CN114498708B - Multi-mode optimization operation method for energy storage system of communication base station - Google Patents

Abstract

The invention discloses a multi-mode optimization operation method for a communication base station energy storage system, which is based on optimization of distribution network load power and new energy output of an area where the communication base station energy storage system is located, divides the operation mode of the communication base station into three types of fluctuation stabilization, time-of-use electricity price and standby supply, considers fluctuation of power generation output and uncertainty of electricity load of distributed new energy in the area, constructs an optimization model taking system operation cost, maintenance cost and area distribution network power variance as targets according to the operation mode, introduces nonlinear constraint to construct robust optimization problem, solves the problem by using an improved list and constraint generation algorithm, and obtains corresponding scheduling strategies to guide the optimization operation of the communication base station energy storage system. The invention improves the utilization rate of the energy storage system of the communication base station, reduces the power fluctuation of the distribution network of the area where the energy storage system of the communication base station is located, improves the electric energy quality, ensures the balance of regional power supply and demand and effectively reduces the electricity cost of users.

Description

A multi-mode optimization operation method for a communication base station energy storage system

Technical Field

The present invention relates to the field of energy storage optimization scheduling, and in particular to a multi-mode optimization operation method for a communication base station energy storage system solved by an improved column and constraint algorithm.

Background Art

After a large number of distributed energy storages were connected to the distribution network, the traditional operation mode of the distribution network was changed. The original control system used a sequential control method with low efficiency, high communication requirements, and general control effect. Most distributed energy storages have small power generation, large numbers, and are scattered on the user side. Direct dispatch of all distributed energy storages will lead to excessive costs and reduced economic benefits, which need further integration. In addition, with the large-scale construction of 5G base stations, communication base station energy storage systems are widely used, and sufficient backup power storage capacity is reserved. The investment cost is huge but the utilization rate is low. The economic benefits brought by the optimization of energy storage operation have not been fully utilized. It is a dormant resource with huge development potential. The integrated service and differentiated service models have their own advantages, but lack flexibility and cannot adapt to changing network needs. On the other hand, the current network resource allocation methods are mostly based on software simulation or theoretical derivation, and lack specific implementation methods. By fully tapping the user-side energy storage resources and conducting research on the optimized operation of user-side energy storage clusters, a benign interaction between load and storage can be achieved, the economy of users after installing energy storage can be improved, the user's electricity cost can be effectively reduced, the refined use of energy storage can be promoted, and the overall energy efficiency of society can be improved.

Existing energy storage optimization operation methods mostly construct robust optimization problems and use column and constraint algorithms to solve them. In constructing robust optimization problems, piecewise linear approximation of nonlinear objectives is often used. However, the increase in the number of segments will lead to a sharp increase in the complexity of problem solving. It is necessary to start with the robust quadratic optimization problem and study the optimization algorithm for directly solving nonlinear optimization objectives.

Summary of the invention

The purpose of the present invention is to provide a multi-mode optimization operation method for a communication base station energy storage system in view of the deficiencies in the prior art.

The objective of the present invention is achieved through the following technical solutions:

A multi-mode optimization operation method for a communication base station energy storage system, the method is optimized based on the distribution network load power and new energy output in the area where the communication base station energy storage system is located, specifically comprising:

The control center issues control instructions to make the communication base station energy storage system enter a specific mode of operation. The specific modes are divided into three types: 1) Fluctuation smoothing mode: This mode shifts the output of distributed new energy in the area during the low load period to the peak load period, making the load curve relatively smooth after energy storage optimization scheduling; 2) Time-of-use electricity price mode: charging when the electricity price is low and discharging when the electricity price is high, making a profit through the price difference; 3) Backup provision mode: When the upper power grid is out of power due to a fault, the output of the energy storage system is maximized to ensure the power supply of important loads in the region as much as possible.

