period; Step S3 specifically includes the following steps: S301: In the time interval [t0, t0+T H -Δt], forecasting load demand and distributed generation output over the rolling period; S302: Generate N S Predict error scenarios; S303: The first stage optimization model generates a dispatch strategy for the corresponding discrete control device through the forecast data of load demand and distributed generation output within the rolling period; S4: The second stage optimization model optimizes and updates the set value of the power spring according to the forecast data of load demand and distributed generation output in the rolling period and the scheduling strategy of the discrete control device, and generates the output reactive power of the power spring; Step S4 specifically includes the following steps: S401: In the time interval [t0, t0+Δt], determine the scheduling strategy corresponding to the discrete control device, and set t=t0; S402: at the time interval [t, t+Δτ], forecast the load demand and distributed generation output within the rolling period; S403: The second stage optimization model optimizes and updates the set value of the power spring through the forecast data of load demand and distributed generation output in the rolling period and the scheduling strategy of the discrete control device to generate the output reactive power of the power spring; In step S403, after generating the output reactive power of the electric spring, set t=t+Δτ: if t>t0+Δt, set t0=t0+Δt and execute step S301; otherwise, return to step S402.

2. The method for optimizing power distribution network voltage reduction and energy saving by considering power springs as claimed in claim 1, characterized in that: Step S1 specifically includes the following steps: S101: Obtaining parameters of the distribution network and the position of the power spring; S102: Construct an equivalent circuit of an electric spring; S103: According to the parameters of the distribution network, the position of the electric spring and the equivalent circuit of the electric spring, a corresponding node admittance matrix and an electric spring steady-state model are established.

3. The method for optimizing power distribution network voltage reduction and energy saving by considering power spring as claimed in claim 1, characterized in that: The first-stage optimization model and the second-stage optimization model are linearized by the sequence bound tightening algorithm.

4. The method for optimizing power distribution network voltage reduction and energy saving by considering power springs as claimed in claim 1, characterized in that: The dispatch strategy of discrete control devices includes the tap position of the on-load tap changer and the capacitor output.

Images