# Review on Reactive Power and Voltage Optimization of Active Distribution Network with Renewable Distributed Generation and Time-Varying Loads

Authors: Xingmin Li; Hongwei Li; Shuaibing Li; Ziwei Jiang; Xiping Ma
Journal: Mathematical Problems in Engineering
 (2021)

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## Abstract

With a high proportion of renewable distributed generation and time-varying load connected to the distribution network, great challenges have appeared in the reactive power optimization control of the active distribution networks. This paper first introduces the characteristics of active distribution networks, the mechanism and research status of wind power, photovoltaic, and other renewable distributed generators, and time-varying loads participating in reactive power and voltage optimization. Then, the paper summarizes the methods of reactive power optimization and voltage regulation of active distribution network, including multi-timescale voltage optimization, coordinated optimization of network reconfiguration and reactive power optimization, coordinated optimization of active and reactive power optimization based on model predictive control, hierarchical and zoning control of reactive power, and voltage and power electronic switch voltage regulation. The pros and cons of the reactive power optimization algorithms mentioned above are summarized. Finally, combined with the development trend of the energy Internet, the future directions of reactive power and voltage control technology in the active distribution network are discussed.


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## Body

## 1. Introduction

As one of the most abundant renewable energy areas in China, the total exploitable amount of wind and photovoltaic energy resources in Northwest China ranks at the forefront. Due to the high altitude, less water vapor, and thin clouds, the photovoltaic energy resource reserves in this area are extremely rich with annual sunshine above 3200 hours and the total annual radiation over 6690–8360 MJ/m2 per year [1]. The statistics showed that the exploitable wind energy resources in this region can be up to 300 million kW, and the available wind resources in Gansu, Xinjiang, and Ningxia provinces accounted for about 1/3 of the total land wind energy in China [2]. With the in-depth promotion of a new round of the development of the western region strategy in China, green economy development based on renewable energy is an effective way to promote the economic development of Western China.By 2021, the new energy power generation dominated by renewable distributed generation (RDG) such as wind power and photovoltaic accounted for about 11% of the power consumption of the whole society, of which Qinghai and Gansu provinces account for more than 50% and 40%, respectively. To achieve the goal of “Carbon Peaking and Carbon Neutralization,” the National Energy Administration issued the notice on matters related to the development and construction of wind power and photovoltaic power generation in 2021. It was expected that by 2030, the proportion of non-fossil energy in primary energy consumption will decline to 25%, and the total installed capacity of wind power and photovoltaic power generation will increase to more than 1.2 billion kW. To promote energy conservation and emission reduction, develop a green economy, and help achieve the goal of “Carbon Peaking and Carbon Neutralization” in advance, developing new energy sources dominated by renewable distributed power and vigorously developing distribution networks containing RDG can effectively improve the energy efficiency of power system generation, transmission, distribution, and utilization. Building a new power system dominated by a high proportion of new energy becomes an effective measure to improve the consumption of new energy [3, 4].With the continuous development of social economy and power grid structure, in addition to renewable distributed generation such as wind power and photovoltaic power, there are also various loads with time-varying characteristics such as electric vehicles (EVs) and industrial loads [5], which brings challenges to the operation and control of active distribution network, especially the control of reactive power and voltage. This has become the primary technical problem of active distribution network [6, 7]. To solve the aforementioned problems, it is necessary to consider the collaborative optimization of various schemes, to realize the optimal control of ADN parameters, especially reactive power and voltage.Therefore, by expounding the characteristics of active distribution network with RDGs and time-varying loads, this paper discusses the voltage regulation mechanism of wind power, photovoltaic, and other renewable distributed generation participating in active distribution network and the research status of time-varying load participating in voltage regulation. And it also analyzes the reactive power optimization methods of active distribution network in detail, including multi-timescale voltage optimization, coordination of network reconfiguration and reactive power optimization, coordination and optimization of active and reactive power, model predictive control (MPC), hierarchical and zoning control, and power electronic switch voltage regulation.The possible measures of dealing with uncertain problems are introduced, and the advantages and disadvantages of the reactive power optimization algorithm are summarized. According to the development needs of the energy Internet, the future research directions of multienergy collaborative reactive power optimization control, interactive voltage optimization between users and power suppliers, and power electronic equipment participating in reactive power and voltage optimization regulation are discussed.The rest of this paper is organized as follows: the second part introduces the characteristics of active distribution network, the third part introduces the source load reactive power regulation mechanism in active distribution network, the fourth part summarizes the reactive power optimization methods, uncertain problem processing methods, and solution algorithms, the fifth part prospects the future research, and the sixth part gives the conclusion.


## 2. Characteristics of Active Distribution Network with RDG and Time-Varying Load

Active distribution network is the distribution network with internal distributed power generation and control and operational capability. Figure1 is a schematic diagram of a typical active distribution network. Compared with the traditional distribution network, the characteristics of active distribution network with RDG and time-varying load mainly include the following.Figure 1
Schematic diagram of active distribution network architecture.


### 2.1. The Uncertainty of Power Output and Load Demand

The grid connection of a high proportion of renewable distributed generators and the access of a variety of time-varying loads on the load side to the distribution network make the source load and both ends of the distribution network uncertain [8].The active power output of wind power generation and photovoltaic power generation is strongly correlated with season and climate conditions, with strong randomness and volatility. Figure2 shows the wind power and photovoltaic output curve of a new energy station in Northwest China. As the wind power output is affected by wind speed, the wind power output is more at 00 : 00-08 : 00 and 19 : 00-24 : 00, less at 08 : 00-10 : 00 and 15 : 00-19 : 00, and the output is the least at 10 : 00-15 : 00. The photovoltaic output is greatly affected by the light intensity, and almost no electric energy is generated in 00 : 00-08 : 00 and 18 : 00-24 : 00. The photovoltaic power generation increases with the increase of light intensity in 08 : 00-13 : 00 and gradually decreases in 13 : 00-18 : 00.Figure 2
Photovoltaic daily output curve of wind power in a certain area of Northwest China.Electric vehicles are charged disorderly in the charging station [9]. Figure 3 shows the daily charging demand forecast curve of electric vehicles in a certain area of Northwest China. From 08 : 00 to 11 : 00, a small number of users charge electric vehicles, and most users charge electric vehicles from 18 : 00 to 23 : 00. The load demand is closely related to users’ charging behaviour, with strong uncertainty. The electric arc furnace and rolling mill are nonlinear and time-varying loads in the steel plant. The electric energy required under different production conditions is very different in the process of steelmaking and rolling.Figure 3
Prediction curve of daily charging demand of electric vehicles in a certain area of Northwest China.


### 2.2. High Proportion Power Electronics

On the power supply side, the grid connection of wind power and photovoltaic power is inseparable from power electronic devices, which means that the distribution network has more power electronic interfaces [10]. On the grid side, power electronic transformers and power electronic switches have fast real-time response speed and strong regulation ability, so a large number of power electronic devices replace traditional equipment and further power electronation of active distribution network [11]. On the load side, the working mechanism of the electric vehicle charging pile is three-phase AC input. After filtering and rectification (AC/DC), pulsating DC is obtained, then the high-frequency inverter is carried out through a power conversion circuit (DC/DC), and finally, secondary high-frequency current integration filtering is carried out through a high-frequency isolation transformer to achieve stable DC output [12, 13]. Impact loads such as electric arc furnaces and rolling mills will cause extremely serious voltage fluctuation, flicker, negative sequence, and harmonic disturbance during production. Therefore, local reactive power compensation measures will generally be taken to suppress load fluctuation and improve power quality. Common reactive power compensation devices include switching virtual circuit(SVC), static var generator (SVG), and static synchronous compensator (STATCOM) [14], which are typical power electronic devices.


### 2.3. Diversified Operation Scenarios

Considering a large number of renewable distributed generation and time-varying loads connected to the distribution network, there is serious uncertainty at both ends of the source load, and the output at both ends of the source load has a time-series correlation with the power demand, and the operation scenarios are diversified [15]. For example, in the reactive power optimization of the distribution network, the modelling and solution of uncertain problems need to divide the distribution network into scenes. The purpose is to convert the uncertain factors into multiple deterministic factors, thus simplifying the difficulty of the solution.


### 2.4. The Fuzzification of Source Charge Boundary

Since the existence of renewable energy in the power grid is the coexistence of centralized and distributed energy, the large-scale access of electric vehicles and distributed energy storage to the power grid makes it possible for the load in the traditional distribution network to become a power source to provide power to the power system. The interaction technology between multiuser and power grid supply and demand and the intelligent interaction technology between the electric vehicle and power grid are the technical bottlenecks in the process of “new electrification” of the new power system [16]. In the future, the boundary between source loads will be gradually confused with the development of the power system.