The predicted values and uncertainty ranges of new energy output and regional distribution network load power are obtained through existing new energy power prediction technology and load prediction technology, and expressed in the form of box inequality;

Construct a robust optimization model for solving the multi-mode optimization operation strategy of the communication base station energy storage system:

Among them, u is a vector composed of the predicted values of new energy output and load power issued by the control center in a scheduling cycle, and its value range U is a polyhedron independent of y. y is a vector composed of the upper limit of charging power and the upper limit of discharging power of the communication base station energy storage system, and its value range Y is a polyhedron independent of u. x is a vector composed of the electric energy purchased and sold by the communication base station energy storage system from the upper power grid during a scheduling cycle, and its value range is determined by the linear constraints composed of u and y. According to the operation mode of the communication base station energy storage system, in the fluctuation smoothing mode, the optimization objective function f(y,u,x) is the evaluation index of regional distribution network peak shaving and valley filling, and in the time-of-use electricity price mode, the optimization objective function f(y,u,x) is the total electricity price cost.

Apply the improved column and constraint generation algorithm to solve the above robust optimization problem:

(1) Set LB = -∞, UB = +∞, and iteration count k = 1. Define a discrete set V and select any vertex of U as the initial element of discrete set V.

(2) Solving MP

y∈Y

Solved renew If the difference between UB and LB is less than the preset allowable error, go to step (5).

(3) Solving SP

s.t.u∈U

Solved Will Add discrete set V. If Update And record As the current optimal y. If the difference between UB and LB is less than the preset allowable error, go to step (5).

(4) Add variable x _k+1 to MP and add constraints to MP

Update k=k+1 and go to step (2).

(5) Return y* as the optimal solution to the original robust problem and end the algorithm.

Step 5: The optimal solution of the robust optimization problem solved in step 4 is applied to the communication base station energy storage system as the energy storage output within a scheduling period.

Furthermore, the predicted values and uncertainty ranges of the new energy output and distribution network load power in the area where the communication base station energy storage system is located, expressed in the form of a box inequality, are as follows:

P _pv,t,min ≤P _pv,t ≤P _pv,t,max

P _load,t,min ≤P _load,t ≤P _load,t,nax

P _pv,t is the new energy output at time t, P _pv,t,max and P _pv,t,min are the upper and lower bounds of P _pv,t respectively, P _load,t is the uncertain distribution network load power at time t, P _load,t,max and P _load,t,min are the upper and lower bounds of P _load,t respectively.

Furthermore, in the fluctuation smoothing mode, the optimization objective function f(y, u, x) is the evaluation index of peak shaving and valley filling of the regional distribution network:

Under the time-of-use electricity price mode, the optimization objective function f(y, u, x) is the total electricity price cost:

In the backup provision mode, the communication base station energy storage system outputs power to the maximum extent without optimizing the output. In order to ensure a certain degree of backup capacity, the lower limit of the energy storage SOC is limited during the optimization process.

Where: w ₁ , w ₂ are the weights of cost and power, respectively, and their values are [0, 1]. C _maint is the maintenance cost of the communication base station energy storage system and the maintenance cost of new energy, expressed as:

in,

SOC ₁ = SOC _T

Δt represents the step length, and T represents a scheduling cycle. m _pv is the maintenance cost coefficient of new energy, P _pv,t represents the output of new energy at time t, m _b is the maintenance cost coefficient of the communication base station energy storage system, P _b+,t represents the power storage of the communication base station energy storage system at time t, and P _b-,t represents the discharge of the communication base station energy storage system at time t. _{P b,max+} represents the upper limit of the charging power of the communication base station energy storage system,

P _b,max- represents the lower limit of the charging power of the communication base station energy storage system. E _t represents the energy stored in the communication base station energy storage system at time t, and η _c and η _d represent the charging and discharging efficiency respectively. SOC _min and SOC _max represent the upper and lower limits of the state of charge SOC _t operating range of the communication base station energy storage system. E _rated represents the total rated capacity of the communication base station energy storage system.

C _charge is the total electricity price of the communication base station energy storage system purchasing and selling electricity to the upper power grid, expressed as:

P _buy,t represents the electric energy purchased by the communication base station energy storage system from the upper power grid at time t, and P _sell,t represents the electric energy sold by the communication base station energy storage system to the upper power grid at time t. c _buy,t and c _sell,t represent the electricity prices for purchasing and selling electricity respectively, and 0≤c _sell,t ＜c _buy,t . C _var is the total power variance of the regional distribution network, expressed as:

in,

P _buy,t -P _sell,t =P _load,t -P _pv,t +P _b+,t -P _b-,t

The total power of the distribution network in the area is expressed as P _buy,t -P _sell,t =P _load,t -P _pv,t +P _b+,t -P _b-,t , where P _pv,t is the output of new energy at time t, and P _load,t is the uncertain load power at time t. is the average value of P _buy,t in T, is the average value of P _sell,t within T.

