Active Network Management Research Articles

PMU-based voltage estimation and distributed generation effects in active distribution networks

The integration of distributed generation (DG) and phasor measuring units (PMUs) has significantly impacted electrical distribution networks. This study focuses on real-time control of renewable energy intermittency and strategic PMU deployment. By optimizing the placement of PMUs and DGs, the study aims to achieve several goals: maximize power loss reduction, increase DG penetration, and maintain acceptable voltage profiles. The recommended PMU-based approach incorporates the optimal number of DGs into the distribution network for load flow analysis. Testing this technique on 12-bus, 33-bus, and 69-bus networks yielded substantial gains. For example, power loss was 81.044 % lower in the 33-bus system and 79.256 % lower in the 69-bus test system compared to the base-case scenario. We also compared these results with other methods found in the literature. The developed algorithm is recommended for application in a real electrical power distribution network for more efficient integration of PMUs and new distributed generation units. Integrating PMUs with DG benefits active distribution network management, enabling proactive grid management and stability.

Strategic Enhancement and Differential Governance: China’s Partnership Diplomacy in Latin America

This paper studies China’s partnership diplomacy and its practice in Latin America and the Caribbean (LAC). China has constructed an all-round, multi-level, and three-dimensional partnership network in LAC through three stages: strategic exploration, development, and improvement. China has implemented differential governance towards its partners, which enables active shaping and dynamic management of the partnership network. This paper categorizes the 15 partner countries into three types: pivot countries, link countries, and node countries based on their political friendliness with China, geopolitical influence, and potential for economic and trade cooperation. China’s partnerships in LAC are vital support for implementing its diplomatic strategy in the region. However, challenges to this strategy’s development include insufficient mutual support from other diplomatic means, doubts and misinterpretations of the China-Latin America partnership by some Latin American countries, and the political and identity shifts within these countries. To enhance the role of partnerships in maintaining state relations and promoting the implementation of diplomatic strategies, China has introduced new content to its partnership diplomacy in LAC.

Assessment of the Technical Impacts of Electric Vehicle Penetration in Distribution Networks: A Focus on System Management Strategies Integrating Sustainable Local Energy Communities

Aligned with the objectives of the energy transition, the increased penetration levels of electric vehicles as part of the electrification of economy, especially within the framework of local energy communities and distributed energy resources, are crucial in shaping sustainable and decentralized energy systems. This work aims to assess the impact of escalating electric vehicles’ deployment on sustainable local energy community-based low-voltage distribution networks. Through comparative analyses across various levels of electric vehicle integration, employing different charging strategies and system management approaches, the research highlights the critical role of active system management instruments such as smart grid monitoring and active network management tools, which significantly enhance the proactive management capabilities of distribution system operators. The findings demonstrate that increased electric vehicle penetration rates intensify load violations, which strategic electric vehicle charging management can significantly mitigate, underscoring the necessity of load management strategies in alleviating grid stress in the context assessed. This study highlights the enhanced outcomes derived from active system management strategies which foster collaboration among distribution system operators, demand aggregators, and local energy communities’ managers within a local flexibility market framework. The results of the analysis illustrate that this proactive and cooperative approach boosts system flexibility and effectively averts severe grid events, which otherwise would likely occur. The findings reveal the need for an evolution towards more predictive and proactive system management in electricity distribution, emphasizing the significant benefits of fostering robust partnerships among actors to ensure grid stability amid rising electric vehicle integration.

Robust Assessment of Hosting Capacity of Renewable Generation in Distribution Systems Considering the Active Network Management

Abstract As the penetration rate of renewable distributed generation (RDG) increases, its random fluctuation characteristics have brought many problems to the safe operation of the active distribution network (ADN), such as voltage over-limit, reverse flow, and deterioration of power quality. Therefore, it has effectively affected the new energy hosting capacity of the ADN. Therefore, the effective assessment of the new energy hosting capacity of the ADN is a key part of the distribution network planning. In order to cope with the random fluctuation characteristics of new energy, this paper presents a two-stage robust assessment method of RDG hosting capacity of distribution networks by considering active network management (ANM) techniques such as network reconfiguration, reactive power compensation and on-load voltage regulation. Finally, the improved IEEE-33 distribution system is used to verify the results. The proposed method has been shown to be effective for evaluating the ADN’s RDG hosting capacity.

