You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review of Reactive Power Optimization in Distribution Network 
Chapter Title:  VAR Configuration Optimization 
1. Abstract of Cited Paper (BIB001):  Deploying shunt capacitor banks in distribution systems can effectively reduce the power loss and provide additional benefits for system operation. In practice, the power loss on distribution transformers can account for a considerable portion of the overall loss. This paper proposes a method for optimal placement of capacitor banks to the distribution transformers to reduce power loss. The capacitor bank locations are considered at the low-side of transformers. The net present value (NPV) criterion is adopted to evaluate the cost benefit of the capacitor installation project. First, an explicit formula for directly calculating the power loss of radial distribution systems is derived. Then, the optimal capacitor bank placement is formulated as a mixed-integer programming (MIP) model maximizing the NPV of the project subject to certain constraints. The model is suitable for being solved by commercial MIP packages, and the operational control of the capacitor banks to maximize the power loss reduction can be simply achieved by local automatic switching according to VAR measurements. The proposed method has been practically applied in the Macau distribution system, and the simulation results show that the proposed method is computationally efficient, and a considerable positive NPV can be obtained from the optimal capacitor bank placement. 
2. Abstract of Cited Paper (BIB002):  In this paper, we proposed a combined power loss sensitivity approach to determine the optimal capacitor locations and sizes for power loss reduction and voltage profile improvement in unbalanced radial distribution system (UBRDS). The key objective of Reactive power compensation is to improve the voltage, increase power flow capacity and reduce losses. The proposed sensitivity approach involves two steps: first potential locations for optimal capacitor placement are determined from combined power loss sensitivity; then optimal capacitor sizes are obtained for minimum losses. The existing power loss index (PLI) and index vector based approaches are also applied for comparative analysis. The proposed method is tested on IEEE 13-bus and 25-bus test systems, and the results are comparatively analyzed and discussed. 
3. Abstract of Cited Paper (BIB003):  Abstract In this paper, two new algorithms are implemented to solve optimal placement of capacitors in radial distribution systems in two ways that is, optimal placement of fixed size of capacitor banks (Variable Locations Fixed Capacitor banks-VLFQ) and optimal sizing and placement of capacitors (Variable Locations Variable sizing of Capacitors-VLVQ) for real power loss minimization and network savings maximization. The two bio-inspired algorithms Bat Algorithm (BA) and Cuckoo Search (CS): search for all possible locations in the system along with the different sizes of capacitors, in which the optimal sizes of capacitor are chosen to be standard sizes that are available in the market. To check the feasibility, the proposed algorithms are applied on standard 34 and 85 bus radial distribution systems. And the results are compared with results of other methods like Particle Swarm Optimization (PSO), Harmonic Search (HS), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Teaching Learning Based Optimization (TLBO) and Plant Growth Simulation Algorithm (PGSA), as available in the literature. The proposed approaches are capable of producing high-quality solutions with good performance of convergence. The entire simulation has been developed in MATLAB R2010a software. 
4. Abstract of Cited Paper (BIB004):  Abstract In this paper, Flower Pollination Algorithm (FPA) is proposed for optimal allocations and sizing of capacitors in various distribution systems. First the most candidate buses for installing capacitors are suggested using Loss Sensitivity Factors (LSF). Then the proposed FPA is employed to deduce the locations of capacitors and their sizing from the elected buses. The proposed algorithm is tested on 10, 33 and 69 bus radial distribution systems. The obtained results via the proposed algorithm are compared with others to highlight the benefits of the proposed algorithm in reducing total cost and maximizing the net saving. Moreover, the results are introduced to verify the effectiveness of the proposed algorithm to enhance the voltage profiles for various distribution systems. 
5. Abstract of Cited Paper (BIB005):  Abstract In this paper, an Improved Harmony Algorithm (IHA) is proposed for optimal allocations and sizing of capacitors in various distribution systems. Initially, Power Loss Index (PLI) is introduced to get the highest candidate buses for installing capacitors. Then, the proposed IHA is employed to decide the most optimal locations of capacitors and their sizing from the elected buses by PLI. The objective function is designed to reduce the total cost and losses and consequently, to increase the net saving per year. The proposed algorithm is tested on three different radial distribution systems. The obtained results via the proposed algorithm are compared with others to highlight its benefits. Moreover, the results are introduced to verify the effectiveness of the suggested algorithm under different loading conditions. 
6. Abstract of Cited Paper (BIB006):  Abstract This paper presents an optimization model for volt/var control and energy losses minimization in power distribution networks, considering the presence of distributed generation (DG); it can also be applied to obtain the optimal solution for the allocation of capacitor banks. Unlike usual approaches using nonlinear equations, the model uses a linear objective function and linear constraints, binary and continuous variables. Thus, the optimization problem can be represented as a mixed-integer linear programming (MILP) model, which can be solved through classical optimization techniques. The objective function considers the minimization of: (a) energy losses; (b) voltage violations; (c) acquisition, installation and maintenance costs of capacitors. The solution of the optimization problem provides the location and the rating of the capacitor banks; the solution also indicates the number of automatic capacitors to be connected for each load level, and the best operation point for DGs. The model is validated by comparing the results obtained for four test networks with those obtained through exhaustive enumeration technique and conventional load flow. 