Furthermore, the operation mode is determined according to the requirements:

If the predicted value of regional distribution network load power fluctuates greatly between the obtained new energy output and the regional distribution network load power forecast value, the power leveling mode will be entered; otherwise, the time-of-use electricity price mode will be entered to reduce electricity costs;

When the upper grid loses power, it enters the backup supply mode to ensure that the regional distribution network load is not disconnected.

The beneficial effect of the present invention is that compared with the original single operation mode of the communication base station energy storage as a backup power supply, the present invention adds fluctuation smoothing and time-of-use electricity price modes; for the latter two operation modes, without affecting its backup power supply function, the uncertainty of the new energy output and distribution network load in the area where the communication base station energy storage is located is taken into account, and a robust optimization problem is constructed for the two newly added operation modes of the energy storage base station operation, and the nonlinear form of the robust optimization objective function is retained, and an improved column and constraint algorithm is used for solving it, thereby realizing the solution and application of robust optimization problems with complex constraints, and can solve the optimal scheduling problem of energy storage caused by source and load uncertainty, improve the utilization rate of the communication base station energy storage system, reduce the power fluctuation of the distribution network in the area where the communication base station energy storage system is located, improve the power quality, ensure the balance of regional power supply and demand, effectively reduce the user's electricity cost, and provide power backup in emergency situations such as power outages.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of a multi-mode optimization operation method for a communication base station energy storage system.

Figure 2 shows the uncertainty set of photovoltaic output in the example.

Figure 3 shows the uncertainty set of user load in the example.

Figure 4 shows the total power of the regional distribution network before fluctuation smoothing, the total power of the regional distribution network after fluctuation smoothing, and the energy storage output power under the expected most unfavorable possibility in the example.

Figure 5 is a comparison of regional distribution network electricity costs before and after optimization under the time-of-use electricity price model in the example.

DETAILED DESCRIPTION

The present invention will be described in detail below with reference to the accompanying drawings.

A multi-mode optimization operation method for a communication base station energy storage system, the method is optimized based on the distribution network load power and new energy output in the area where the communication base station energy storage system is located, wherein new energy refers to new energy power generation equipment in the communication base station area, such as photovoltaic equipment, wind power generation equipment, etc., and distribution network load power refers to the total load power of various types of users corresponding to the distribution network distributed electric energy in the communication base station area. Specifically including:

Step 1: Use existing technologies to predict the new energy output and its uncertainty (photovoltaic output prediction based on neural networks, etc.) and the distribution network load power and its uncertainty (short-term load prediction based on big data, etc.) in the area where the communication base station energy storage system is located, and use the box uncertainty set for the above prediction data, that is, the box uncertainty set with future output power as shown in Figures 2 and 3:

P _pv,t,min ≤P _pv,t ≤P _pv,t,max

P _load,t,min ≤P _load,t ≤P _load,t,max

For time t, according to the prediction results, the uncertain new energy output power P _pv,t has an upper bound P _pv,t,max and a lower bound P _pv,t,min , and the uncertain load power P _load,t also fluctuates between the upper and lower bounds P _load,t,max and P _load,t,min .

Determine the operation mode based on demand: If the predicted value of regional distribution network load power fluctuates greatly between the obtained new energy output and the regional distribution network load power forecast value, enter the power smoothing mode; this mode shifts the distributed new energy output of the region in the low load period to the peak load period, making the load curve relatively smooth after the energy storage optimization scheduling;

Otherwise, it will enter the time-of-use electricity price mode, charging when the electricity price is low and discharging when the electricity price is high, reducing the electricity cost and making a profit through the price difference;

When the upper grid loses power, it enters the backup mode and the energy storage system maximizes its output to ensure the power supply to important loads in the region as much as possible.

According to the determined operation mode, control instructions are issued through the control center to make the communication base station energy storage system enter a specific mode of operation.

