Hierarchical Coordinated Voltage Control of Active Distribution Network and Microgrid Based on Multi-Agent Technology

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.

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 the development of the economy and the improvement of people’s living standards, energy has become an increasingly indispensable substance. The large-scale development and utilization of fossil energy has caused problems of energy crisis and environmental pollution. In order to solve these two problems, it is necessary to develop and utilize renewable energy sources, and access distributed power sources in the distribution network to form an active distribution network. The active distribution network contains a large number of distributed power sources, energy storage units and reactive power compensation equipment. Although it provides more convenience for voltage and reactive power regulation of the distribution network, the intermittent and volatility of renewable energy sources will also be Its voltage has a big impact. In order to solve the voltage dip and overvoltage problem of active distribution network, a new active distribution network voltage control strategy is proposed by using model prediction control technology. Inaccurate system modeling is reduced by establishing active distribution bus voltages, distributed power supplies, and energy storage device prediction models. With the minimum adjustment cost as the control target, the second-order cone relaxation method is used to optimize the action behavior of each control device. Finally, the IEEE33 node simulation example is built on the matlab simulation platform. The results show that the proposed voltage control strategy has good control effect and robustness.

Master-slave strategy based in artificial intelligence for the fault section estimation in active distribution networks and microgrids

Protection schemes are essential in active distribution networks and microgrids’ reliable, efficient, and flexible operation. However, the protection of these networks presents significant challenges due to operational changes, such as variations in topology, distributed energy resources connection/disconnection, and microgrid operating modes, among others. This paper proposes an adaptive protection scheme based on overcurrent devices with several setting groups based on artificial intelligence algorithms. The developed strategy is composed of two stages. In the off-line stage, a clustering technique is employed to group the active distribution network operating scenarios exhibiting similarities. The optimal settings for the protection devices are determined for each set of scenarios. On the other hand, in the on-line stage, the protection strategy’s implementation and operation, considering the active distribution network’s existing communication system, are defined. Furthermore, the approach formulates the overcurrent relay coordination as a mixed-integer non-linear optimization problem, and as a result, the optimal setting of the overcurrent protection devices is obtained. It aims to minimize the operating time, considering the transformers’ thermal limits, fuse operating curves, and overcurrent relay settings. The solution is determined by using an Augmented Lagrangian genetic algorithm. The presented protection scheme is validated on the modified IEEE 34 node test feeder, considering the main operating scenarios of the active distribution networks, such as topology changes, distributed energy resource connection/disconnection, and microgrid operating modes (on-grid and off-grid). The results obtained and its easy implementation indicates the high potential for real-life applications.

Advancements in data-driven voltage control in active distribution networks: A Comprehensive review

Distributed energy resources transform the passive distribution networks into active distribution networks (ADNs). Coordinating the dispatch actions of distributed resources has been studies in the literature, both within an ADN and between an ADN and the transmission system. However, the direct coordination between ADNs interconnected via a physical tie line is a topic boldly under-discussed, despite its practical relevance. In this paper, we consider the problem of coordinating the dispatch actions, including the energy exchange, of two interconnected ADNs, each one integrated with flexible loads (managed by demand response aggregators), energy storage systems, and microturbines (MTs). The bilateral energy trading enables the neighboring ADNs to benefit from the difference in locational marginal prices. The coordination problem is formulated as a robust bi-level program under price uncertainty. At the upper level, the total cost of the ADNs is minimized subject to the uncertainty of electricity market prices and technical constraints of the networks and the resources. At the lower level, the DR aggregators present at each ADN selfishly minimize their own cost. Moreover, the worst case realization of wholesale electricity market prices is considered. The problem is linearized using the Karush–Kuhn–Tucker (KKT) conditions and decomposed using the alternating direction method of multipliers (ADMM). Simulation results verify the convergence behavior of the proposed method and quantify the value of DSO-DSO coordination in the presence of an interconnecting line between the ADNs.

The increased distributed generators (DGs) have exacerbated voltage violations in active distribution networks (ADNs). Local reactive power control of DG inverters can realize a fast response to frequent voltage fluctuations. However, commonly used model-based voltage control depends upon accurate network parameters and entire ADN data, which may cause the sensitive information leakage of ADN and DG behaviors in practical operation. In this article, a cloud-edge collaboration-based local voltage control strategy for DGs is proposed with privacy preservation. First, a local voltage control framework is established based on cloud-edge collaboration, in which a surrogate model is built based on the graph convolutional neural networks to estimate the ADN voltages. By transferring the surrogate model, the edge side can obtain the exact voltage estimation in the local curve tuning process without the authority of the whole ADN data, preserving the network parameters of ADN. Then, the interarea coordination based on federated learning is proposed to realize the parameter updating of DG control curves, which can achieve better voltage control performance. By updating surrogate submodels based on private data distributed across multiple edge devices, federated learning can effectively preserve DG behaviors. Finally, the effectiveness and adaptability of the proposed control strategy are validated using the modified IEEE 33-node system. The proposed local DG control strategy can effectively cope with voltage problems and enhance the adaptability to variations in practical operation states while considering privacy preservation.