## 2.1. The Uncertainty of Power Output and Load Demand

The grid connection of a high proportion of renewable distributed generators and the access of a variety of time-varying loads on the load side to the distribution network make the source load and both ends of the distribution network uncertain [8].The active power output of wind power generation and photovoltaic power generation is strongly correlated with season and climate conditions, with strong randomness and volatility. Figure2 shows the wind power and photovoltaic output curve of a new energy station in Northwest China. As the wind power output is affected by wind speed, the wind power output is more at 00 : 00-08 : 00 and 19 : 00-24 : 00, less at 08 : 00-10 : 00 and 15 : 00-19 : 00, and the output is the least at 10 : 00-15 : 00. The photovoltaic output is greatly affected by the light intensity, and almost no electric energy is generated in 00 : 00-08 : 00 and 18 : 00-24 : 00. The photovoltaic power generation increases with the increase of light intensity in 08 : 00-13 : 00 and gradually decreases in 13 : 00-18 : 00.Figure 2
Photovoltaic daily output curve of wind power in a certain area of Northwest China.Electric vehicles are charged disorderly in the charging station [9]. Figure 3 shows the daily charging demand forecast curve of electric vehicles in a certain area of Northwest China. From 08 : 00 to 11 : 00, a small number of users charge electric vehicles, and most users charge electric vehicles from 18 : 00 to 23 : 00. The load demand is closely related to users’ charging behaviour, with strong uncertainty. The electric arc furnace and rolling mill are nonlinear and time-varying loads in the steel plant. The electric energy required under different production conditions is very different in the process of steelmaking and rolling.Figure 3
Prediction curve of daily charging demand of electric vehicles in a certain area of Northwest China.


## 2.2. High Proportion Power Electronics

On the power supply side, the grid connection of wind power and photovoltaic power is inseparable from power electronic devices, which means that the distribution network has more power electronic interfaces [10]. On the grid side, power electronic transformers and power electronic switches have fast real-time response speed and strong regulation ability, so a large number of power electronic devices replace traditional equipment and further power electronation of active distribution network [11]. On the load side, the working mechanism of the electric vehicle charging pile is three-phase AC input. After filtering and rectification (AC/DC), pulsating DC is obtained, then the high-frequency inverter is carried out through a power conversion circuit (DC/DC), and finally, secondary high-frequency current integration filtering is carried out through a high-frequency isolation transformer to achieve stable DC output [12, 13]. Impact loads such as electric arc furnaces and rolling mills will cause extremely serious voltage fluctuation, flicker, negative sequence, and harmonic disturbance during production. Therefore, local reactive power compensation measures will generally be taken to suppress load fluctuation and improve power quality. Common reactive power compensation devices include switching virtual circuit(SVC), static var generator (SVG), and static synchronous compensator (STATCOM) [14], which are typical power electronic devices.


## 2.3. Diversified Operation Scenarios

Considering a large number of renewable distributed generation and time-varying loads connected to the distribution network, there is serious uncertainty at both ends of the source load, and the output at both ends of the source load has a time-series correlation with the power demand, and the operation scenarios are diversified [15]. For example, in the reactive power optimization of the distribution network, the modelling and solution of uncertain problems need to divide the distribution network into scenes. The purpose is to convert the uncertain factors into multiple deterministic factors, thus simplifying the difficulty of the solution.


## 2.4. The Fuzzification of Source Charge Boundary

Since the existence of renewable energy in the power grid is the coexistence of centralized and distributed energy, the large-scale access of electric vehicles and distributed energy storage to the power grid makes it possible for the load in the traditional distribution network to become a power source to provide power to the power system. The interaction technology between multiuser and power grid supply and demand and the intelligent interaction technology between the electric vehicle and power grid are the technical bottlenecks in the process of “new electrification” of the new power system [16]. In the future, the boundary between source loads will be gradually confused with the development of the power system.


## 3. Regulation Mechanism of Source Load Reactive Power in Active Distribution Network

The technical requirements for grid connection of distributed generation (GB/T 33593-2017) pointed out that the ways for renewable distributed generation to participate in the voltage regulation of distribution network can include adjusting the reactive power of power supply, adjusting the input of reactive power compensation equipment, and adjusting the transformation ratio of power supply transformer. This section introduces the mechanism of photovoltaic, wind power, and electric vehicles participating in reactive power and voltage regulation from two aspects, both the source and the load.


### 3.1. Mechanism of Photovoltaic Power Generation Participating in Reactive Power Optimization

Photovoltaic power generation can participate in the voltage regulation of active distribution network for reactive power and voltage control [17]. Photovoltaic power generation has reactive power control capability and can output reactive power to the distribution network [18–20]. The main reason is that the photovoltaic inverter has the ability of reactive power control. The topology of the photovoltaic grid-connected inverter is shown in Figure 4.Figure 4
Topology of photovoltaic grid-connected inverter.Since the topology of the grid-connected inverter is the same as that of an active reactive power compensation device, a photovoltaic array will transmit certain reactive power while transmitting active power to the power grid [21, 22]. To output standard sine wave current, PV grid-connected inverter generally adopts PQ control. The output active power and reactive power are shown in the following equations, respectively:(1)P=UocU2πfLsinα,(2)Q=Uoc22πfL−Uoc2πfLcosα,where Uoc is the output voltage of the inverter; U is the grid voltage; α is the phase angle difference between Uoc and U; L is the value of coupling inductance; and f is the system frequency.The reactive power regulation capacity of the grid-connected inverter is limited, and its output range is shown in as follows:(3)−Smax2−PPV2≤QPV≤Smax2−PPV2,where PPV is the active output of the inverter; QPV is the reactive output of the inverter; and Smax is the inverter capacity.Figure5 shows the P−Q capacity curve of the photovoltaic inverter. At point A : PV’s active power rating is PPV,max, and reactive power regulation range is [−QPV,1, QPV,1]. At point B: inverter cut-in and cut-out power PPV, cut, reactive power regulation range −QPV,2,QPV,2. During the day, the reactive power regulation range of the inverter is −QPV,2,−QPV,1 ∪ QPV,1,QPV,2. At night, the active output of the inverter is 0, and the reactive power regulation range is the rated capacity of the inverter, and the regulation capacity is strong.Figure 5
P−Q capacity curve of photovoltaic inverter.


### 3.2. Reactive Power Regulation Characteristics of Doubly Fed Induction Generator Wind Turbine

The basic structure of doubly fed induction generator (DFIG) is shown in Figure6 [23, 24], which is composed of a wind turbine, doubly fed generator, and two AC/DC rotor pulse width modulation (PWM) converters based on insulated gate bipolar transistor (IGBT) [25]. The stator side and grid-side converters emit or absorb reactive power. The grid-side converter of a doubly fed induction wind motor generally operates in the unit power factor mode. Because the reactive power transmitted by the PWM converter is small, it could be ignored [26].Figure 6
The basic structure of the DFIG wind turbine.The reactive powerQT of DFIG is the same as the reactive power QS transmitted at the stator side. The DFIG active and reactive power calculation formula is shown in the following equations:(4)PT1−s2+QT2=3USIS2,(5)PT1−s2+QT+3US2XS2=3XMXSUSIR2,(6)PT=1−sPS,where PT and QT are active output and reactive output of doubly fed induction wind turbine; US,PS, and XS are stator side voltage, active power, and stator leakage reactance, respectively; IR and IS are rotor transformer current and stator winding current, respectively; and s is the slip rate of the motor and XM is the excitation reactance.TheP−Q capacity curve of a doubly fed induction wind turbine is displayed in Figure 7. It can be seen that when the active power increases, the regulation range of reactive power will decrease. When the active output PH of DFIG is constant, DFIG adjusts the reactive power within QH,min,QH,max by adjusting the PWM converter current IR and stator winding current IS. QH,min is the static stability limit of the doubly fed induction wind turbine. When the absorbed reactive power of DFIG exceeds QH,min, DFIG works in an unstable state.Figure 7
P−Q capacity curve of doubly fed induction wind turbine.


### 3.3. Electric Vehicles Participate in Voltage Regulation of Active Distribution Network

Large-scale access of electric vehicles to the modern smart grid is an inevitable development trend. EVs can be used as distributed power supply or battery energy storage equipment and can be flexibly connected to or separated from the power grid, so they have the ability of reactive power and voltage regulation [27]. It mainly includes (i) load shedding or reverses power supply, (ii) reactive power response of electric vehicle charging pile, and (iii) build a real-time electric vehicle charging price scheme to stimulate users’ charging behavior.For the reactive power regulation of electric vehicles, a series of measures were conducted. Qi et al. [28] established the topology of grid-connected voltage regulation of EVs. It was reported that adjusting the power factor of the charger to compensate for the reactive power of the node and changing the charging active power was an effective control strategy, which had a significant effect on the voltage security, stability, and economy of the distribution network [29]. To change the charging behaviour of users through real-time electricity price, some methods were used to conduct this, including quantifying the response of vehicle owners to electricity price stimulation combining with the principle of consumer psychology, formulating dynamic time-of-time electricity price according to the predicted reactive load value, and making the vehicles in the occupation period cooperate with the reactive power compensation device to participate in reactive power optimization by changing the operation model of charging device [30]. A distributed load shedding control method was proposed to prevent voltage instability considering the interruptible characteristics of electric vehicles [31].


