A Review of Reactive Power Optimization in Distribution Network <s> Dynamic VAR Optimization <s> An integrated algorithmic and heuristic technique for transition-optimised voltage and reactive power control is presented. The method seeks to provide a 'transition-optimised' voltage and reactive-power control method. Essentially it considers voltage and reactive power control as a time-based scheduling problem, with the intention of avoiding unnecessary changes in status and output of reactive control plant. The demand profile is classified adaptively using a heuristic iteration method through evaluating the standard deviation and the range of the sequence of load-demand points. Furthermore, an algorithmic transition-optimised model of the volt/VAr control is then presented based on demand-pattern classification. The solution algorithm and different control strategies are studied in the transition optimisation. Case studies illustrate that the integrated algorithmic and heuristic technique for transition-optimised voltage and reactive power control not only effectively reduces power losses, but also greatly simplifies control operations. <s> BIB001 </s> A Review of Reactive Power Optimization in Distribution Network <s> Dynamic VAR Optimization <s> This paper considers reactive power of PV station as reactive power optimization control variables, and discusses the reactive power optimization problem in distribution power system with photovoltaic power station. Due to the randomness output of photovoltaic power station, the voltage of distribution network with photovoltaic power station distribution also shows randomness, as a result, the constraint of the determined voltage in traditional reactive power optimization model is inaccurate. For the randomness of the output of PV power plant, we use random flow algorithm to obtain the probability distribution of each node voltage, put chance constraints on the voltage, take the minimum of power loss as the objective function and then build mathematical model of reactive power optimization for distribution network connected with PV power plant. Using the above reactive power optimization mathematical model based on random constraints, we optimize an example by using the stardard particle swarm algorithm. The optimization results verify the accuracy and the effectiveness of the model and algorithm. <s> BIB002 </s> A Review of Reactive Power Optimization in Distribution Network <s> Dynamic VAR Optimization <s> With the increasing of installed capacity, wind farm that incorporates into power network has great influence on power system. The uncertainty analysis of wind farm incorporating into power network becomes more important. Firstly, the SRSM is applied to the uncertainty analysis. A probabilistic load flow model considering the wind farm is built with SRSM. Secondly, combining the AMDPSO with the probabilistic load flow model, a multi-objective reactive power optimization model considering the expectation value of active power losses and bus voltage beyond limits probability is built. Then, the simulation is conducted for IEEE-14 bus system integrated with wind farms. And the paper calculates bus voltage cumulative distribution by SRSM and Monte Carlo. The results show that SRSM has a high precision and efficiency. At last, a multi-objective reactive power optimization model applied to IEEE-14 bus system is simulated. The experiment proves that AMDPSO can avoid stagnation effectively compared with IWPSO. <s> BIB003
Dynamic VAR optimization represents the VAR optimization over period of time with constraints of the equipment operating times in order to acquire the full-time dispatching state with minimum energy loss in the entire duration  . Considering dynamic VAR optimization based on period of time, the objective function is often the minimum of energy loss during the scheduling period  BIB001 . Due to the constraint of the number of device operations, the objective function also includes the minimization of the device operating times    . The active power loss expectations usually are the optimization objective as well considering the random output of DG among the VAR optimization BIB002  BIB003 . About the constraints, dynamic VAR optimization should consider the sequential operational restriction of control variables except the conventional constraints  . Due to the time-varying load and the constraints of the number of device operation, the dynamic optimization has the characteristic of strong space-time couple leading to its solving difficulty far beyond static VAR optimization.  utilizes a Fisher ordered clustering algorithm to segment a load curve, and then uses a two-phase optimization method based on a modified genetic algorithm and a quasi-dynamic programming to obtain the all-day operation scheme for realizing a multi-period coordinated optimization. BIB001 suggests one time period can be divided into N time intervals. The discrete variables based on the optimization using the average load of whole time interval are only set at the start time of each interval. And under the different load of each interval the transition optimisation can be fulfilled incrementally to keep constant over the whole interval. At last the conventional VAR optimization method can be directly applied to solve the optimization problem.  controls the number of equipment operations based on the changed load to avoid the equipment operating frequently and employs improved multi-species ant colony ant algorithm to complete multi-objective dynamic VAR optimization.  transforms one day of dynamic optimization into 24 time sections of static optimization, then presents the two-stages method, which consists of heuristic search and variable correction to achieve the optimal operating results of devices, via the parallel computing to obtain the optimization results quickly. In  , the pre-optimization is made for each time period separately. A multi-objective function made up of sensitivity, the cost of equipment operation and the number of equipment operation etc is the optimal objective in real-time control. Due to the regulation frequently of device, the inertia factors are introduced to avoid frequent operation. Furthermore the devices are dispatched reasonable to each interval of scheduling period for achieving the comprehensive optimization of the system voltage, the power loss, the equipment life and the number of operations. In the research of dynamic VAR optimization considering random DG output, the light intensity of solar power often is described as Beta distribution BIB002  . And the wind speed of wind power generally submits to Weibull distribution  BIB003 . Moreover the load fluctuation tends to be expressed as a normal distribution  . As for the influence caused by the uncertainty of distributed power output and load fluctuation,  investigates a VAR optimization approach based on interval arithmetic. According to the interval range of the uncertainty value, the proposed method can make the grid work in a near-optimal state considering the uncertainty of grid. Concerning the situation of three-phase unbalanced load in real distribution system,  set up a three-phase power flow model based on the three-phase radial distribution system and applies the second-order cone relaxation technique to convexify the power flow equations for realizing the active and reactive power coordinated multi-period optimization in three-phase unbalance active distribution network.