Temporal Scenarios-based Assessment of Maximum Hosting Capacity of Renewable Generation in Distribution Systems Considering ANM Techniques

Abstract Incorporating large-scale renewable distributed generation (RDG) into the power grid introduces significant challenges associated with the indeterminacy of the power system, significantly impacting the comprehensive integration and efficient utilization of the distribution network. This underscores the imperative to investigate scientific evaluation methods and mechanisms for enhancing the RDG absorptive capacity within the distribution network. In light of the stochastic and temporal characteristics inherent to RDG, a novel approach for assessing the RDG hosting capacity within distribution networks is proposed, taking temporal scenarios into account. This method optimizes the RDG output probability distribution model based on its temporal fluctuation change rule, and constructs RDG temporal scenario model by using scenario reduction technology. Subsequently, a temporal scenarios-based RDG hosting capacity assessment model is established, taking into account active network management (ANM) techniques. The effectiveness of the proposed methodology is substantiated through simulation results conducted on a modified IEEE 33-bus system.

Deep Reinforcement Learning-Based Active Network Management and Emergency Load-Shedding Control for Power Systems

Deep Reinforcement Learning-Based Active Network Management and Emergency Load-Shedding Control for Power Systems

A security-constrained robust optimization for energy management of active distribution networks with presence of energy storage and demand flexibility

A discernible rise in the frequency and intensity of unpredictable events and their significant social and economic effects have made power system planners pay special attention to the security and reliability of power networks. For this purpose, this article tries to provide an efficient model for strengthening the security of active distribution networks based on multiple microgrids by optimally using energy storage resources and consumption management plans. In the proposed plan, a hierarchical two-stage approach has been developed in which the first stage models the incident and their impact on the distribution network. Then, in the next stage, preventive and corrective measures are implemented to increase system readiness and reduce damages caused by severe accidents. At this stage, various tools such as energy storage, distributed and renewable production sources have been used in addition to responsive loads. In the corrective phase, the independent microgrid partitioning method is used to restore the distribution network to increase the speed of load pickup process. In order to consider the uncertainty and the risk caused by events on the performance of the proposed plan, the problem is done by robust optimization to obtain more realistic results. Finally, in order to confirm the effectiveness of the proposed method in improving the security of distribution systems, a standard network of 33 buses was used with different operating conditions. The results confirm that the proposed security-constrained robust optimization plan not only reduces system operating costs, but also improves consumer security in the face of severe accidents. Although the increase in network security is associated with a slight increase in system costs, the benefit of reducing outages and optimal use of network capacity has led to the effectiveness of the proposed plan.

Multi-Mode Control of a Hybrid Transformer for the Coordinated Regulation of Voltage and Reverse Power in Active Distribution Network

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.

Energy Storage for Active Network Management on Electricity Distribution Networks with Wind Power

Increasing amounts of variable renewable generation are likely to enter our energy systems in the near future. In order to accommodate this generation onto our electricity networks, the concept of active network management (ANM) has become a significant area of research interest. Network connected energy storage is considered here as a means to actively control the network in order to increase the amount of generation it is possible to connect to a network. Energy storage is one of several potential methods of ANM, but has not been widely researched in this context. In this paper the ability of energy storage to increase the amount of wind energy accepted onto a network is assessed over a range of round trip storage efficiencies. The results show that energy storage is able to increase the energy accepted onto a distribution network, with the efficiency of the energy storage, energy storage capacity, wind farm size, network losses and network characteristics being important in determining the relative effectiveness of energy storage.