## 3.1. Mechanism of Photovoltaic Power Generation Participating in Reactive Power Optimization

Photovoltaic power generation can participate in the voltage regulation of active distribution network for reactive power and voltage control [17]. Photovoltaic power generation has reactive power control capability and can output reactive power to the distribution network [18–20]. The main reason is that the photovoltaic inverter has the ability of reactive power control. The topology of the photovoltaic grid-connected inverter is shown in Figure 4.Figure 4
Topology of photovoltaic grid-connected inverter.Since the topology of the grid-connected inverter is the same as that of an active reactive power compensation device, a photovoltaic array will transmit certain reactive power while transmitting active power to the power grid [21, 22]. To output standard sine wave current, PV grid-connected inverter generally adopts PQ control. The output active power and reactive power are shown in the following equations, respectively:(1)P=UocU2πfLsinα,(2)Q=Uoc22πfL−Uoc2πfLcosα,where Uoc is the output voltage of the inverter; U is the grid voltage; α is the phase angle difference between Uoc and U; L is the value of coupling inductance; and f is the system frequency.The reactive power regulation capacity of the grid-connected inverter is limited, and its output range is shown in as follows:(3)−Smax2−PPV2≤QPV≤Smax2−PPV2,where PPV is the active output of the inverter; QPV is the reactive output of the inverter; and Smax is the inverter capacity.Figure5 shows the P−Q capacity curve of the photovoltaic inverter. At point A : PV’s active power rating is PPV,max, and reactive power regulation range is [−QPV,1, QPV,1]. At point B: inverter cut-in and cut-out power PPV, cut, reactive power regulation range −QPV,2,QPV,2. During the day, the reactive power regulation range of the inverter is −QPV,2,−QPV,1 ∪ QPV,1,QPV,2. At night, the active output of the inverter is 0, and the reactive power regulation range is the rated capacity of the inverter, and the regulation capacity is strong.Figure 5
P−Q capacity curve of photovoltaic inverter.


## 3.2. Reactive Power Regulation Characteristics of Doubly Fed Induction Generator Wind Turbine

The basic structure of doubly fed induction generator (DFIG) is shown in Figure6 [23, 24], which is composed of a wind turbine, doubly fed generator, and two AC/DC rotor pulse width modulation (PWM) converters based on insulated gate bipolar transistor (IGBT) [25]. The stator side and grid-side converters emit or absorb reactive power. The grid-side converter of a doubly fed induction wind motor generally operates in the unit power factor mode. Because the reactive power transmitted by the PWM converter is small, it could be ignored [26].Figure 6
The basic structure of the DFIG wind turbine.The reactive powerQT of DFIG is the same as the reactive power QS transmitted at the stator side. The DFIG active and reactive power calculation formula is shown in the following equations:(4)PT1−s2+QT2=3USIS2,(5)PT1−s2+QT+3US2XS2=3XMXSUSIR2,(6)PT=1−sPS,where PT and QT are active output and reactive output of doubly fed induction wind turbine; US,PS, and XS are stator side voltage, active power, and stator leakage reactance, respectively; IR and IS are rotor transformer current and stator winding current, respectively; and s is the slip rate of the motor and XM is the excitation reactance.TheP−Q capacity curve of a doubly fed induction wind turbine is displayed in Figure 7. It can be seen that when the active power increases, the regulation range of reactive power will decrease. When the active output PH of DFIG is constant, DFIG adjusts the reactive power within QH,min,QH,max by adjusting the PWM converter current IR and stator winding current IS. QH,min is the static stability limit of the doubly fed induction wind turbine. When the absorbed reactive power of DFIG exceeds QH,min, DFIG works in an unstable state.Figure 7
P−Q capacity curve of doubly fed induction wind turbine.


## 3.3. Electric Vehicles Participate in Voltage Regulation of Active Distribution Network

Large-scale access of electric vehicles to the modern smart grid is an inevitable development trend. EVs can be used as distributed power supply or battery energy storage equipment and can be flexibly connected to or separated from the power grid, so they have the ability of reactive power and voltage regulation [27]. It mainly includes (i) load shedding or reverses power supply, (ii) reactive power response of electric vehicle charging pile, and (iii) build a real-time electric vehicle charging price scheme to stimulate users’ charging behavior.For the reactive power regulation of electric vehicles, a series of measures were conducted. Qi et al. [28] established the topology of grid-connected voltage regulation of EVs. It was reported that adjusting the power factor of the charger to compensate for the reactive power of the node and changing the charging active power was an effective control strategy, which had a significant effect on the voltage security, stability, and economy of the distribution network [29]. To change the charging behaviour of users through real-time electricity price, some methods were used to conduct this, including quantifying the response of vehicle owners to electricity price stimulation combining with the principle of consumer psychology, formulating dynamic time-of-time electricity price according to the predicted reactive load value, and making the vehicles in the occupation period cooperate with the reactive power compensation device to participate in reactive power optimization by changing the operation model of charging device [30]. A distributed load shedding control method was proposed to prevent voltage instability considering the interruptible characteristics of electric vehicles [31].


## 4. Reactive Power and Voltage Optimization of Active Distribution Network

### 4.1. Reactive Power and Voltage Optimization Method

For the voltage problems existing in the distribution network, voltage regulation is usually carried out by adjusting the tap of the on-load tap changer (OLTC) or switching slow-moving reactive power regulation resources such as shunt capacity bank (SCB) [32]. This kind of regulation mode is discrete and cannot be adjusted flexibly. As RDG is connected to the distribution network extensively, adjustable resources in the distribution network are gradually diversified, and the ways of reactive power optimization are becoming more and more abundant [33, 34].This section summarizes the latest research progress of reactive power optimization of active distribution network at home and abroad, mainly including the following six aspects: multi-timescale voltage optimization of active distribution network, collaborative optimization of active distribution network reconfiguration and reactive power optimization, active and reactive power coordination optimization of active distribution network, reactive power optimization of active distribution network based on model predictive control, and voltage optimization of active distribution network with power electronic switches.


#### 4.1.1. Multi-Timescale Voltage Optimization of Active Distribution Network

Multi-timescale voltage optimization divides voltage optimization into different stages. Different states have different timescales. Generally, it is divided into two stages [35]. The timescale of the first stage is usually 24 hours or 1 hour, which is called the day-ahead optimization. The timescale of the second stage is usually 15 min and 5 min, which is called intraday optimization. The principle of multitimescale voltage optimization is shown in Figure 8.Figure 8
Principle of multi-time scale voltage optimization.In Figure8, in the first stage, based on the power supply and load prediction data, the reactive power access of discrete reactive power compensation devices, on-load tap changer, and other slow-moving equipment in the active distribution network is adjusted in advance so that the active power and reactive power of each node of the distribution network will be in a reasonable range in the next 24 hours or 1 hour. The second stage is to adjust renewable distributed generation output, switching virtual circuit, static var generator, and other reactive power compensation device access and energy storage port output in real-time based on the current power output, load demand, and grid operation status information on the premise of configuring slow motion equipment in the first stage, thus stabilizing the insufficient or out-of-limit reactive voltage caused by RDG output and load demand fluctuation in the next 15 min or 5 min.For the day-ahead optimization and intraday optimization, Peng et al. [36] used the idea of MPC to configure discrete reactive power compensation devices in the day ahead and used continuously intraday power compensation devices to control voltage. Guowei et al. [37] used energy storage for day ahead and intraday voltage optimization of distribution network and realized voltage optimization by limiting the energy storage power range in the day ahead and adjusting the port output in real-time in intraday. Considering the influence of photovoltaic, Wei et al. [38] proposed a multi-time scale optimization control method for photovoltaic grid-connected distributed generation system, which aimed at minimizing the expected value of operation cost and carries out reactive power optimization of the distribution network in long timescale and short timescale. Further considering the influence of load characteristics on voltage, Zhiqiang et al. [39] carried out reactive power optimization in two stages: day-ahead optimization and real-time optimization and added the correction of the day-ahead optimization in the day. At the same time, considering the uncertainty of new energy and flexible load, double timescales (1-hour level and 15-minute level) [40] were proposed, which used the traditional on-load voltage regulating transformer on-load tap changer and shunt capacitor for hour level voltage optimization and used the reactive power output of new energy units for 15-minute level to control the voltage deviation of nodes.


#### 4.1.2. Collaborative Optimization of Active Distribution Network Reconfiguration and Reactive Power Optimization

The active distribution network has black start RDG as backup power supply and a large number of flexible loads, including source load that can interact with the distribution network in two directions and adjustable load regulated in one direction by the distribution network [41, 42].Figure9 shows the characteristic analysis of network reconfiguration. The topology of active distribution network is flexible and changeable. A reasonable network reconfiguration scheme can reduce network loss, promote RDG consumption, and improve the economy of the distribution network [43, 44]. Network reconfiguration also provides more possibilities for reactive power and voltage optimization of active distribution network.Figure 9
Network reconfiguration characteristics of active distribution network.Considering the influence of the uncertainty of renewable distributed generation and time-varying load on active distribution network, Haishu et al. [33] proposed a comprehensive optimization model of ADN combining dynamic reconfiguration and reactive power optimization. Ping et al. [45] proposed a two-stage reactive power and voltage optimization method. In the first stage, the reconstruction scheme and the operation state of slow action equipment were determined, and in the second stage, the influence of fast action equipment on RDG random output was adjusted. Dihong [46] introduced renewable distributed generation into the process of distribution network reconfiguration and coordinated the two schemes of network reconfiguration and adjusting RDG injection power to obtain lower network loss and higher voltage quality.With the increase of DC load in the distribution network, the distribution network gradually develops to AC/DC hybrid distribution network. Xu et al. [47] applied network reconfiguration to the AC/DC power grid, which can optimize the power flow distribution of the distribution system and improve the economy. Further considering the temporal and spatial imbalance of the net load of the distribution network, Wang et al. [48] took the light abandonment, load loss, and minimum switching cost as the goal and divided the period according to the load timing characteristics; the multiperiod and multilevel reconfiguration scheme of active distribution network was obtained.