Active distribution network is an effective solution to realize the optimizing operation of integrating large-scale distributed energy into distribution network. The intermittency of distributed energy makes the traditional PI control dissatisfying the requirement on the active distribution network control, this paper proposed a method based on model predictive control to apply to the coordination control of active distribution network according to the multi-layer coordination control characteristics of the active distribution network. And uses the dynamic matrix control algorithm to simulate the regional autonomous control in active distribution network with Matlab/Simulink, the simulation results show the effectiveness of the model predictive control method.

Summary A distribution network with distributed generation (DG) is usually called an active distribution network. The voltage quality in an active distribution network tends to degrade because of the bidirectional power flow in the network and the random output characteristic of DG. Existing voltage control methods cannot effectively address this issue. In this article, an improved differential evolution (DE) algorithm–based optimal series compensation voltage control method for an active distribution network is presented. It is assumed that the series compensating voltage is produced flexibly by modern power electronics equipment. The quantitative relationship connecting the node voltage deviation, network power loss and compensating voltage is analysed. The optimal control model, including the power loss and load node voltage, is constructed. Then an improved DE algorithm is proposed to solve the optimal model, which combines the standard DE approach with the simulated annealing and adaptive mutation operator method. The optimal control model can be solved effectively by the improved algorithm. A simulation model is developed based on the IEEE 33 standard distribution network with DG. Simulation results confirm the validity of the optimal control model and the advantages of the improved DE algorithm.

In recent years, the integration of flexible resources into active distribution networks (ADNs) has been significantly enhanced. By coordinating a variety of such resources, the economic efficiency, operational security, and overall stability of ADNs can be improved. In this study, a bi-level planning model is proposed for active distribution networks. The upper-level model aims to minimize the annual comprehensive cost, while the lower-level model focuses on reducing network losses. To solve the upper-level problem, a hybrid whale optimization algorithm (HWOA) is developed. The algorithm integrates adaptive mutation based on Gaussian–Cauchy distributions, a nonlinear cosine-based control strategy, and a dual-population co-evolution mechanism. These enhancements allow HWOA to achieve faster convergence, higher accuracy, and stronger global search capabilities, thereby reducing the risk of falling into local optima. The lower-level problem is addressed using the interior point method due to its nonlinear and continuous nature. The proposed model and algorithm are validated through simulations on the IEEE 33-bus system. The results show that DG consumption increases by 88.77 MWh, network losses decrease by 6.8 MWh, and the total system cost is reduced by CNY 3.62 million over the entire project lifecycle. These improvements contribute to both the economic and operational performance of the ADN. Compared with the polar fox optimization algorithm (PFA), HWOA improves algorithmic efficiency by 18.92%, lowers network loss costs by 6.22%, and reduces the total system costs by 0.71%, demonstrating its superior effectiveness in solving complex bi-level optimization problems in active distribution networks. These findings not only demonstrate the technical efficiency of the proposed method but also contribute to the long-term goals of sustainable energy systems by improving renewable energy utilization, reducing operational losses, and supporting carbon reduction targets in active distribution networks.

In response to the difficulties in integrating high proportion distributed photovoltaic energy storage into active distribution network lines at present, this study proposes a novel multi period reconstruction and loss reduction model for active distribution networks with safety constrained optimal power flow. The method proposed by the research institute could effectively allocate reactive power output based on reactive power capacity, achieving better reactive voltage control. Nodes 2 and 3 with smaller reactive power capacity outputted 0.33Mvar, nodes 27 and 28 with medium capacity outputted 0.71Mvar, and nodes 16 and 17 with larger capacity outputted up to 0.98Mvar. This differentiated reactive power allocation strategy effectively optimized voltage control and ensured the stability of active distribution network lines. In addition, the annual system operating cost of the suggested method was reduced by 167889.33 yuan. From this, the suggested method demonstrates significant benefits in both economic and environmental aspects. It provides a practical and feasible optimization strategy for the high proportion of distributed photovoltaic energy storage connected to active distribution networks, which can promote the transformation of energy structure.

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.

Active distribution network is an effective way to accommodate intermittent renewable energy and a realization of smart grid. There are distributed power resources in active distribution network and the load can be adjusted. The traditional deterministic model is no longer able to evaluate the risk accurately. To deal with the randomness and uncertainty in active distribution network, a hybrid Monte Carlo method (HMC) which has the high-dimension insensitivity of Monte Carlo method and the sampling uniformity of quasi Monte Carlo method is proposed and used to form system state matrix. Stochastic load flow calculation and risk assessment of node overvoltage are carried out in active distribution network based on HMC method. An example is taken to prove that the hybrid-Monte Carlo method can make the calculation results more close to the high precision convergence results under the same calculation times.

The access of distributed generation has grant influences on the power distribution system network structure and as a consequence, changes its failure risk. This article presents a power failure risk evaluation model of power distribution network, which including two aspects of device level and feeder level. In the first place, the risk of the power distribution network containing distributed generation will be evaluated from two aspects, device failure possibility and device failure severity. Basing on varies devices' evaluation, through the comprehensive evaluation method to quantify the feeder's risk of failure. When the risk exceeds the threshold value, the system would start its safety precaution. Numerical example results show that, the evaluation method that this article established can realize the risk evaluation and early warning of the power distribution network containing distributed generation, the results have guiding significance for construction retrofitting, and hastens the optimal operation for active power distribution network.