Stochastic model for active distribution networks planning: An analysis of the combination of active network management schemes

The widespread adoption of distributed generation (DG) is changing the operation of distribution networks. To increase the control of power flows and avoid the detriments of DG intermittency, active distribution networks (ADN) emerge as a feasible solution. In this paper, we propose a convex model for ADN expansion planning. Four active network management (ANM) schemes are considered: DG active power curtailment, DG reactive power control, on-load tap changer (OLTC) tap adjustment, and shunt compensation device control. The problem is formulated as a mixed integer second-order cone programming problem (MISOCP) using the distflow equations and second-order cone relaxations (SOCR). By scenario analysis, we address the uncertainty associated with power demand, renewable generation, and electricity prices. The proposed model is used to study the benefits of different combinations of ANM schemes to a modified IEEE 33-bus system and compare them with the individual benefits of each scheme. The results indicate that ANM schemes can reduce total network costs, reduce losses, delay network reinforcement, and improve the voltage profile. In addition, some combinations of schemes develop synergies in which the combined economic benefits are greater than the sum of the individual benefits.

An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks

Distribution grids must be regularly updated to meet the global electricity demand. Some of these updates result in fundamental changes to the structure of the grid network. Some recent changes include two-way communication infrastructure, the rapid development of distributed generations (DGs) in different forms, and the installation of smart measurement tools. In addition to other changes, these lead to distribution grid modifications, allowing more advanced features. Even though these advanced technologies enhance distribution grid performance, the operation, management, and control of active distribution networks (ADNs) have become more complicated. For example, distribution system state estimation (DSSE) calculations have been introduced as a tool to estimate the performance of distribution grids. These DSSE computations are highly dependent on data obtained from measurement devices in distribution grids. However, sufficient measurement devices are not available in ADNs due to economic constraints and various configurations of distribution grids. Thus, the modeling of pseudo-measurements using conventional and machine learning techniques from historical information in distribution grids is applied to address the lack of real measurements in ADNs. Different types of measurements (real, pseudo, and virtual measurements), alongside network parameters, are fed into model-based or data-based DSSE approaches to estimate the state variables of the distribution grid. The results obtained through DSSE should be sufficiently accurate for the appropriate management and overall performance evaluation of a distribution grid in a control center. However, distribution grids are prone to different cyberattacks, which can endanger their safe operation. One particular type of cyberattack is known as a false data injection attack (FDIA) on measurement data. Attackers try to inject false data into the measurements of nodes to falsify DSSE results. The FDIA can sometimes bypass poor traditional data-detection processes. If FDIAs cannot be identified successfully, the distribution grid’s performance is degraded significantly. Currently, different machine learning applications are applied widely to model pseudo-measurements, calculate DSSE variables, and identify FDIAs on measurement data to achieve the desired distribution grid operation and performance. In this study, we present a comprehensive review investigating the use of supervised machine learning (SML) in distribution grids to enhance and improve the operation and performance of advanced distribution grids according to three perspectives: (1) pseudo-measurement generation (via short-term load forecasting); (2) DSSE calculation; and (3) FDIA detection on measurement data. This review demonstrates the importance of SML in the management of ADN operation.

Optimal planning and forecasting of active distribution networks using a multi-stage deep learning based technique

This paper presents a comprehensive methodology for long-term planning in distribution networks to address the challenges associated with integrating renewable energy resources (RERs) and battery energy storage systems (BESSs). The methodology incorporates active network management (ANM) schemes, coordinated voltage control (CVC), and demand side management (DSM) to optimize the sizing and siting of RERs and BESS. Deep learning-based long-term forecasting techniques are utilized to capture the hourly variations in load demand, solar irradiance, and wind speed throughout the year. The objective is to minimize overall network costs, considering investment and operational costs, while planning over a 5-year horizon with a 3% annual load growth. The model includes tie-lines as a decision variable for network expansion investments, ensuring efficient network flexibility. The linearized mathematical model is developed as a mixed integer linear programming (MILP) problem to enhance scalability. A case study is conducted on the UKGDS 16-bus system to verify the optimization model, and further validation is performed on the IEEE 69-bus system to assess scalability. The results demonstrate the effectiveness of the proposed method in achieving accurate forecasting and efficient sizing and siting decisions for RERs and BESSs, including tie-line investments, providing valuable insights for real-world distribution network scenarios.