#### 4.1.3. Active and Reactive Power Coordination Optimization of Active Distribution Network

Large-timescale renewable distributed generation grid connection makes active power and reactive power coupling of active distribution network strong [49]. In terms of distribution network distributed optimization, relevant research at home and abroad mainly focused on reactive power optimization, and there was less research on active and reactive power coordination optimization. Active power optimization can reduce distribution network loss and improve economic efficiency. Figure 10 analyzes the regulation significance of active power optimization and reactive power optimization to active distribution network from the perspectives of economy and safety and defines the necessity of active and reactive power coordination optimization to active distribution network regulation.Figure 10
Active and reactive power optimization characteristics of active distribution network.To centralize the management and control of multiple types of distributed generators and multiple types of loads in active distribution network, Lingfeng et al. [50] modelled the active and reactive power output characteristics of various schedulable resources in ADN, planned and scheduled the adjustable active and reactive power in the system in real time, improved the consumption level of new energy and reduces voltage deviation. The new power electronic switch was applied to the active and reactive power coordination optimization. In [51], the flexible distribution switch (FDS) was applied to the active and reactive power coordination optimization to realize the distributed adaptive control of feeder load rate and voltage. To consider the participation of energy storage in active and reactive power coordination optimization, Hong et al. [52] considered power flow constraints, line operation constraints, distributed generation and shunt capacitor constraints, and energy storage charge and discharge constraints to achieve overall optimization.


#### 4.1.4. Reactive Power Optimization of Active Distribution Network Based on Model Predictive Control

Model predictive control is a closed-loop control method based on finite time domain rolling optimization of the predictive model, which can deal with the influence of uncertain factors in the distribution system [53]. The MPC and multi-time scale combined reactive power and voltage optimization have a daily dispatching stage, intraday rolling scheduling stage, and real-time feedback correction stage, and the coordination relationship of each stage is shown in Figure 11.Figure 11
Voltage optimization principle of model predictive control.Applying model predictive control to multi-timescale voltage optimization can greatly improve the control accuracy. Peng et al. [36] applied MPC to multi-timescale voltage optimization of high-proportion wind power connected to the distribution network, which made full use of switching virtual circuit, static var generator, and other reactive power compensation equipment, and suppressed frequent and rapid voltage fluctuation of the distribution network through reactive power and voltage rolling optimization and correction of MPC.To study the influence of renewable distributed generation on distribution network voltage, Hao et al. [54] established the voltage prediction model of each node. Aiming at minimizing the deviation between the predicted voltage and rated voltage of each node, the influence of random fluctuation of renewable distributed generation on voltage is eliminated through model predictive control rolling optimization and feedback correction. Further study on RDG’s participation in power grid voltage regulation was discussed. Xiangwu et al. [53] carried out MPC on distribution network at multiple timescales and fine regulated power grid reactive voltage by coordinating reactive power equipment, rolling regulation of RDG, and load short-term prediction and feedback correction.To consider the influence of time-varying load on power grid voltage regulation, Hongtao et al. [55] established an ADN optimal dispatching model, which used a model predictive control to meet the need of charging users and voltage regulation performance at the same time, reduce power fluctuation, and improve the economy.


#### 4.1.5. Hierarchical and Zonal Control of Reactive Power and Voltage in Active Distribution Network

The centralized optimization of active distribution network has the problems of long optimization time, weak privacy protection in the region, and uncertain source load output. The ADN can be controlled by layers or zones. Figure12 briefly analyzes the principle of reactive power and voltage stratification and the basic principle of reactive power and voltage zoning.Figure 12
Hierarchical and zonal control of reactive power and voltage in active distribution network.For the demand of multilayer information distributed storage and rapid optimization of active distribution network, a hierarchical time-varying optimization tracking algorithm was proposed in the literature [56]. Fei et al. [57] divided the control area into self-made control area and coordinated control area and made full use of the adjustable equipment in the distribution network to adjust the voltage from top to bottom. For hybrid AC/DC distribution networks, Haiping et al. [58] considered the source load uncertainty and aimed to minimize the sum of expected operating costs of prediction scenarios and sampling scenarios, which were decomposed into main and subproblems by the benders decomposition algorithm. The overall optimization is formed between and within regions to achieve global optimization. In [50], for the rational zoning of active distribution network, the distributed accelerated alternating multiplier method was used to solve the subproblems in this region, and the global optimal solution was obtained by iterative optimization.


#### 4.1.6. Participation of Power Electronic Switches in Reactive Power Optimization of Active Distribution Network

A soft open point (SOP) is a fully controlled power electronic device [59], which can accurately control the active power transmitted between the feeders connected on both sides and provide certain reactive power and voltage support. Compared with traditional voltage regulating equipment such as an on-load transformer and capacitor bank, SOP has no limit on the number of actions and has a fast real-time response speed. It is more suitable for scenes with frequent voltage fluctuations. Figure 13 summarizes the advantages of SOP, which not only has efficient voltage regulation capacity but also has a very wide application prospect. However, credited to the high investment and operation cost of SOP, it can not completely replace the traditional voltage regulating equipment. Therefore, the coordination of SOP, tie switch, and traditional voltage regulating equipment should be considered in the reactive power and voltage optimization of active distribution network.Figure 13
Advantages and application of the intelligent soft switch.To study the ability of soft open point to coordinate with traditional voltage regulating equipment to control the voltage of active distribution network, Ruonan et al. [60] optimized the voltage of active distribution network in three stages. In the first stage, the tap position of the on-load voltage regulating transformer and the switching number of the capacitor bank was adjusted. In the second stage, the active power and voltage reactive droop curves of SOP are optimized. In the third stage, the SOP reactive power output is dynamically adjusted in real time. Further considering that SOP and network reconfiguration participate in voltage optimization together, Pengwei et al. [61] established SOP feedback rolling optimization control model based on model predictive control and used mixed-integer second-order cone programming method to solve the coordinated optimization of SOP and network reconfiguration. Yuanquan et al. [51] established a multiperiod active reactive power coordinated optimal operation model considering flexible multistate switches and ADN and fully coordinated the configuration of flexible switches and adjustable factors in active distribution network. From the published research results, it can be seen that there is still much room for improvement in reactive power optimization of ADN through joint coordination between SOP and traditional voltage control equipment.


### 4.2. Methods for Dealing with Uncertainties in Active Distribution Network

Renewable distributed generation and time-varying loads of different properties are connected to the distribution network, and their uncertainty significantly affects the power supply reliability of the distribution network [54]. Common methods to deal with multiple uncertain factors in the distribution system include the scene analysis method, Monte Carlo simulation method, probability clustering method, opportunity constrained programming method, robust optimization method, and time-division optimization method. Table 1 briefly analyzes the advantages and disadvantages of the above treatment methods.Table 1
Summary of treatment methods for uncertain problems.
MethodExisting advantagesPotential deficiencyStochastic optimization [62]Good economy and poor robustnessDiscrete scenes are often of large scale and take a long time to solveRobust optimization [63]Strong robustness and low sensitivity of the optimal solution to parameter changesDifficult to solve directlyDistributionally robust optimization [64]The economy and robustness are good, and the solution time is shortFor the problem of uncertainty in a single time, the research on data correlation between multiple time periods is insufficientScene analysis [65]Avoid complex modelling processes and reduce the amount of calculationThere are errorsMonte Carlo simulation [66]Accurate conclusionLow efficiencyProbabilistic clustering [67]Reduce the amount of calculationComplex modellingChance constrained programming [68]Good applicability and strong robustnessOptimization results are affected by random factorsTime-division optimization [69]Highlight the overall change characteristics of uncertain factorsThe method of time-division may be subjectiveInterval optimization [70]According to the subjective preference of decision-makers, give flexible consideration to optimality and robustnessSlow operation speedFuzzy set theory [71]Strong processing capacityLack of systematicnessRough set theory [72]Without any prior information, the description is objectiveLimitations: it may not be able to fully and effectively describe uncertain problems


