# An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation
## 1 Introduction  
Reactive power optimization aims to produce optimal reactive power-compensation device control strategies in order to achieve minimum delivery losses and at the same time to satisfy specific operating constraints such as requirements for voltage deviations [1, 2]. The problem in essence is a mixed-integer non-linear programming problem. Various optimization methods have been applied to address this issue, including linear programming [3], non-linear programming [4], quadratic programming [5], interior point programming [6, 7], and artificial intelligence (AI) methods [2, 8, 9]. Reference [10] used second-order cones to relax the non-convex power flow equations, and then applied a sensitivity-based decomposition method to improve computational efficiency. Reference [11] transformed non-linear power flow equations into a linear approximation form, and used iterative solving algorithm to obtain optimal reactive power dispatch strategy. Reference [12] implemented genetic algorithm to maintain voltage stability of the power system and also to reduce power losses in systems with mass distributed generators. The aforementioned research provided solutions for stable reactive power optimization, which optimizes based on certain load level at single time stamp. However, since power generation and loads are constantly variable, the optimal strategies for devices like voltage regulators or switchable capacitor banks may not be the same at different time stamps. Due to lifetime limitations of these devices, it violates the economic rule if the devices are adjusted too frequently. Therefore, dynamic reactive poweroptimization is of importance, which optimizes for a time period and makes the adjustment times of devices one of the optimization objectives. Dynamic reactive power optimization is a large-scale multi-period mix-integer non-linear programming problem. The classical methods such as non-linear programming [4] require differentiable objective function and are thus not applicable for dynamic optimization. A number of attempts have been made to conquer the problem of dynamic optimization. Reference [13] made daily reactive power optimization considering network losses, adjustment times of on-load tap changer (OLTC) and switchable capacitor banks, using an improved multi-population ant colony algorithm. Reference [14] used mixed integer non-linear programming to optimize transmission losses only, considering constraints of reactive power equipment operation limits and power grid security. Using a filter collaborative state transition algorithm (FCSTA), reference [15] built a two-objective dynamic reactive power optimization model, with one objective to minimize the actual power losses and voltage deviations, the other to minimize incremental system loss compared with the last time stamp. The objectives can be exchanged during dynamic optimization. Generally, state-of-the-art attempts include reducing scales of objectives or constraints, optimizing for increments at continuous time stamp, and using AI methods. However, much computational burden will be introduced when using AI methodology. Aiming to maintain objectives and constraints, as well as to reduce computational complexity, this paper proposes to segment load curves in a duty cycle into several stable time series first, and then conduct optimization for each segment. Based on online load predictions, the method can be applied in an online mode. Since the whole time series of loads need to be segmented into parts with lowest inner deviation, the methods of wind ramp extraction [16; 17] are utilized to extract data points corresponding to big changes. The data points are afterwards used for segmentation. This paper is organized as follows. The proposed load curve segmentation-based reactive power optimization is described in Section 2. In Section 3, two case studies, one based on offline load-curve measurements, the other based on online load predictions, are presented to evaluate the effectiveness of the proposed method.