7. Abstract of Cited Paper (BIB007):  Abstract This paper presents a new and comprehensive Objective Function (OF) for capacitor placement in distribution networks. In this study, distribution network management’s viewpoint toward identifying comprehensive OF to maximize the benefit of a distribution company is considered. In addition to considering active power loss cost and capacitor cost, two other important terms, i.e. cost of buying reactive power and voltage drop penalty for maximizing the benefit of distribution companies are considered in the OF. All actual conditions including time varying nature of load, annual load growth, time varying price of active and reactive power, and switchable and fixed capacitor are taken into account based on the reality. The profit derived from the proposed OF is compared with two other common OFs, and it is shown that the benefit achieved from the proposed OF is more than the two other OFs. The proposed OF is validated and tested on radial distribution systems with differing topologies and varying sizes and complexities.

Literature Review:
The research on VAR optimal placement is by choosing the installation location and sizing of VAR compensation device to reduce loss and improve voltage etc. The capacitor bank (CB) is the most commonly used in distribution network for VAR compensation. Thus most research discusses the CB optimal placement problem. In order to reduce power losses or improve voltage profile, the objective of VAR configuration in studies usually is to minimize the power losses or to maximize the improvement of voltage profile BIB003  BIB005 . From a financial point of view, the minimum of total annual cost can also be the objective function, including the total investment and operational costs BIB006 BIB007  . BIB008 based on the real-time changing load, annual growth rate of load and the active-reactive price varying over time establishes a novel objective function of comprehensive costs more according with actual conditions. The constraints of this problem contain power balance constraints, VAR compensation limits, generator output limits and voltage limits etc. Based on Lagrange multiplier method, the bus voltage limits often are added to the objective function as cross-border penalty function, so as to enhance the overall optimal flexibility and avoid the infeasible solutions BIB007 BIB008 . The optimal configuration of VAR compensation device mainly emphasizes the determination of the installation position and the capacity. The main methods include the analysis method, the numerical programming method, the heuristic method and the artificial intelligence approach. For selecting the installation position of CBs, sensitivity analysis method is often applied to choose the candidate bus of VAR compensation device, then the artificial intelligence algorithm is applied to determine the specific installation node and sizing of VAR compensation, which can reduce the search space and improve the computing speed BIB005 BIB006 BIB001  .  takes advantage of the hierarchical clustering method for clustering analysis the sensitivity of compensation nodes in order to find the evenly distributed and effective compensation nodes. Then the compensation location and capacity are selected by the improved ant colony algorithm which the search strategy is modified to targeted search the optimal solution. The optimization of VAR compensation position can also employ the weak node index to determine the potential location of CB for reducing search space and computation time. Due to a single index can't fully reflect the complex operation of power system,  overall utilizes the variety of voltage stability index to determine weak nodes. The fuzzy theory is applied to determine candidate compensation nodes because the segmentation line is fuzzy in practical power system. The system partition method is usually used to reduce the search space of installation buses of CB. In  , the second-precise moment approach is applied to system division in order to avoid the cross-range of the VAR compensation, then the search scope of compensation capacity decreases based on the relevant power technical regulations. The location and rating of CBs can also be determined simultaneously by sensitivity analysis method BIB003 , numerical programming method BIB007 , and artificial intelligence method BIB004 . BIB003 proposes a novel combined power loss sensitivity(CPLS) method to determine the optimal sizing and siting capacitor. Reduction in total power loss and total savings obtained real with CPLS approach is higher compared to index vector and PLI methods. In BIB007 a novel mixed-integer linear programming (MILP) approach is presented to obtain the optimal solution. Since linear constraints can be easily integrated into the proposed MILP model, making the model and the solution much simpler and decreasing computing time. Because the application of Bat and Cuckoo Search (CS) algorithms for optimal capacitor placement problem of distribution system has not been explored in previous works, BIB004 utilizes CS and Bat Algorithms to solve the optimal capacitor allocation problem. The results indicate that CS algorithm with high solution quality and Bat algorithms with very quickly convergence speed can be combined to solve the optimization problem. Since the distribution transformer loss occupies a larger share of power loss in distribution network, BIB002 chooses the low voltage side of distribution transformer as the location of capacitor. Then the compensation position and sizing with best benefit of loss reduction are the optimum by building a mixed integer programming model. And other priority issues should also be considered in the research of VAR compensation optimal allocation. Firstly about whether considering the impact of DG on the VAR optimal placement, BIB007 considers the optimal allocation of CBs with DG. And there is no need to specify DGs as Active Power -Voltage (PV) note or Active Power-Reactive power (PQ) note in addressing the problem of operation or expansion planning of distribution systems, which can provide the different investment solutions to achieve lower total costs. But the stochastic output of distributed power is not considered in BIB007 . Then about the problem of load with uncertain factors in CB optimal placement, many researches are just based on several different loads BIB006 BIB007 , and that the uncertainty model of the load has not been set up while optimizing CB placement. A lot of studies don't consider the effect of three-phase unbalanced load and harmonic factor existing in reality. BIB007 establishes the single phase circuit model supposing the distributed system three-phase load is balanced. But because the model is simple and easily extended to two-phase system or unbalanced load system, it can also deal with the problem of capacitor optimal placement with three-phase unbalanced load. BIB003 based on three-phase unbalanced radial distribution system completes the capacitor optimal placement.  thinks over the impact of mutual coupling, load unbalancing and harmonics on the capacitor placement in distribution system. But only a small scale distribution system is verified, future work should consider the capacitor optimal allocation on a large scale distribution system with time-varying harmonics.