### 4.3. Reactive Power Optimization Algorithm

The reactive power and voltage optimization problem of active distribution network is a multidimensional nonlinear problem with multivariable and multiconstraints [73, 74]. How to deal with the nonlinear objective function, improve the convergence of optimization algorithm, and realize accurate control has always been the focus of research.Credited to the high investment and operation cost of SOP, it can not completely replace the traditional voltage regulating equipment. To the rapid development of computer technology and mathematical theory, there are more and more new reactive power optimization algorithms, which can be divided into three categories: traditional optimization algorithms, intelligent optimization algorithms, and hybrid optimization algorithms.Table2 summarizes the advantages and disadvantages of various basic optimization algorithms in detail. With the high proportion of power electronic devices, new energy generation, and flexible loads connected to the distribution network, a single optimization algorithm can no longer meet our expectations for solution speed, accuracy, convergence, and robustness. In the optimization process, the improved intelligent optimization algorithm or hybrid optimization algorithm is usually used to obtain the optimal solution.Table 2
Comparison of advantages and disadvantages of different reactive power optimization algorithms.
ClassificationConditionExisting advantagesPotential deficiencyTraditional optimization algorithmLinear programming methodSensitivity analysis [75]It is convenient to introduce various constraints, and the result is idealThe amount of calculation is large, and there are many requirements for memory and timeInterior point method [76]It has good robustness and convergence, and the calculation time is not affected by the scale of the systemThe results obtained have errors compared with the actual resultsSimplified gradient method [77]The principle is simple, the storage requirement is small, and it is easy to implementWeak convergence performanceNonlinear programming methodNewton method [78]The search direction is reasonable, with second-order convergence speed and a small amount of calculationIt takes time to solve the comparisonQuadratic programming method [79]Good convergence characteristicsIt can only solve the problem whose objective function is quadratic and the constraint condition is linearMixed-integer programming [80]Integer variables and continuous variables can be treated separatelyPoor integrity, complex calculation process, a large amount of calculation, slow convergence, and easy oscillation in the calculation processArtificial intelligence algorithmDynamic programming method [80]The global optimal solution can be obtained without strictly observing the linearity and convexity of the objective function and constraints, and the convergence is good.When there are many variables, the modelling is complex and the calculation speed is slow.Tabu search method [81]The number of iterations is small and the convergence rate is high.The convergence speed and the quality of the final solution are closely related to the appropriate selection of the initial value, and the global search ability is not good.Particle swarm optimization [82]Strong search accuracy and global searchabilitySlow convergenceAnt colony algorithm [73]Strong robustness and optimization abilityThe solution result is related to the parameter setting.Immune genetic algorithm [83]Strong global search ability, strong robustness, and computing powerPoor local optimization ability, slow convergence, and low resolution.Tabu search [84]Strong local development ability, fast convergence speed, and strong universalityThe global development ability is weak, and only the locally optimal solution is obtained.Gray wolf algorithm [85]Strong convergence performance, few parameters, and strong global searchabilityThe accuracy is slightly poor.Whale algorithm [86]Simple structure, few parameters, and strong searchabilityEasy to fall into local extremum and slow convergenceCuckoo algorithm [87]Few parameters, simple operation, easy implementation, random search path optimization, and strong optimization abilityThe step size is generated randomly, which is not conducive to the calculationBee colony algorithm [88]Few control parameters and fast convergence speedThe amount of iterative calculation is large and the scope of application is limited.Ant lion algorithm [89]Few adjustment parameters, high convergence accuracy, and good robustnessThe global search ability is insufficient, and it is easy to fall into local optimization.Bat algorithm [90]High calculation accuracy and efficiency, fast convergence speedThere is local repeated search; the larger the system scale, the more complex the algorithm and the slower the calculation speed.Simulated annealing algorithm [91]The principle is simple and easy to implement.There is local repeated search; the larger the system scale, the more complex the algorithm and the slower the calculation speed.Fish swarm algorithm [92]Good convergence and robustnessThe search efficiency is ordinary.Neural network [93]Good nonlinear characteristics, predictability, guidance, convergence, and flexibilityIt is easy to fall into local optimal solution, which is not conducive to the online and rapid control of the system.Hybrid optimization algorithmTabu search particle swarm optimization [84]Strong optimization ability, high calculation efficiency, and accuracyThe algorithm is complex.Genetic simulated annealing algorithm [91]High calculation accuracy and fast calculation speedWhen the population size is small, it will fall into local optimization.Improved quantum particle swarm optimization [94]Fast convergence speed and strong optimization abilityIt is easy to fall into the problem of premature convergence.Genetic quasi-Newton hybrid algorithm [95]High search efficiency and high precisionConvergence to the extreme value cannot be guaranteed.Particle swarm optimization interior-point hybrid algorithm [96]The global optimization ability and convergence are strong, and the accuracy of the optimal solution is high.Not suitable for large-scale power gridChaotic bat algorithm [97]The model is simple, the control parameters are few, and the convergence speed is fast.It is easy to fall into local optimization and poor population diversity.


## 4.1. Reactive Power and Voltage Optimization Method

For the voltage problems existing in the distribution network, voltage regulation is usually carried out by adjusting the tap of the on-load tap changer (OLTC) or switching slow-moving reactive power regulation resources such as shunt capacity bank (SCB) [32]. This kind of regulation mode is discrete and cannot be adjusted flexibly. As RDG is connected to the distribution network extensively, adjustable resources in the distribution network are gradually diversified, and the ways of reactive power optimization are becoming more and more abundant [33, 34].This section summarizes the latest research progress of reactive power optimization of active distribution network at home and abroad, mainly including the following six aspects: multi-timescale voltage optimization of active distribution network, collaborative optimization of active distribution network reconfiguration and reactive power optimization, active and reactive power coordination optimization of active distribution network, reactive power optimization of active distribution network based on model predictive control, and voltage optimization of active distribution network with power electronic switches.


### 4.1.1. Multi-Timescale Voltage Optimization of Active Distribution Network

Multi-timescale voltage optimization divides voltage optimization into different stages. Different states have different timescales. Generally, it is divided into two stages [35]. The timescale of the first stage is usually 24 hours or 1 hour, which is called the day-ahead optimization. The timescale of the second stage is usually 15 min and 5 min, which is called intraday optimization. The principle of multitimescale voltage optimization is shown in Figure 8.Figure 8
Principle of multi-time scale voltage optimization.In Figure8, in the first stage, based on the power supply and load prediction data, the reactive power access of discrete reactive power compensation devices, on-load tap changer, and other slow-moving equipment in the active distribution network is adjusted in advance so that the active power and reactive power of each node of the distribution network will be in a reasonable range in the next 24 hours or 1 hour. The second stage is to adjust renewable distributed generation output, switching virtual circuit, static var generator, and other reactive power compensation device access and energy storage port output in real-time based on the current power output, load demand, and grid operation status information on the premise of configuring slow motion equipment in the first stage, thus stabilizing the insufficient or out-of-limit reactive voltage caused by RDG output and load demand fluctuation in the next 15 min or 5 min.For the day-ahead optimization and intraday optimization, Peng et al. [36] used the idea of MPC to configure discrete reactive power compensation devices in the day ahead and used continuously intraday power compensation devices to control voltage. Guowei et al. [37] used energy storage for day ahead and intraday voltage optimization of distribution network and realized voltage optimization by limiting the energy storage power range in the day ahead and adjusting the port output in real-time in intraday. Considering the influence of photovoltaic, Wei et al. [38] proposed a multi-time scale optimization control method for photovoltaic grid-connected distributed generation system, which aimed at minimizing the expected value of operation cost and carries out reactive power optimization of the distribution network in long timescale and short timescale. Further considering the influence of load characteristics on voltage, Zhiqiang et al. [39] carried out reactive power optimization in two stages: day-ahead optimization and real-time optimization and added the correction of the day-ahead optimization in the day. At the same time, considering the uncertainty of new energy and flexible load, double timescales (1-hour level and 15-minute level) [40] were proposed, which used the traditional on-load voltage regulating transformer on-load tap changer and shunt capacitor for hour level voltage optimization and used the reactive power output of new energy units for 15-minute level to control the voltage deviation of nodes.


### 4.1.2. Collaborative Optimization of Active Distribution Network Reconfiguration and Reactive Power Optimization

The active distribution network has black start RDG as backup power supply and a large number of flexible loads, including source load that can interact with the distribution network in two directions and adjustable load regulated in one direction by the distribution network [41, 42].Figure9 shows the characteristic analysis of network reconfiguration. The topology of active distribution network is flexible and changeable. A reasonable network reconfiguration scheme can reduce network loss, promote RDG consumption, and improve the economy of the distribution network [43, 44]. Network reconfiguration also provides more possibilities for reactive power and voltage optimization of active distribution network.Figure 9
Network reconfiguration characteristics of active distribution network.Considering the influence of the uncertainty of renewable distributed generation and time-varying load on active distribution network, Haishu et al. [33] proposed a comprehensive optimization model of ADN combining dynamic reconfiguration and reactive power optimization. Ping et al. [45] proposed a two-stage reactive power and voltage optimization method. In the first stage, the reconstruction scheme and the operation state of slow action equipment were determined, and in the second stage, the influence of fast action equipment on RDG random output was adjusted. Dihong [46] introduced renewable distributed generation into the process of distribution network reconfiguration and coordinated the two schemes of network reconfiguration and adjusting RDG injection power to obtain lower network loss and higher voltage quality.With the increase of DC load in the distribution network, the distribution network gradually develops to AC/DC hybrid distribution network. Xu et al. [47] applied network reconfiguration to the AC/DC power grid, which can optimize the power flow distribution of the distribution system and improve the economy. Further considering the temporal and spatial imbalance of the net load of the distribution network, Wang et al. [48] took the light abandonment, load loss, and minimum switching cost as the goal and divided the period according to the load timing characteristics; the multiperiod and multilevel reconfiguration scheme of active distribution network was obtained.


### 4.1.3. Active and Reactive Power Coordination Optimization of Active Distribution Network

Large-timescale renewable distributed generation grid connection makes active power and reactive power coupling of active distribution network strong [49]. In terms of distribution network distributed optimization, relevant research at home and abroad mainly focused on reactive power optimization, and there was less research on active and reactive power coordination optimization. Active power optimization can reduce distribution network loss and improve economic efficiency. Figure 10 analyzes the regulation significance of active power optimization and reactive power optimization to active distribution network from the perspectives of economy and safety and defines the necessity of active and reactive power coordination optimization to active distribution network regulation.Figure 10
Active and reactive power optimization characteristics of active distribution network.To centralize the management and control of multiple types of distributed generators and multiple types of loads in active distribution network, Lingfeng et al. [50] modelled the active and reactive power output characteristics of various schedulable resources in ADN, planned and scheduled the adjustable active and reactive power in the system in real time, improved the consumption level of new energy and reduces voltage deviation. The new power electronic switch was applied to the active and reactive power coordination optimization. In [51], the flexible distribution switch (FDS) was applied to the active and reactive power coordination optimization to realize the distributed adaptive control of feeder load rate and voltage. To consider the participation of energy storage in active and reactive power coordination optimization, Hong et al. [52] considered power flow constraints, line operation constraints, distributed generation and shunt capacitor constraints, and energy storage charge and discharge constraints to achieve overall optimization.


