Increasing the use of solar photovoltaic (PV) generation in order to decarbonize the electric energy system results in many challenges. Overvoltage is one of the most common problems in distribution systems with high penetration of solar PV. Utilizing demand-side resources such as residential demand response (RDR) have the potential to alleviate this problem. To increase the solar PV hosting capacity, we propose an RDR based load-shifting scheme that utilizes the interaction between the distribution system operator (DSO) and demand-side resources. We first model a customer utility that consists of the cost of purchasing power, revenue from the subsidy, and discomfort due to load shifting. When an overvoltage problem is expected, DSO issues a local subsidy, and customers in the distribution system move their load in response. An optimization framework that minimizes the additional cost due to the subsidy while keeping the voltages in a prescribed range is proposed. Because of the non-linearity of the power flow analysis, we propose a sub-optimal algorithm to obtain a subsidy, prove the performance gap between the optimal subsidy and the subsidy obtained by the algorithm. A case study shows that the proposed RDR scheme increases the hosting capacity to almost its theoretical limit at a lower cost than the curtailment method.

The conventional volt-VAR control (VVC) in distribution systems has limitations in solving the overvoltage problem caused by massive solar photovoltaic (PV) deployment. As an alternative method, VVC using solar PV smart inverters (PVSIs) has come into the limelight, which can respond quickly and effectively to solve the overvoltage problem by absorbing reactive power. However, the network power loss, that is, the sum of line losses in the distribution network, increases with reactive power. Dynamic distribution network reconfiguration (DNR), which hourly controls the network topology by controlling sectionalizing and tie switches, can also solve the overvoltage problem and reduce network loss by changing the power flow in the network. In this study, to improve the voltage profile and minimize the network power loss, we propose a control scheme that integrates the dynamic DNR with volt-VAR control of PVSIs. The proposed control scheme is practically usable for three reasons: Primarily, the proposed scheme is based on a deep reinforcement learning (DRL) algorithm, which does not require accurate distribution system parameters. Furthermore, we propose the use of a heterogeneous multiagent DRL algorithm to control the switches centrally and PVSIs locally. Finally, a practical communication network in the distribution system is assumed. PVSIs only send their status to the central control center, and there is no communication between the PVSIs. A modified 33-bus distribution test feeder reflecting the system conditions of South Korea is used for the case study. The results of this case study demonstrates that the proposed control scheme effectively improves the voltage profile of the distribution system. In addition, the proposed scheme reduces the total power loss in the distribution system, which is the sum of the network power loss and curtailed energy, owing to the voltage violation of the solar PV output.

Fault classification in power transmission lines is important in distance relaying for identifying the accurate phases implicated in the fault occurrence. Generally, the accuracy of fault classification algorithms is evaluated by simulation data, which shows quite different characteristics from real fault data. Also, most of the previous works on fault classification used a single-stage method such as a rule-based algorithm or machine learning-based algorithm. Because of the diverse characteristics of real fault data, the performance of the single-stage method is limited. To address these issues, this paper proposes a novel two-stage algorithm that combines the strengths of rule-based and machine-learning algorithms to improve the accuracy of real fault data. A case study using real fault data shows that the proposed two-stage algorithm outperforms other conventional single-stage algorithms.

Distribution network reconfiguration (DNR) is a technique that changes the status of sectionalizing and tie switches for various purposes such as loss minimization, voltage profile improvement, load leveling, and hosting capacity increase. Although previous algorithms for DNR show good performance, they still have practical limitations. Most of the algorithms assumed that a central coordinator knows all parameters and/or perfect states in a distribution network. Reinforcement learning which is a model-free optimization technique can be a key way to overcome these limitations. This work proposes a DNR scheme using deep reinforcement learning to minimize power loss defined by the amount of line loss and renewable energy curtailment. We model the DNR problem as a Markov decision process (MDP) problem and apply the reinforcement learning algorithm to solve this problem in real-time. Simulation result using 33-bus radial distribution system shows that the proposed scheme shows similar performance compared to an existing method which uses all information on the distribution network.