Fault Analysis Method of Active Distribution Network Under Cloud Edge Architecture

Detection and isolation of faulty line in an active distribution network using intelligent numerical relay

With the gradual increase of distributed energy penetration, the traditional optimization model of distribution network can no longer guarantee the stable and efficient operation of the distribution network. In order to deal with the inevitable uncertainty of distributed energy, a new robust optimal operation method is proposed for active distribution network (ADN) based on the minimum confidence interval of distributed energy Beta distribution in this paper. First, an ADN model is established with second-order cone to include the energy storage device, capacitor bank, static var compensator, on-load tap changer, wind turbine and photovoltaic. Then, the historical data of related distributed energy are analyzed and described by the probability density function, and the minimum confidence interval is obtained by interval searching. Furthermore, via taking this minimum confidence interval as the uncertain interval, a less conservative two-stage robust optimization model is established and solved for ADN. The simulation results for the IEEE 33-bus distribution network have verified that the proposed method can realize a more stable and efficient operation of the distribution network compared with the traditional robust optimization method.

In view of the lack of discussion on the connection mode of double-loop network and the influence on the power supply capacity of distribution network in the current soft open point (SOP) planning method, this paper proposes a four-terminal SOP optimization configuration method for active distribution network considering the influence of power supply capacity. Firstly, the application scenario of four-terminal SOP and its control mode under normal and fault operation of distribution network are discussed. Secondly, a bi-level programming method for four-terminal SOP including planning and operation of active distribution network is proposed. On this basis, a planning model aiming at minimizing the sum of the annual investment and operation cost of the four-terminal SOP, the annual normal operation cost of the distribution network and the annual benefit cost taking into account the variation of power supply capacity is constructed. Thirdly, the hybrid integer nonlinear programming model is solved by combining particle swarm optimization, cone programming and power supply capability evaluation. Finally, an example is given to verify the validity and rationality of the proposed method.

The unprecedented growth of distributed renewable generation is changing the distribution network from passive to active, resulting in issues like reverse power flow, voltage violations, malfunction of protection relays, etc. To ensure the reliable and flawless operation of active distribution networks, an electrical device enabling active network management is necessary, and a hybrid distribution transformer offers a promising solution. This study introduces a novel hybrid transformer topology and multi-mode control strategy to achieve coordinated voltage and reverse power regulation in active distribution networks. The proposed hybrid transformer combines conventional transformer windings with a partially rated SiC-MOSFET-based back-to-back converter, reducing additional investment costs and enhancing system reliability. A multi-mode control strategy is proposed to facilitate the concurrent reverse power control and voltage violation mitigation of the presented hybrid transformer, allowing a smooth transition between the P–Q control mode and the V–f control mode. The control mode switching can be activated manually or autonomously in response to voltage violations or reverse power overloading. The effectiveness of the proposed hybrid transformer configuration and its control mode transition mechanism are examined through comprehensive case studies conducted in the PSCAD/EMTDC environment. The proposed HT design has been confirmed to achieve a voltage regulation range of ±20% of the nominal voltage and effectively regulate bidirectional active power flow within a range of −25% to 25% of the rated power.

The massive accessing of unbalance and nonlinear loads to a power distribution network may cause serious problems on power quality and result in additional power loss and safety risks. Distributed generation (DG) with a converter interface has a flexible adjusting ability and can provide certain unbalance and harmonic compensations to power distribution networks. In this paper, an optimal operation method of an active distribution network (ADN) involving the unbalance and harmonic compensation of a converter was proposed. First, the power regulating characteristics of DG were analyzed. Second, an optimization model was constructed to minimize ADN power loss, sum of harmonic voltage squares, and total unbalance of the negative sequence voltage, and multiple constraints, such as converters and network, were considered in the model. Third, the optimization model was transformed into a semidefinite programming model based on the characteristics of the nonconvex and nonlinear original model, thus assuring global convergence and decreasing difficulties in problem solving. Finally, the validity of the proposed algorithm was verified by using IEEE 33-node and 123-node test systems.