### 4.1.4. Reactive Power Optimization of Active Distribution Network Based on Model Predictive Control

Model predictive control is a closed-loop control method based on finite time domain rolling optimization of the predictive model, which can deal with the influence of uncertain factors in the distribution system [53]. The MPC and multi-time scale combined reactive power and voltage optimization have a daily dispatching stage, intraday rolling scheduling stage, and real-time feedback correction stage, and the coordination relationship of each stage is shown in Figure 11.Figure 11
Voltage optimization principle of model predictive control.Applying model predictive control to multi-timescale voltage optimization can greatly improve the control accuracy. Peng et al. [36] applied MPC to multi-timescale voltage optimization of high-proportion wind power connected to the distribution network, which made full use of switching virtual circuit, static var generator, and other reactive power compensation equipment, and suppressed frequent and rapid voltage fluctuation of the distribution network through reactive power and voltage rolling optimization and correction of MPC.To study the influence of renewable distributed generation on distribution network voltage, Hao et al. [54] established the voltage prediction model of each node. Aiming at minimizing the deviation between the predicted voltage and rated voltage of each node, the influence of random fluctuation of renewable distributed generation on voltage is eliminated through model predictive control rolling optimization and feedback correction. Further study on RDG’s participation in power grid voltage regulation was discussed. Xiangwu et al. [53] carried out MPC on distribution network at multiple timescales and fine regulated power grid reactive voltage by coordinating reactive power equipment, rolling regulation of RDG, and load short-term prediction and feedback correction.To consider the influence of time-varying load on power grid voltage regulation, Hongtao et al. [55] established an ADN optimal dispatching model, which used a model predictive control to meet the need of charging users and voltage regulation performance at the same time, reduce power fluctuation, and improve the economy.


### 4.1.5. Hierarchical and Zonal Control of Reactive Power and Voltage in Active Distribution Network

The centralized optimization of active distribution network has the problems of long optimization time, weak privacy protection in the region, and uncertain source load output. The ADN can be controlled by layers or zones. Figure12 briefly analyzes the principle of reactive power and voltage stratification and the basic principle of reactive power and voltage zoning.Figure 12
Hierarchical and zonal control of reactive power and voltage in active distribution network.For the demand of multilayer information distributed storage and rapid optimization of active distribution network, a hierarchical time-varying optimization tracking algorithm was proposed in the literature [56]. Fei et al. [57] divided the control area into self-made control area and coordinated control area and made full use of the adjustable equipment in the distribution network to adjust the voltage from top to bottom. For hybrid AC/DC distribution networks, Haiping et al. [58] considered the source load uncertainty and aimed to minimize the sum of expected operating costs of prediction scenarios and sampling scenarios, which were decomposed into main and subproblems by the benders decomposition algorithm. The overall optimization is formed between and within regions to achieve global optimization. In [50], for the rational zoning of active distribution network, the distributed accelerated alternating multiplier method was used to solve the subproblems in this region, and the global optimal solution was obtained by iterative optimization.


### 4.1.6. Participation of Power Electronic Switches in Reactive Power Optimization of Active Distribution Network

A soft open point (SOP) is a fully controlled power electronic device [59], which can accurately control the active power transmitted between the feeders connected on both sides and provide certain reactive power and voltage support. Compared with traditional voltage regulating equipment such as an on-load transformer and capacitor bank, SOP has no limit on the number of actions and has a fast real-time response speed. It is more suitable for scenes with frequent voltage fluctuations. Figure 13 summarizes the advantages of SOP, which not only has efficient voltage regulation capacity but also has a very wide application prospect. However, credited to the high investment and operation cost of SOP, it can not completely replace the traditional voltage regulating equipment. Therefore, the coordination of SOP, tie switch, and traditional voltage regulating equipment should be considered in the reactive power and voltage optimization of active distribution network.Figure 13
Advantages and application of the intelligent soft switch.To study the ability of soft open point to coordinate with traditional voltage regulating equipment to control the voltage of active distribution network, Ruonan et al. [60] optimized the voltage of active distribution network in three stages. In the first stage, the tap position of the on-load voltage regulating transformer and the switching number of the capacitor bank was adjusted. In the second stage, the active power and voltage reactive droop curves of SOP are optimized. In the third stage, the SOP reactive power output is dynamically adjusted in real time. Further considering that SOP and network reconfiguration participate in voltage optimization together, Pengwei et al. [61] established SOP feedback rolling optimization control model based on model predictive control and used mixed-integer second-order cone programming method to solve the coordinated optimization of SOP and network reconfiguration. Yuanquan et al. [51] established a multiperiod active reactive power coordinated optimal operation model considering flexible multistate switches and ADN and fully coordinated the configuration of flexible switches and adjustable factors in active distribution network. From the published research results, it can be seen that there is still much room for improvement in reactive power optimization of ADN through joint coordination between SOP and traditional voltage control equipment.


## 4.1.1. Multi-Timescale Voltage Optimization of Active Distribution Network

Multi-timescale voltage optimization divides voltage optimization into different stages. Different states have different timescales. Generally, it is divided into two stages [35]. The timescale of the first stage is usually 24 hours or 1 hour, which is called the day-ahead optimization. The timescale of the second stage is usually 15 min and 5 min, which is called intraday optimization. The principle of multitimescale voltage optimization is shown in Figure 8.Figure 8
Principle of multi-time scale voltage optimization.In Figure8, in the first stage, based on the power supply and load prediction data, the reactive power access of discrete reactive power compensation devices, on-load tap changer, and other slow-moving equipment in the active distribution network is adjusted in advance so that the active power and reactive power of each node of the distribution network will be in a reasonable range in the next 24 hours or 1 hour. The second stage is to adjust renewable distributed generation output, switching virtual circuit, static var generator, and other reactive power compensation device access and energy storage port output in real-time based on the current power output, load demand, and grid operation status information on the premise of configuring slow motion equipment in the first stage, thus stabilizing the insufficient or out-of-limit reactive voltage caused by RDG output and load demand fluctuation in the next 15 min or 5 min.For the day-ahead optimization and intraday optimization, Peng et al. [36] used the idea of MPC to configure discrete reactive power compensation devices in the day ahead and used continuously intraday power compensation devices to control voltage. Guowei et al. [37] used energy storage for day ahead and intraday voltage optimization of distribution network and realized voltage optimization by limiting the energy storage power range in the day ahead and adjusting the port output in real-time in intraday. Considering the influence of photovoltaic, Wei et al. [38] proposed a multi-time scale optimization control method for photovoltaic grid-connected distributed generation system, which aimed at minimizing the expected value of operation cost and carries out reactive power optimization of the distribution network in long timescale and short timescale. Further considering the influence of load characteristics on voltage, Zhiqiang et al. [39] carried out reactive power optimization in two stages: day-ahead optimization and real-time optimization and added the correction of the day-ahead optimization in the day. At the same time, considering the uncertainty of new energy and flexible load, double timescales (1-hour level and 15-minute level) [40] were proposed, which used the traditional on-load voltage regulating transformer on-load tap changer and shunt capacitor for hour level voltage optimization and used the reactive power output of new energy units for 15-minute level to control the voltage deviation of nodes.


## 4.1.2. Collaborative Optimization of Active Distribution Network Reconfiguration and Reactive Power Optimization

The active distribution network has black start RDG as backup power supply and a large number of flexible loads, including source load that can interact with the distribution network in two directions and adjustable load regulated in one direction by the distribution network [41, 42].Figure9 shows the characteristic analysis of network reconfiguration. The topology of active distribution network is flexible and changeable. A reasonable network reconfiguration scheme can reduce network loss, promote RDG consumption, and improve the economy of the distribution network [43, 44]. Network reconfiguration also provides more possibilities for reactive power and voltage optimization of active distribution network.Figure 9
Network reconfiguration characteristics of active distribution network.Considering the influence of the uncertainty of renewable distributed generation and time-varying load on active distribution network, Haishu et al. [33] proposed a comprehensive optimization model of ADN combining dynamic reconfiguration and reactive power optimization. Ping et al. [45] proposed a two-stage reactive power and voltage optimization method. In the first stage, the reconstruction scheme and the operation state of slow action equipment were determined, and in the second stage, the influence of fast action equipment on RDG random output was adjusted. Dihong [46] introduced renewable distributed generation into the process of distribution network reconfiguration and coordinated the two schemes of network reconfiguration and adjusting RDG injection power to obtain lower network loss and higher voltage quality.With the increase of DC load in the distribution network, the distribution network gradually develops to AC/DC hybrid distribution network. Xu et al. [47] applied network reconfiguration to the AC/DC power grid, which can optimize the power flow distribution of the distribution system and improve the economy. Further considering the temporal and spatial imbalance of the net load of the distribution network, Wang et al. [48] took the light abandonment, load loss, and minimum switching cost as the goal and divided the period according to the load timing characteristics; the multiperiod and multilevel reconfiguration scheme of active distribution network was obtained.


