Research on Active Distribution Network Power Flow Optimization Method Based on Energy Storage Regulation

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.

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

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.

The large-scale integration of distributed photovoltaics (PV) can cause reverse power flow due to an imbalance between load and PV generation, altering the voltage profile in traditional distribution networks. The uncertainties in PV output can also cause voltage violations in the distribution network. Conventional methods for assessing static stability in voltage within such systems utilize continuous power flow techniques to calculate the load margin index, which provides a measure of how stable the voltage is in the distribution network. Nevertheless, the frequent load margin calculations in such methods result in significant computational costs, rendering them impractical for networks with distributed generators (DGs). Conventional static voltage stability assessment methods typically use continuation power flow (CPF) to calculate load margins but face significant computational bottlenecks in active distribution networks with distributed generation. For instance, even with improved power flow algorithms, a 33-bus system requires 58 iterations and about 0.3–0.5 seconds per analysis; in large-scale systems with tens of thousands of buses, total evaluation time can exceed 10 minutes. This paper introduces the holomorphic embedding load flow method (HELM), which accommodates different bus types (PI, PQ, PV) in active distribution networks. Building on HELM, a voltage partial sensitivity method is proposed, providing a criterion for evaluating the voltage stability in distribution networks. The sensitivity-based stability criteria using holomorphic embedding functions consider various DG operating conditions without requiring maximum load margin computation. The efficient calculation of these indexes makes the approach ideal for managing voltage in systems that include distribution networks with DGs. Finally, these indexes are used to manage voltage in active distribution networks, and the effectiveness of the method is confirmed through comprehensive numerical examples.

This paper mainly investigates risk assessment of voltage limit violation in active distribution network with integration of wind generation (WG), photovoltaic generation (PVG) and electric vehicles (EVs). Firstly, to avoid additional peak load caused by random EV charging, a controlled EV charging and discharging strategy is designed. Then, for the correlations of spatially near WGs and PVGs, Nataf transformation and orthogonal transformation (OT) are integrated to solve the problem, and this provides a path for point estimate method (PEM) based probabilistic load flow (PLF) to obtain steady-state voltage of active distribution network. Furthermore, based on the voltage results, a model for quantifying the risk of voltage limit violation is developed by considering loss of load caused by voltage limit violation, which is different from the previous risk indices calculated by possibility and severity of voltage limit violation. Finally, the proposed model is tested on the modified IEEE 33-bus system. Simulation results confirm that the effective EV charging/discharging strategies and penetration increment of WG and PVG help to decrease operation risk of active distribution network.

With the increasing share of uncertain renewable energy sources (RES) generation, it has become increasingly crucial to arrange the output of energy storage reasonably to suppress power fluctuation and ensure low-carbon and economic operation of the distribution network. In this regard, a low-carbon optimal dispatch method in active distribution network (ADN), which includes an energy storage coordination strategy and a ladder carbon trading mechanism, is proposed in this paper. The energy storage coordination strategy can schedule centralized and distributed energy storage resources according to their differences in capacity and response speed. The distributed energy storage is used as power-type energy storage to smooth out the rapid power fluctuation of RES output, while the centralized energy storage is used as energy-type energy storage to take part in the peak shaving of ADN. The ladder carbon trading mechanism can mobilize the use of energy storage in carbon reduction. By setting several carbon emission intervals, it can stimulate the output of distributed energy with different price signals to limit carbon emissions. Then, an optimization model aiming at the minimum sum of the operation cost and carbon trading cost of ADN is established and provides the optimal scheduling strategy. A case study on a modified IEEE 33-bus system verified the effectiveness of the proposed method in stabilizing power fluctuation, reducing carbon emissions, and improving the economy of system operation.

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.

Active Distribution Networks (ADNs) are modern and intelligent power distribution systems that integrate renewable energy sources and advanced control technologies to provide reliable, secure, and efficient electricity supply. The traditional power distribution networks are designed to deliver electricity in a one-way flow from the transmission system to the customers. In contrast, ADN enables two-way power flow between the distribution network and customers, allowing them to become active participants in the electricity system. The key technology behind ADN includes advanced metering infrastructure (AMI), Distribution Automation (DA), and energy storage systems (ESS). AMI provides real-time data on electricity consumption, generation, and distribution, enabling efficient load management and demand response. Other key technologies of ADN include renewable energy sources such as solar photovoltaic (PV), Wind turbines and small-scale hydroelectricity. These sources are connected to the distribution network through power electronic interfaces that convert the DC output of the renewable sources into AC for distribution. Smart grid technologies such as microgrids, virtual power plants, and energy management systems are also integrated into ADN to improve grid stability and resilience. ADNs have the potential to transform the electricity system by enabling more efficient, sustainable, and reliable energy systems that can better serve the needs of consumers and society. Thus, ADN is expected to become a critical technology in achieving a cleaner and more sustainable energy future.

