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:  Static VAR Optimization 
1. Abstract of Cited Paper (BIB001):  In the distribution management system (DMS), an essential application for the energy efficiency improvement is loss reduction. Loss reduction often involves the control of reactive power (var) resources to optimize the var flow in the network. Traditionally, most available var resources in the distribution network have been switchable shunt capacitor banks. Today distributed energy resources (DER) are becoming another significant var resource due to their increasing penetration, which brings new opportunities as well as challenges for loss reduction. This paper proposes an advanced loss reduction approach to achieve the optimal control coordination among multiple capacitors and DERs. The proposed approach and solution are developed on the basis of the detailed multi-phase distribution network modeling and the state-of-the-art optimization technology. This work also investigates the impact of the DER var control on the loss reduction improvement and voltage violation correction. The effectiveness of the proposed approach is demonstrated on practical utility distribution circuits with varying degree of unbalance and model complexity. The performance of the solution method proves the capability of online solution speed for large and complex distribution systems. 
2. Abstract of Cited Paper (BIB002):  This paper presents a fully distributed reactive power optimization algorithm that can obtain the global optimum solution of nonconvex problems for distribution networks (DNs) without requiring a central coordinator. Second-order conic relaxation is used to achieve exact convexification. A fully distributed second-order cone programming solver (D-SOCP) is formulated corresponding to the given division of areas based on the alternating direction method of multipliers (ADMM) algorithm, which is greatly simplified by exploiting the structure of active DNs. The problem is solved for each area with very little interchange of boundary information between neighboring areas. D-SOCP is extended by using a varying penalty parameter to improve convergence. A proof of its convergence is also given. The effectiveness of the method is demonstrated via numerical simulations using the IEEE 69-bus, 123-bus DNs, and a real 1066-bus distribution system.

Literature Review:
Static VAR optimization usually refers to the VAR optimization under the system operating state of a certain time, considering the voltage and other constraints to minimize the power loss  BIB002 BIB003 BIB001  . The violation of voltage limits is often added to the objective function as a penalty function  BIB002 . The VAR of generator and compensation equipment can also be a penalty function  . The optimization algorithm mainly covers numerical programming methods    and artificial intelligence methods     .  proposes a practical algorithm combining nonlinear interior point method and a heuristic parameter to solve the optimization problem.  applies a mixed integer interior-point method to reach fast convergence speed.  presents an improved niche genetic algorithm to enhance the ability of global searching.  employs an improved PSO algorithm to reach better convergence accuracy and computing efficiency. For the problems of nonlinear, non-convex, discrete and a large number of VAR optimization control variables, a lot of researches propose the relevant solutions. First, about the problem of non-convex and nonlinear programming difficult to solve, the second-order conic relaxation is used to achieve exact convexification with feasible solutions BIB003 BIB001 . As for the discontinuous problem of VAR optimization with discrete control variables,  presents a strategy of continuous treatment on discrete variables according to Gaussian penalty function. Then the penalty function added to the objective function makes the discrete variables be the continuous variables, which has better effect than directly integerize. In view of the defects of the complexity of optimization and poor computational efficiency caused by large-scale control variables of VAR optimization, plenty of scholars have done the relative research.  employs the new method based on cooperative coevolutionary approach for decomposing the VAR optimization into a series of interconnected sub-optimization problems, via the coordinated-parallel computing to reduce the calculation time so as to solve massive VAR optimization problems.  proposes a parallel decomposition-coordination algorithm based on the thought of block coordinate descent for settling the VAR optimization of the whole grid. The implemented approach just requires to exchange power and voltage information of boundary nodes between adjacent sub-areas, which can avoid slow computing speed and bottleneck of data transmission in large scale power grid centralized VAR optimization.  introduces a sub-region particle swarm optimization to avoid falling into the local optimum of PSO. The search space is divided into several sub-regions and the optimum of each region obtained by PSO would be compared to gain the global optimum. There are still other problems in static VAR optimization research. About the condition of unbalanced three-phase load in practical distribution network, BIB002 establishes a multiphase model for reflecting the unbalance in real distribution network, which makes losses calculation more accurate and the VAR optimization control more conform to reality. BIB001 develops the VAR optimization model of three-phase distribution network for settling the VAR optimization problem of three-phase asymmetrical large scale system. About the VAR optimization in the presence of DGs,  considers the DG, capacitor group and under-load tap changing transformer simultaneously to implement VAR optimization not yet including the randomness of DG output.