## 4.1.3. Active and Reactive Power Coordination Optimization of Active Distribution Network

Large-timescale renewable distributed generation grid connection makes active power and reactive power coupling of active distribution network strong [49]. In terms of distribution network distributed optimization, relevant research at home and abroad mainly focused on reactive power optimization, and there was less research on active and reactive power coordination optimization. Active power optimization can reduce distribution network loss and improve economic efficiency. Figure 10 analyzes the regulation significance of active power optimization and reactive power optimization to active distribution network from the perspectives of economy and safety and defines the necessity of active and reactive power coordination optimization to active distribution network regulation.Figure 10
Active and reactive power optimization characteristics of active distribution network.To centralize the management and control of multiple types of distributed generators and multiple types of loads in active distribution network, Lingfeng et al. [50] modelled the active and reactive power output characteristics of various schedulable resources in ADN, planned and scheduled the adjustable active and reactive power in the system in real time, improved the consumption level of new energy and reduces voltage deviation. The new power electronic switch was applied to the active and reactive power coordination optimization. In [51], the flexible distribution switch (FDS) was applied to the active and reactive power coordination optimization to realize the distributed adaptive control of feeder load rate and voltage. To consider the participation of energy storage in active and reactive power coordination optimization, Hong et al. [52] considered power flow constraints, line operation constraints, distributed generation and shunt capacitor constraints, and energy storage charge and discharge constraints to achieve overall optimization.


## 4.1.4. Reactive Power Optimization of Active Distribution Network Based on Model Predictive Control

Model predictive control is a closed-loop control method based on finite time domain rolling optimization of the predictive model, which can deal with the influence of uncertain factors in the distribution system [53]. The MPC and multi-time scale combined reactive power and voltage optimization have a daily dispatching stage, intraday rolling scheduling stage, and real-time feedback correction stage, and the coordination relationship of each stage is shown in Figure 11.Figure 11
Voltage optimization principle of model predictive control.Applying model predictive control to multi-timescale voltage optimization can greatly improve the control accuracy. Peng et al. [36] applied MPC to multi-timescale voltage optimization of high-proportion wind power connected to the distribution network, which made full use of switching virtual circuit, static var generator, and other reactive power compensation equipment, and suppressed frequent and rapid voltage fluctuation of the distribution network through reactive power and voltage rolling optimization and correction of MPC.To study the influence of renewable distributed generation on distribution network voltage, Hao et al. [54] established the voltage prediction model of each node. Aiming at minimizing the deviation between the predicted voltage and rated voltage of each node, the influence of random fluctuation of renewable distributed generation on voltage is eliminated through model predictive control rolling optimization and feedback correction. Further study on RDG’s participation in power grid voltage regulation was discussed. Xiangwu et al. [53] carried out MPC on distribution network at multiple timescales and fine regulated power grid reactive voltage by coordinating reactive power equipment, rolling regulation of RDG, and load short-term prediction and feedback correction.To consider the influence of time-varying load on power grid voltage regulation, Hongtao et al. [55] established an ADN optimal dispatching model, which used a model predictive control to meet the need of charging users and voltage regulation performance at the same time, reduce power fluctuation, and improve the economy.


## 4.1.5. Hierarchical and Zonal Control of Reactive Power and Voltage in Active Distribution Network

The centralized optimization of active distribution network has the problems of long optimization time, weak privacy protection in the region, and uncertain source load output. The ADN can be controlled by layers or zones. Figure12 briefly analyzes the principle of reactive power and voltage stratification and the basic principle of reactive power and voltage zoning.Figure 12
Hierarchical and zonal control of reactive power and voltage in active distribution network.For the demand of multilayer information distributed storage and rapid optimization of active distribution network, a hierarchical time-varying optimization tracking algorithm was proposed in the literature [56]. Fei et al. [57] divided the control area into self-made control area and coordinated control area and made full use of the adjustable equipment in the distribution network to adjust the voltage from top to bottom. For hybrid AC/DC distribution networks, Haiping et al. [58] considered the source load uncertainty and aimed to minimize the sum of expected operating costs of prediction scenarios and sampling scenarios, which were decomposed into main and subproblems by the benders decomposition algorithm. The overall optimization is formed between and within regions to achieve global optimization. In [50], for the rational zoning of active distribution network, the distributed accelerated alternating multiplier method was used to solve the subproblems in this region, and the global optimal solution was obtained by iterative optimization.


## 4.1.6. Participation of Power Electronic Switches in Reactive Power Optimization of Active Distribution Network

A soft open point (SOP) is a fully controlled power electronic device [59], which can accurately control the active power transmitted between the feeders connected on both sides and provide certain reactive power and voltage support. Compared with traditional voltage regulating equipment such as an on-load transformer and capacitor bank, SOP has no limit on the number of actions and has a fast real-time response speed. It is more suitable for scenes with frequent voltage fluctuations. Figure 13 summarizes the advantages of SOP, which not only has efficient voltage regulation capacity but also has a very wide application prospect. However, credited to the high investment and operation cost of SOP, it can not completely replace the traditional voltage regulating equipment. Therefore, the coordination of SOP, tie switch, and traditional voltage regulating equipment should be considered in the reactive power and voltage optimization of active distribution network.Figure 13
Advantages and application of the intelligent soft switch.To study the ability of soft open point to coordinate with traditional voltage regulating equipment to control the voltage of active distribution network, Ruonan et al. [60] optimized the voltage of active distribution network in three stages. In the first stage, the tap position of the on-load voltage regulating transformer and the switching number of the capacitor bank was adjusted. In the second stage, the active power and voltage reactive droop curves of SOP are optimized. In the third stage, the SOP reactive power output is dynamically adjusted in real time. Further considering that SOP and network reconfiguration participate in voltage optimization together, Pengwei et al. [61] established SOP feedback rolling optimization control model based on model predictive control and used mixed-integer second-order cone programming method to solve the coordinated optimization of SOP and network reconfiguration. Yuanquan et al. [51] established a multiperiod active reactive power coordinated optimal operation model considering flexible multistate switches and ADN and fully coordinated the configuration of flexible switches and adjustable factors in active distribution network. From the published research results, it can be seen that there is still much room for improvement in reactive power optimization of ADN through joint coordination between SOP and traditional voltage control equipment.


## 4.2. Methods for Dealing with Uncertainties in Active Distribution Network

Renewable distributed generation and time-varying loads of different properties are connected to the distribution network, and their uncertainty significantly affects the power supply reliability of the distribution network [54]. Common methods to deal with multiple uncertain factors in the distribution system include the scene analysis method, Monte Carlo simulation method, probability clustering method, opportunity constrained programming method, robust optimization method, and time-division optimization method. Table 1 briefly analyzes the advantages and disadvantages of the above treatment methods.Table 1
Summary of treatment methods for uncertain problems.
MethodExisting advantagesPotential deficiencyStochastic optimization [62]Good economy and poor robustnessDiscrete scenes are often of large scale and take a long time to solveRobust optimization [63]Strong robustness and low sensitivity of the optimal solution to parameter changesDifficult to solve directlyDistributionally robust optimization [64]The economy and robustness are good, and the solution time is shortFor the problem of uncertainty in a single time, the research on data correlation between multiple time periods is insufficientScene analysis [65]Avoid complex modelling processes and reduce the amount of calculationThere are errorsMonte Carlo simulation [66]Accurate conclusionLow efficiencyProbabilistic clustering [67]Reduce the amount of calculationComplex modellingChance constrained programming [68]Good applicability and strong robustnessOptimization results are affected by random factorsTime-division optimization [69]Highlight the overall change characteristics of uncertain factorsThe method of time-division may be subjectiveInterval optimization [70]According to the subjective preference of decision-makers, give flexible consideration to optimality and robustnessSlow operation speedFuzzy set theory [71]Strong processing capacityLack of systematicnessRough set theory [72]Without any prior information, the description is objectiveLimitations: it may not be able to fully and effectively describe uncertain problems