Dynamic behaviors of distribution networks are of great importance for the power system analysis. Nowadays, due to the integration of the renewable energy generation, energy storage, plug-in electric vehicles, and distribution networks turn from passive systems to active ones. Hence, the dynamic behaviors of active distribution networks (ADNs) are much more complex than the traditional ones. The research interests how to establish an accurate model of ADNs in modern power systems are drawing a great deal of attention. In this paper, motivated by the similarities between power system differential algebraic equations and the forward calculation flows of recurrent neural networks (RNNs), a long short-term memory (LSTM) RNN-based equivalent model is proposed to accurately represent the ADNs. First, the adoption reasons of the proposed LSTM RNN-based equivalent model are explained, and its advantages are analyzed from the mathematical point of view. Then, the accuracy and generalization performance of the proposed model is evaluated using the IEEE 39-Bus New England system integrated with ADNs in the study cases. It reveals that the proposed LSTM RNN-based equivalent model has a generalization capability to capture the dynamic behaviors of ADNs with high accuracy.

In the active distribution networks (ADNs), the emerging trend of high penetration of sustainable energy sources such as wind turbines, photovoltaics, and the forthcoming integration of energy storage devices and additional dynamic loads like plug‐in‐electric vehicles (PEVs) influences to change the nature of electric distribution networks. Consequently, it deteriorates power quality and reliability in terms of unacceptable voltage rise‐drop, harmonic distortion, voltage unbalance, and excessive power loss. In this context of transition into the active distribution network, the probabilistic optimal power flow (P‐OPF) in the ADN is considered an important tool that helps the distribution grid operators for defining the optimal settings of the system's control variables. However, the optimal solution of P‐OPF becomes highly complex while considering the multiple variables, time‐varying characteristics and operational uncertainties associated with sustainable resources, consumer behaviour and electricity market prices.This review aims at promoting the research and discussion of P‐OPF studies in the context of ADNs characteristics and challenges. First, we perform a data‐driven and visualized scientometric analysis of 1988 quality documents retrieved from the Scopus during the last ten years using VOSviewer software. Second, an in‐depth investigation of the recent and emerging techniques for solving the P‐OPF problem is presented. Third, a set of recommendations and highlights of future challenges for P‐OPF in ADNs are offered. The review findings showed high interest among the scientific community in sustainable energy source integration, given the recent rise in publications and citations. Also, there is still a lack of innovative optimization techniques that can adequately handle the current issues associated with P‐OPF and objectives with fair computational efficiency and accuracy. Finally, this paper offers a guide for P‐OPF researchers to navigate the recent studies considering the sustainable sources, spot the new research frontiers, and identify the most impactful countries and publishers.

This article proposes a hierarchical distributed voltage control (HDVC) scheme for active distribution networks (ADNs) with high penetration of photovoltaics based on distributed model predictive control (DMPC) and alternating direction method of multipliers (ADMM). The reactive power outputs of several photovoltaic clusters (PVCs) and photovoltaic (PV) units within each PVC are optimally coordinated to keep PV terminal voltages and the voltages of all critical buses of ADNs within the feasible range and mitigate voltage fluctuations. In the ADN layer, a distributed reactive power control scheme based on DMPC is designed for the PVC, which regulates the voltages of all critical buses to be closed to the rated value and mitigates the reactive power variations. In the PVC layer, the reactive power outputs of PV units are optimized based on ADMM to minimize the voltage deviation of each PV terminal and track the reactive power reference from the PVC control. The proposed HDVC scheme requires communication only between neighboring PVC controller, while each PV controller only communicates with the corresponding PVC controller. This regulates the voltages in a completely decentralized manner and effectively reduces the computation burden of the PVC and PV controllers. A modified Finnish distribution network with 10 PVCs was used to validate the control performance of the proposed HDVC scheme.

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.

The constraints in the active - reactive power coordinated optimal scheduling of the conventional active distribution network are single - faceted, the scheduling efficiency is low, resulting in an increase in the final network loss ratio. Therefore, a method for designing and analyzing the active - reactive power coordinated optimal scheduling in the active distribution network with consideration of demand response is proposed. In light of the existing scheduling requirements, an analysis of the active - reactive power scheduling requirements is initially conducted, and the adaptive approach is adopted to improve the overall scheduling efficiency and construct the adaptive balance scheduling constraints. Based on this, a demand - response - based active - reactive power coordinated optimal scheduling model for the active distribution network is established, and optimization and iteration are used to achieve optimal scheduling processing. The test results indicate that, in comparison with the contract - coordinated distribution network active - reactive power coordinated optimal scheduling method and the multi - level scale congested distribution network method, the test network loss ratios of the demand - response - based distribution network active - reactive power coordinated optimal scheduling method are kept below 2.5. This demonstrates that the active - reactive power coordination optimization scheduling method for the active distribution network is more practical and reliable, with better practical application effect and high scheduling efficiency, and has practical application value.

With the high proportion of renewable energy access to the distribution network, the volatility and randomness of renewable energy output will have a serious impact on the distribution network, especially at the peak of renewable power output, the reverse trend phenomenon is obvious. The active distribution network displays the “power” characteristics externally, which makes the voltage problem and transmission congestion problem in the distribution network very serious. This paper establishes a distributed energy storage optimization configuration model that takes into account the voltage quality, and considers the energy storage operation strategy optimization in the energy storage site selection and volume model, which makes the energy storage configuration more reasonable, improves the voltage quality and enhances the renewable energy consumption capacity, improve the economics of system operation.