It is a technical difficulty for the safe and economic operation of active distribution network to control and manage distributed generators, distributed energy storage and load flexibly and effectively to eliminate the influence of distributed energy uncertainty on grid. In order to realize the full consumption of a large number of intermittent energy sources in active distribution network, this paper proposes an optimal control strategy for active distribution network considering energy storage system and controllable load. And a multi-objective optimal dispatching model with the lowest active power dispatching cost and the smallest intermittent energy reduction for active distribution network is established. The improved particle swarm optimization algorithm is used to solve the model. Finally, a simulation system is constructed to verify the effectiveness and feasibility of the proposed control strategy.

Distributed energy storage planning in soft open point based active distribution networks incorporating network reconfiguration and DG reactive power capability

Optimization principles play an important role in the planning and operation of active distribution networks (ADNs), which are designed to handle the inrush of distributed generation (DG) resources like renewables and, nowadays, battery energy storage systems (BESSs) and electric vehicles (EVs) [...]

High penetration of distributed renewable energy introduces significant uncertainties to active distribution networks. Optimal control methods accounting for inherent uncertainties are needed to facilitate economic and reliable operation of active distribution networks. This paper proposes a stochastic receding horizon control method based on modified stochastic model predictive control framework to integrate high penetration of distributed generation. Multiple controllable resources are jointly optimized over a finite prediction horizon while ensuring relevant security restrictions. The simplified Z-bus sensitivity for active distribution networks is developed for computationally efficient estimation of system nonlinearity with high accuracy, and is combined with the sequential linear programming to iteratively derive the linear state space model for compensation of cumulative modeling errors. Furthermore, the voltage limitations are reformulated as chance constraints to indicate the probabilistic reliability index of voltage qualification rate, and achieve tradeoffs between cost reduction and voltage regulation. The affine-disturbance feedback control policy is leveraged here to enforce close-loop control performance and analytically transform intractable chance constraints into second-order cone constraints. Comprehensive case studies based on 33-bus and 123-bus distribution systems are carried out to demonstrate the capability and effectiveness of the proposed approach in terms of modeling accuracy, control performance, cost reduction, and method scalability. The proposed approach can effectively enforce voltage regulation against uncertainties with the prescribed probability level. Control costs and constraint violation can be reduced compared with deterministic model predictive control and open-loop control strategies.

In order to meet the high permeability and the access of large scale distributed generation, at present and in the future, active distribution network becomes the trend of smart grid research. This paper introduces the influence of distribution network voltage distribution and stability of distributed energy access based on active distribution network; The reactive power and voltage optimization control method of active power distribution network is summarized from the aspects of optimal voltage regulation measure, voltage regulation measure of power network equipment, reactive power optimal dispatch of power system, and so on. Analysis of the current status of the research, and summarized the existing active distribution network with a distributed power pickup distribution network reactive power and voltage control of the existence of some problems and the insufficiency still some problems that must be solved in the study of reactive power and voltage optimization control of active distribution network are pointed out.

With recent development of technology and management in power market and equipment, more and more distributed generation (DG) is embedded in the distribution network. However the approach of connecting DG in most cases is based on a so-called ‘fit and forget’ policy and the capacity of DG is limited rigidly by distribution system operator to avoid the negative effects of high level penetration. New management technologies have been proposed to handle the integration of DGs in the distribution networks. In this review, the micro grid (MG) was treated as the local control method to coordinate DGs within a small area of distribution network. And the active distribution network (AND) was treated as the global control mechanism to actively manage DGs, MGs and other equipment. The operation framework of ADN was firstly introduced. Then based on the static and dynamic models of DGs and MGs, impacts of DGs and MGs on the ADN are surveyed from power quality, stability to the operation. Finally, the conclusion and suggestion is given in this paper.

With the large-scale integration of high-penetration distributed energy resources and the transformation of terminal loads into prosumer models, the operation of active distribution networks (ADNs) has become increasingly flexible, often leading to frequent bidirectional power flows. These dynamics pose significant challenges for accurate loss calculation in ADNs and the correction of transaction outcomes between generators and consumers under power market environments. Moreover, the distinct operating structures of the spot and bilateral contract markets further increase the complexity of ADN operation and impose significant challenges on the adaptability of loss allocation (LA) mechanisms. To address these issues, this paper proposes an innovative LA method for ADNs based on Virtual Contribution Theory (VCT), applicable to both spot and bilateral contract market scenarios. By constructing virtual contribution matrices—branch-based for the spot market and transaction-based for the bilateral market—the method accurately traces and quantifies the actual contributions of each market participant under different trading models, thereby enabling reasonable, transparent, and fair loss allocation. Additionally, the method eliminates the need for complex matrix inversion required in existing approaches by leveraging basic operations on the virtual contribution matrix, thereby reducing computational complexity and significantly improving computational efficiency. Case studies on IEEE 33 and IEEE 69 systems demonstrate the effectiveness, applicability, and fairness of the proposed approach.