Step 2: Establish a robust optimization model for the communication base station energy storage system:

Among them, u is a vector consisting of the predicted values of new energy output and load power issued by the control center in a dispatching cycle. Considering that the power prediction result has certain uncertainties but has upper and lower bounds, it is expressed in the form of a box inequality set, and its value range U is a polyhedron independent of y. y is a vector consisting of the upper and lower limits of the charging power of the communication base station energy storage system, and its value range Y is a polyhedron independent of u. x is a vector consisting of the electric energy purchased and sold by the communication base station energy storage system from the upper power grid during a dispatching cycle, and its value range is determined by the linear constraints composed of u and y. According to the operation mode of the communication base station energy storage system, in the fluctuation smoothing mode, the optimization objective function f(y,u,x) is the evaluation index of the peak shaving and valley filling of the regional distribution network, and in the time-of-use electricity price mode, the optimization objective function f(y,u,x) is the total electricity price cost.

Preferably, for different operation modes, the optimization objective function has the following two options:

(1) Time-of-use electricity price model:

The optimization objective function consists of the following two parts:

a. Total electricity price of power purchase and sales of communication base station energy storage system

Where T represents a dispatch cycle, P _buy,t represents the power purchased by the communication base station energy storage system from the upper power grid at time t, and P _sell,t represents the power sold by the communication base station energy storage system to the upper power grid at time t. _{c buy,t} and c _sell,t represent the electricity prices for purchasing and selling electricity, respectively. When the model adopts time-of-use electricity prices, the electricity prices at different times t will change over time according to the specific local electricity price policies.

b. Maintenance cost of communication base station energy storage system and maintenance cost of new energy output

Where m _pv is the maintenance cost coefficient of new energy, P _pv,t represents the output of new energy at time t, m _b is the maintenance cost coefficient of the communication base station energy storage system, P _b+,t represents the storage of the communication base station energy storage system at time t, and P _b-,t represents the discharge of the communication base station energy storage system at time t. Δt represents the step size.

f (y, u, x) = w ₁ (C _maint + C _charge )

(2) Volatility Smoothing Mode:

The total power of the regional distribution network can be expressed as P _buy,t -P _sell,t , where is the average value of P _buy,t in T, is the average value of P _sell,t within the current T.

f(y,u,x) _{= w2Cvar}

w ₁ ,w ₂ are the weights of cost and power respectively, and their values are [0,1].

The remaining constraints include

(1) Constraints of communication base station energy storage system

Due to the nature of the equipment itself, the input and output power of the energy storage system has upper and lower limits. In the formula, P _b,max+ represents the upper limit of the charging power of the communication base station energy storage system, and P _b,max- represents the lower limit of the discharging power of the communication base station energy storage system.

The state of charge (SOC) of the energy storage system must operate within a certain range, and its lower limit corresponds to the backup provision mode. In the formula, _Et represents the energy stored in the communication base station energy storage system at time t, and _ηc and _ηd represent the charging and discharging efficiency respectively. The state of charge _SOCt of the communication base station energy storage system needs to operate within a limited range so that the communication base station energy storage system can have the expected performance. _SOCmin and _SOCmax represent the upper and lower limits of the range. _Erated represents the total rated capacity of the communication base station energy storage system.

SOC ₁ = SOC _T

The energy stored in the communication base station energy storage system needs to be equal at the beginning and end of an optimization cycle, which makes the impact of the energy stored in the energy storage system on any optimization cycle equal, and adjacent optimization cycles can be connected.

(2) Regional distribution network power equation constraints

P _buy,t -P _sell,t =P _load,t -P _pv,t +P _b+,t -P _b-,t

To simplify the model, grid losses are ignored. The energy of the regional distribution network interacting with the outside world at time t is determined by the amount of new energy power generation, the total load on the user side, and the charging and discharging of the communication base station energy storage system.

make

Finally, the following robust optimization model can be established

You must also add the condition

0≤c _{sell, t} ＜c _{buy, t}

To ensure is a convex function.

Step 3: Import known parameters. The parameters in this example mainly refer to the time-of-use electricity price policy of a certain place, the operation example of the communication base station energy storage system, the IEEE calculation example, etc. The parameter values are shown in the following table

To solve the above robust optimization problem, the MP of the following algorithm involves nonlinear convex optimization, and the SP involves nonlinear non-convex optimization. It is necessary to use a solver that supports both, such as MAATLAB, gurobi, etc.