Model and observation of the feasible region for PV integration capacity considering Wasserstein-distance-based distributionally robust chance constraints

The increasing integration scale of photovoltaic (PV) systems brings enormous challenges on distribution networks (DNs). To provide an explicit boundary of feasible PV integration capacity (PVIC) associated with each integration location, this paper proposes a novel depiction of PV hosting capability, which is the feasible region for PVIC in high-dimensional space. Next, a multi-objective optimization model based on information gap decision theory (IGDT) is proposed to observe the feasible region, where active distribution network management (ADNM) schemes with a limited budget are deployed to improve PV hosting capability. The impact of PV output uncertainty on security constraints is addressed by data-driven Wasserstein-distance-based distributionally robust chance constraints (WDRCCs). Finally, a systematic procedure is developed to solve the proposed model. Note that the exact power flow model associated with a new equivalent WDRCC reformulation method is deployed so as to guarantee the accuracy of the assessment. The effectiveness, solution accuracy, computational efficiency, and scalability of the proposed model and method are verified with the 4-bus system, the IEEE 33-bus system, and the IEEE 123-bus system. The output of the common method based on the linearized power flow deviates by more than 10% from the output of the proposed method based on the exact power flow in the 33-bus system. Each multi-location PV integration request is feasible as its locations and capacities belong to the proposed region. It implies that this region can provide guidance for PV allocation.

The effect of contract- and network management on performance and innovation in infrastructure projects

IMPACT How should complex infrastructure projects be managed? A survey of practitioners in public–private partnership (PPP) projects and 35 interviews with these practitioners provided a detailed picture of the management of these projects. The survey research revealed that strict contract management (monitoring performance criteria and sticking to the contract) did not show a significant relationship to either the performance of, or innovation in, these projects. Network management (for example connecting involved parties and exploring new solutions), however, was significant—especially in terms of performance. In the interviews, practitioners highlighted that the complexity of these projects meant that a collaborative relationship between the public and private parties was essential to overcome unforeseen problems. It is therefore advisable to make process agreements at the start of contracts that provide for unexpected issues. The authors conclude that the contract is certainly important but will not generate good performance without active network management and making process rules about how parties can collaborate. ABSTRACT Based on economic and governance theories, this article uses survey and interview data to examine the relationship between contract management and network management on the one hand and collaboration, innovation and performance on the other. A positive relationship was found between network management and collaboration and performance. Contract management demonstrated no significant relationship with either collaboration or performance. Additionally, while there was a positive association between network management and innovation, it was not statistically significant. Qualitative data emphasized the complexity of projects and limitations of contracts as a possible explanation.

Hosting capacity enhancement of hybrid AC/DC distribution network based on static and dynamic reconfiguration

AbstractThe hybrid AC/DC distribution networks will have a crucial role in the future of smart grids due to their adaptiveness to a variety of DC loads and energy resources. Thus, it is necessary to assess and enhance the Hosting Capacity (HC) of these hybrid networks for the integration of Renewable Energy Sources (RESs). One way to increase the HC without further expansion in the grid is using the potential of Network Reconfiguration (NR). This paper proposes an HC enhancement model that includes AC/DC distribution NR. Two types of NR are considered, namely static, that is, embedding the NR capabilities at the planning stage, and dynamic, that is, using remotely controlled switches as an Active Network Management (ANM) scheme. Besides, two unique potential sources of hybrid AC/DC distribution networks that could be used for the HC enhancement are included, that is, reactive power control of Voltage Source Converters (VSCs) and the mesh operation capability. The problem is formulated as a mixed‐integer linear optimal power flow that aims to maximize the RESs HC under thermal and voltage constraints. Also, stochastic programming is utilized for managing the RESs uncertainties. The simulation results show the accuracy and efficiency of the proposed formulation from various perspectives.