With increasing connection of distributed generations (DGs), the power flow in electric distribution network is no longer unidirectional and the network has become active distribution network. Thus, the connection of DGs in electric distribution networks has created a challenge for distribution network operators due to bidirectional power flow. One of the main technical challenges of an active distribution network is to maintain an acceptable voltage level in the system. This has initiated efforts in using various voltage control strategies to regulate the network voltage profile so that the voltage is maintained at its allowable voltage limits. A number of voltage control methods have been applied to solve voltage control problems associated with the connection of DGs in a distribution system. These voltage control strategies may be classified as decentralized or distributed and centralized or coordinated voltage control. From the literature, a number of coordinated voltage control strategies have been implemented to provide better and faster control to the system. This chapter presents an improved coordinated voltage control method in active electric distribution network by coordinating the three voltage control methods, namely, power factor control, on load tap changer control and generation curtailment control. These voltage control methods are coordinated using fuzzy logic by considering the load bus voltages and DG power as inputs and the voltage control actions as the outputs. The fuzzy logic if-then control rules which are generated to build the fuzzy logic control system are based on the simulations results as well as from the previous works. Results obtained using the fuzzy logic based coordinated voltage control has shown that the voltages are able to be kept within its permissible limits.

To achieve carbon neutrality goals, large amounts of renewable energy sources (RESs) are being integrated into power systems. In particular, high penetration of distributed photovoltaic (PV) makes distribution networks highly stochastic, calling for dynamic distribution network reconfiguration (DNR). Existing DNR approaches can be broadly categorized into model-driven optimization-based methods and learning-based methods, with deep reinforcement learning (DRL) being a representative paradigm for fast online decision-making. Existing DNR models typically belong to mixed-integer linear programming, which requires solution methods such as deep reinforcement learning (DRL). However, existing methods commonly struggle to account for human factors, i.e., the time-varying preferences of distribution network operators in DRL decisions. To this end, this paper proposes a natural language-driven, human-in-the-loop DNR framework, which combines a DRL base policy for hour-level dynamic reconfiguration with a large language model (LLM)-based instruction supervision layer. Based on this human-in-the-loop framework, commands from operators in natural language are translated into online adjustments of safety-screened DRL switching actions. Therefore, the framework demonstrates the fast, model-free decision capability of DRL while providing an explicit and interpretable interface for incorporating temporary and context-dependent operator requirements without retraining. Case studies on IEEE 16-bus and 33-bus distribution networks show that the proposed framework reduces network losses, improves voltage profiles, and limits switching operations. It also achieves markedly higher compliance with operator instructions than a conventional model-based method and a pure DRL baseline. These results highlight a viable path to embedding natural language guidance into the data-driven operation of active distribution networks.

With the rapid development of distributed generations (DGs) and interruptible loads (ILs), distribution network company can actively purchase electricity in market instead of playing as a traditional passive purchaser. This study proposes a stochastic bi‐level model‐based strategic trading model for an active distribution company (ADisCo) which operates the active distribution network (ADN) to maximise its profit in electricity market. Uncertainties pertaining to bidding and offering prices of other market rivals’, the imbalance prices in the balance market and the productions of DGs are considered via stochastic programming. Besides, a linear ADN operation model is proposed to ensure ADN operation security within the stochastic programming model. The proposed model is initially formulated as a stochastic bi‐level model, where the upper‐level problem represents the maximisation of the profit of ADN operator, whereas the lower‐level model represents the maximisation of the social welfare in clearing of market from the perspective of independent system operator. On the basis of the complementarity theory, the proposed model can be transformed into a mixed integer linear programming model. Case studies demonstrate the efficiency and effectiveness of the proposed strategic trading model for an ADisCo with DGs and ILs.