(1) Set LB = -∞, UB = +∞, and iteration count k = 1. Define a discrete set V, and select any vertex of U as the initial element of the discrete set V. Preset the allowable error ε = 0.01.

(2) Solving MP

y∈Y

Solved renew If the difference between UB and LB is less than the preset allowable error ε, go to step (5).

(3) Solving SP

s.t.u∈U

Solved Will Add discrete set V. If Update And record As the current optimal y. If the difference between UB and LB is less than the preset allowable error, go to step (5).

(4) Add variable x _k+1 to MP and add constraints to MP

Update k=k+1 and go to (2).

(5) Return y ^* as the optimal solution to the original robust problem and end the algorithm.

Step 4: Apply y ^* obtained in step 3 as the energy storage output within a scheduling period to the communication base station energy storage system.

Figure 4 shows the optimization effect of the optimal solution under the fluctuation smoothing mode in a cycle under the most unfavorable possibility. It can be seen that in this case, the energy storage output is reasonably planned to minimize the total power variance, and the distributed new energy output in the area during the load valley period is shifted to the load peak period, making the load curve after energy storage optimization scheduling relatively flat. Figure 5 shows the optimization effect of the optimal solution under the time-of-use electricity price mode in a cycle. It can be seen that in this case, the energy storage output is reasonably planned to minimize the total electricity price and save costs.

Compared with the original single operation mode of using energy storage as a backup power supply in the communication base station, the present invention adds fluctuation smoothing and time-of-use electricity price modes. For the latter two operation modes, without affecting its backup power supply function, the uncertainty of new energy output and distribution network load in the area where the communication base station energy storage is located is taken into account, and a robust optimization problem is constructed for the two newly added operation modes of the energy storage base station for optimization. The application of this method can significantly improve the utilization rate of the communication base station energy storage system, reduce the power fluctuation of the distribution network in the area where the communication base station energy storage system is located, improve the power quality, ensure the balance of regional power supply and demand, and effectively reduce users' electricity costs.

Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the present invention.

Claims (4)