## 4.3. Reactive Power Optimization Algorithm

The reactive power and voltage optimization problem of active distribution network is a multidimensional nonlinear problem with multivariable and multiconstraints [73, 74]. How to deal with the nonlinear objective function, improve the convergence of optimization algorithm, and realize accurate control has always been the focus of research.Credited to the high investment and operation cost of SOP, it can not completely replace the traditional voltage regulating equipment. To the rapid development of computer technology and mathematical theory, there are more and more new reactive power optimization algorithms, which can be divided into three categories: traditional optimization algorithms, intelligent optimization algorithms, and hybrid optimization algorithms.Table2 summarizes the advantages and disadvantages of various basic optimization algorithms in detail. With the high proportion of power electronic devices, new energy generation, and flexible loads connected to the distribution network, a single optimization algorithm can no longer meet our expectations for solution speed, accuracy, convergence, and robustness. In the optimization process, the improved intelligent optimization algorithm or hybrid optimization algorithm is usually used to obtain the optimal solution.Table 2
Comparison of advantages and disadvantages of different reactive power optimization algorithms.
ClassificationConditionExisting advantagesPotential deficiencyTraditional optimization algorithmLinear programming methodSensitivity analysis [75]It is convenient to introduce various constraints, and the result is idealThe amount of calculation is large, and there are many requirements for memory and timeInterior point method [76]It has good robustness and convergence, and the calculation time is not affected by the scale of the systemThe results obtained have errors compared with the actual resultsSimplified gradient method [77]The principle is simple, the storage requirement is small, and it is easy to implementWeak convergence performanceNonlinear programming methodNewton method [78]The search direction is reasonable, with second-order convergence speed and a small amount of calculationIt takes time to solve the comparisonQuadratic programming method [79]Good convergence characteristicsIt can only solve the problem whose objective function is quadratic and the constraint condition is linearMixed-integer programming [80]Integer variables and continuous variables can be treated separatelyPoor integrity, complex calculation process, a large amount of calculation, slow convergence, and easy oscillation in the calculation processArtificial intelligence algorithmDynamic programming method [80]The global optimal solution can be obtained without strictly observing the linearity and convexity of the objective function and constraints, and the convergence is good.When there are many variables, the modelling is complex and the calculation speed is slow.Tabu search method [81]The number of iterations is small and the convergence rate is high.The convergence speed and the quality of the final solution are closely related to the appropriate selection of the initial value, and the global search ability is not good.Particle swarm optimization [82]Strong search accuracy and global searchabilitySlow convergenceAnt colony algorithm [73]Strong robustness and optimization abilityThe solution result is related to the parameter setting.Immune genetic algorithm [83]Strong global search ability, strong robustness, and computing powerPoor local optimization ability, slow convergence, and low resolution.Tabu search [84]Strong local development ability, fast convergence speed, and strong universalityThe global development ability is weak, and only the locally optimal solution is obtained.Gray wolf algorithm [85]Strong convergence performance, few parameters, and strong global searchabilityThe accuracy is slightly poor.Whale algorithm [86]Simple structure, few parameters, and strong searchabilityEasy to fall into local extremum and slow convergenceCuckoo algorithm [87]Few parameters, simple operation, easy implementation, random search path optimization, and strong optimization abilityThe step size is generated randomly, which is not conducive to the calculationBee colony algorithm [88]Few control parameters and fast convergence speedThe amount of iterative calculation is large and the scope of application is limited.Ant lion algorithm [89]Few adjustment parameters, high convergence accuracy, and good robustnessThe global search ability is insufficient, and it is easy to fall into local optimization.Bat algorithm [90]High calculation accuracy and efficiency, fast convergence speedThere is local repeated search; the larger the system scale, the more complex the algorithm and the slower the calculation speed.Simulated annealing algorithm [91]The principle is simple and easy to implement.There is local repeated search; the larger the system scale, the more complex the algorithm and the slower the calculation speed.Fish swarm algorithm [92]Good convergence and robustnessThe search efficiency is ordinary.Neural network [93]Good nonlinear characteristics, predictability, guidance, convergence, and flexibilityIt is easy to fall into local optimal solution, which is not conducive to the online and rapid control of the system.Hybrid optimization algorithmTabu search particle swarm optimization [84]Strong optimization ability, high calculation efficiency, and accuracyThe algorithm is complex.Genetic simulated annealing algorithm [91]High calculation accuracy and fast calculation speedWhen the population size is small, it will fall into local optimization.Improved quantum particle swarm optimization [94]Fast convergence speed and strong optimization abilityIt is easy to fall into the problem of premature convergence.Genetic quasi-Newton hybrid algorithm [95]High search efficiency and high precisionConvergence to the extreme value cannot be guaranteed.Particle swarm optimization interior-point hybrid algorithm [96]The global optimization ability and convergence are strong, and the accuracy of the optimal solution is high.Not suitable for large-scale power gridChaotic bat algorithm [97]The model is simple, the control parameters are few, and the convergence speed is fast.It is easy to fall into local optimization and poor population diversity.


## 5. Prospect of Further Research

To promote the efficient utilization of renewable distributed generation, active distribution network extends the controllable objects to distributed energy including RDG, energy storage, and controllable load. In recent years, the reform of the electricity market and the popularization and application of new technologies such as the Internet, communication technology, and intelligent electricity will bring about a series of new problems as well as advanced problems in the active distribution network.


### 5.1. Multi-Energy Cooperative Reactive Power Optimization Control

The energy Internet is a multi-energy coupling, represented by the integrated energy system, which expands the power network in space, connects different regional power grids, and realizes the cross-regional consumption of different types of new energy in different regions. Dynamic reactive power optimization is the fundamental means to improve the voltage stability of ADN. At present, the contents to be studied include a variety of heterogeneous energy coupling mechanisms and methods, the interaction of multiple heterogeneous energy sources at different time scales, capability evaluation, and practical application of various heterogeneous energy sources in reactive power optimization.


### 5.2. Source Load Supply Demand Interaction to Participate in Reactive Power Optimization

Under the condition of energy Internet, to realize the integration of electric vehicles, intelligent transportation, and smart grid, it is necessary to control its charge and discharge behaviour on two-timescales: vehicle guidance before connecting to the grid and charge and discharge control after connecting to the grid.At present, the research and application of the business model and the market mechanism of the interaction between electric vehicles and the power grid are still in their infancy. How to establish the market mechanism in terms of electricity price, auxiliary service price, renewable energy carbon tax, and so on should be considered. This is of great significance for the interactive voltage optimization between users and power grid supply and demand.


### 5.3. Application of New Power Electronic Equipment in Reactive Power and Voltage Optimal Control of Active Distribution Network

At present, power electronics have shown a diversified development period: the development of devices is mainly full control hybrid module devices and wide bandgap devices. Power electronic devices have been able to meet more application requirements of active distribution network, with more multifunctional converters composed of multiple conversion units and more forms of multiport, multilevel connection, multiflow direction, and multiform power conversion. The application of new power electronic equipment in reactive power optimization is the key point of future research.


### 5.4. Intelligent Reactive Power and Voltage Control of Active Distribution Network

Under the guidance of the concept of the smart grid, the informatization level of the entire power system has been significantly improved. By integrating network, computing, and physical resources, the information physics system makes each module and subsystem in the system work and cooperate so that the resources of the system can be allocated organically and the operation efficiency can be improved.In the future, the energy network structure in China will continue to be optimized. Low carbon and energy conservation are important prerequisites for the development of ADN. Active distribution network will gradually become intelligent with the development of information physical systems. Two aspects need to be studied: one is to use the information physical system technology to realize the data and transparency of energy resources, and the other is to use the data information provided by the information physical system to improve the reactive power and voltage operation level of active distribution network.


## 5.1. Multi-Energy Cooperative Reactive Power Optimization Control

The energy Internet is a multi-energy coupling, represented by the integrated energy system, which expands the power network in space, connects different regional power grids, and realizes the cross-regional consumption of different types of new energy in different regions. Dynamic reactive power optimization is the fundamental means to improve the voltage stability of ADN. At present, the contents to be studied include a variety of heterogeneous energy coupling mechanisms and methods, the interaction of multiple heterogeneous energy sources at different time scales, capability evaluation, and practical application of various heterogeneous energy sources in reactive power optimization.


## 5.2. Source Load Supply Demand Interaction to Participate in Reactive Power Optimization

Under the condition of energy Internet, to realize the integration of electric vehicles, intelligent transportation, and smart grid, it is necessary to control its charge and discharge behaviour on two-timescales: vehicle guidance before connecting to the grid and charge and discharge control after connecting to the grid.At present, the research and application of the business model and the market mechanism of the interaction between electric vehicles and the power grid are still in their infancy. How to establish the market mechanism in terms of electricity price, auxiliary service price, renewable energy carbon tax, and so on should be considered. This is of great significance for the interactive voltage optimization between users and power grid supply and demand.


## 5.3. Application of New Power Electronic Equipment in Reactive Power and Voltage Optimal Control of Active Distribution Network

At present, power electronics have shown a diversified development period: the development of devices is mainly full control hybrid module devices and wide bandgap devices. Power electronic devices have been able to meet more application requirements of active distribution network, with more multifunctional converters composed of multiple conversion units and more forms of multiport, multilevel connection, multiflow direction, and multiform power conversion. The application of new power electronic equipment in reactive power optimization is the key point of future research.


## 5.4. Intelligent Reactive Power and Voltage Control of Active Distribution Network

Under the guidance of the concept of the smart grid, the informatization level of the entire power system has been significantly improved. By integrating network, computing, and physical resources, the information physics system makes each module and subsystem in the system work and cooperate so that the resources of the system can be allocated organically and the operation efficiency can be improved.In the future, the energy network structure in China will continue to be optimized. Low carbon and energy conservation are important prerequisites for the development of ADN. Active distribution network will gradually become intelligent with the development of information physical systems. Two aspects need to be studied: one is to use the information physical system technology to realize the data and transparency of energy resources, and the other is to use the data information provided by the information physical system to improve the reactive power and voltage operation level of active distribution network.


## 6. Conclusion

This paper introduces the characteristics of active distribution network with renewable distributed generation and time-varying load and introduces the principle and application of source load participation in voltage regulation in active distribution network, and it reviews the modelling methods of reactive power and voltage optimization in active distribution network. Summary and analysis of the modelling methods of reactive power optimization are carried out in six aspects: multi-timescale, network reconstruction, active and reactive power coordination optimization, model predictive control, hierarchical zoning of distribution network, and participation of power electronic switches in reactive power regulation. The methods of dealing with uncertain factors in the process of reactive power optimization are briefly described, and the pros and cons of reactive power optimization algorithms are compared and analysed. The development direction of reactive power and voltage control technology in active distribution network in the future is prospected at last.



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*Source: 1196369-2021-11-23.xml*