1. A multi-mode optimization operation method for a communication base station energy storage system, characterized in that the method is optimized based on the distribution network load power and new energy output in the area where the communication base station energy storage system is located, specifically comprising: The control center issues control instructions to make the communication base station energy storage system enter a specific mode of operation. The specific modes are divided into three types: 1) Fluctuation smoothing mode: This mode shifts the output of distributed new energy in the area during the low load period to the peak load period, making the load curve relatively smooth after energy storage optimization scheduling; 2) Time-of-use electricity price mode: charging when the electricity price is low and discharging when the electricity price is high, making a profit through the price difference; 3) Backup provision mode: in the event of a power outage in the upper power grid due to a fault, the energy storage system output is maximized to ensure the power supply of important loads in the region as much as possible; The predicted values and uncertainty ranges of the new energy output and distribution network load power in the area where the communication base station energy storage system is located are obtained through the existing new energy power prediction technology and load prediction technology, and are expressed in the form of box inequality. Construct a robust optimization model for solving the multi-mode optimization operation strategy of the communication base station energy storage system: Among them, u is a vector composed of the predicted values of new energy output and load power issued by the control center in a scheduling cycle, and its value range U is a polyhedron independent of y. y is a vector composed of the upper limit of charging power and the upper limit of discharging power of the communication base station energy storage system, and its value range Y is a polyhedron independent of u. x is a vector composed of the electric energy purchased and sold by the communication base station energy storage system from the upper power grid within a scheduling cycle, and its value range is determined by the linear constraints composed of u and y. According to the operation mode of the communication base station energy storage system, in the fluctuation smoothing mode, the optimization objective function f(y,u,x) is the evaluation index of regional distribution network peak shaving and valley filling, and in the time-of-use electricity price mode, the optimization objective function f(y,u,x) is the total electricity price cost; Apply the improved column and constraint generation algorithm to solve the above robust optimization problem: (1) Set LB = -∞, UB = +∞, and iteration count k = 1; define a discrete set V and select any vertex of U as the initial element of the discrete set V; (2) Solving MP stη≥f(y,u _i ,x _i ),(u _i ∈V), y∈Y x _i ∈F(y,u _i ),(u _i ∈V), Solved renew If the difference between UB and LB is less than the preset allowable error, go to step (5); (3) Solving SP s.t.u∈U Solved Will Add discrete set V; if Update And record As the current optimal y; if the difference between UB and LB is less than the preset allowable error, go to step (5); (4) Add variable x _k+1 to MP and add constraints to MP Update k=k+1 and go to step (2); (5) Return y ^* as the optimal solution to the original robust problem and end the algorithm; Step 5: The optimal solution of the robust optimization problem solved in step 4 is applied to the communication base station energy storage system as the energy storage output within a scheduling period. 2. According to a multi-mode optimization operation method of a communication base station energy storage system as described in claim 1, it is characterized in that the predicted values and uncertainty ranges of the new energy output and distribution network load power in the area obtained by the existing new energy power prediction technology and load prediction technology are as follows: P _pv,t,min ≤P _pv,t ≤P _pv,t,max P _load,t,min ≤P _load,t ≤P _load,t,max P _pv,t is the new energy output at time t, P _pv,t,max and P _pv,t,min are the upper and lower bounds of P _pv,t respectively, P _load,t is the uncertain distribution network load power at time t, P _load,t,max and P _load,t,min are the upper and lower bounds of P _load,t respectively. 3. A multi-mode optimization operation method for a communication base station energy storage system according to claim 1, characterized in that, in the fluctuation smoothing mode, the optimization objective function f(y,u,x) is an evaluation index of regional distribution network peak shaving and valley filling: Under the time-of-use electricity price mode, the optimization objective function f(y,u,x) is the total electricity price cost: In the backup provision mode, the communication base station energy storage system outputs power to the maximum extent without optimizing the output. In order to ensure a certain degree of backup capacity, the lower limit of the energy storage SOC is limited during the optimization process; Where: w ₁ ,w ₂ are the weights of cost and power, respectively, and their values are [0,1]; C _maint is the maintenance cost of the communication base station energy storage system and the maintenance cost of new energy, expressed as: in, SOC ₁ = SOC _T Δt represents the step length, T represents a scheduling cycle; m _pv is the maintenance cost coefficient of new energy, P _pv,t represents the output of new energy at time t, m _b is the maintenance cost coefficient of the communication base station energy storage system, P _b+,t represents the power storage of the communication base station energy storage system at time t, and P _b-,t represents the discharge of the communication base station energy storage system at time t; P _b,max+ represents the upper limit of the charging power of the communication base station energy storage system, and P _b,max- represents the lower limit of the charging power of the communication base station energy storage system; E _t represents the energy stored in the communication base station energy storage system at time t, η _c and η _d represent the charging and discharging efficiency respectively; SOC _min and SOC _max represent the upper and lower limits of the operating range of the state of charge SOC _t of the communication base station energy storage system; E _rated represents the total rated capacity of the communication base station energy storage system; C _charge is the total electricity price of the communication base station energy storage system purchasing and selling electricity to the upper power grid, expressed as: P _buy,t represents the electric energy purchased by the communication base station energy storage system from the upper power grid at time t, and P _sell,t represents the electric energy sold by the communication base station energy storage system to the upper power grid at time t; c _buy,t and c _sell,t represent the electricity prices for purchasing and selling electricity respectively, and 0≤c _sell,t ＜c _buy,t; C _var is the total power variance of the regional distribution network, which can be expressed as: in, P _buy,t -P _sell,t =P _load,t -P _pv,t +P _b+,t -P _b-,t The total power of the distribution network in the area is expressed as P _buy,t -P _sell,t =P _load,t -P _pv,t +P _b+,t -P _b-,t , where P _pv,t is the output of new energy at time t, and P _load,t is the uncertain load power at time t. is the average value of P _buy,t in T, is the average value of P _sell,t within T. 4. A multi-mode optimization operation method for a communication base station energy storage system according to claim 1, characterized in that the operation mode is determined according to demand: If the predicted value of regional distribution network load power fluctuates greatly between the obtained new energy output and the regional distribution network load power forecast value, the power leveling mode will be entered; otherwise, the time-of-use electricity price mode will be entered to reduce electricity costs; When the upper grid loses power, it enters the backup supply mode to ensure that the regional distribution network load is not disconnected.