Integrated T&D Voltage Stability Assessment Considering Impact of DERs and Distribution Network Topology

Electric distribution systems are going through a major upgrade with increasing penetration of distributed energy resources (DERs) and impacting transmission system operational analysis. Traditionally, the transmission system’s voltage stability margin is computed by performing a continuation power flow (CPF) analysis. In transmission-CPF, loads in distribution systems are aggregated at the transmission-distribution (T&D) interface buses, and the distribution system’s electrical characteristics, like feeder length, increasing penetration of DERs, and associated control, are not considered. The traditional CPF thus considers the distribution network as a purely passive network without active network management and distributed energy resources. In this paper, two integrated T&D CPF methods are implemented using a co-simulation framework that uses individual transmission and distribution solvers. The first method is iteratively-coupled T&D CPF (IC-TDCPF), where the iterative coupling concept forces convergence of boundary variables at T&D PCC (Point of common coupling) at every CPF step. The second method, Loosely Coupled T&D CPF (LC-TDCPF), relaxes the boundary convergence constraints at PCC to achieve faster calculation speed. Moreover, a reduced distribution network equivalent is also developed to enable faster computation of VSM by reducing the number of integrated T&D buses. The developed T&D CPF co-simulation framework uses an open-source platform <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MATPOWER</i> for solving transmission system and open-source software <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OPENDSS</i> for solving distribution system. Three large integrated T&D systems with 4196, 10587, and 12295 buses are developed to demonstrate the impact of DERs, voltage-dependent load models, and distribution network topology on integrated voltage stability margin using the proposed T&D CPF and co-simulation.

Optimal Coordinated Operation for a Distribution Network With Virtual Power Plants Considering Load Shaping

Increasing load demand and renewable energy resource integration will exacerbate the net load profile by enlarging the peak-to-valley difference. Virtual power plant (VPP) technology presents a promising pathway for the efficient energy management of active distribution networks (ADNs) with large-scale distributed energy resources. This paper proposes a bilevel model for the collaborative operation of an ADN with multiple VPPs in a joint energy-reserve market that is organized by a distribution system operator (DSO). In the upper-level problem, the DSO minimizes the total operational cost of the ADN to set the energy and reserve prices for trading with VPPs while considering the network technical constraints and the load shaping performance with reserve participation. In the lower-level problem, VPPs aim to maximize their profits by adjusting bidding quantities according to the price issued by the DSO. Then, the proposed bilevel model is transformed into a tractable single-level optimization problem via the Karush-Kuhn–Tucker optimality conditions and a sequence of reformulation techniques, and an analytical method is designed to calculate the potential losses. Simulation results demonstrate the effectiveness and superiority of the proposed approach in shaping the load profile and improving the system operational economy.

Aging aware adaptive control of Li-ion battery energy storage system for flexibility services provision

Battery energy storage systems (BESSs) play a major role as flexible energy resource (FER) in active network management (ANM) schemes by bridging gaps between non-concurrent renewable energy sources (RES)-based power generation and demand in the medium-voltage (MV) and low-voltage (LV) electricity distribution networks. However, Lithium-ion battery energy storage systems (Li-ion BESS) are prone to aging resulting in decreasing performance, particularly its reduced peak power output and capacity. BESS controllers when employed for providing technical ancillary i.e. flexibility services to distribution (e.g. through ANM) or transmission networks must be aware of changing battery characteristics due to aging. Particularly of importance is BESSs' peak power changes aiding in protection of the Li-ion BESS by restricting its operation limits of it for safety reasons and improving its lifetime in the long run. In this paper, firstly an architecture for ANM scheme is designed considering Li-ion BESSs as one of the FERs in an existing smart grid pilot (Sundom Smart Grid, SSG) in Vaasa, Finland. Further, Li-ion BESS controllers are designed to be adaptive in nature to include its aging characteristics, i.e. tracking the changing peak power as the aging parameter, when utilised for ANM operation in the power grid. Peak power capability of the Li-ion nickel‑manganese‑cobalt (NMC) chemistry-based battery cell has been calculated with the experimental data gathered from accelerated aging tests performed in the laboratory. Impact of such aging aware and adaptive Li-ion BESS controllers on the flexibility services provision for power system operators needs will be analysed by means of real-time simulation studies in an existing SSG pilot.

Coordinated Volt/Var Control of PV and EV Interfaced Active Distribution Networks Based on Dual-Stage Model Predictive Control

This article presents a dual-stage coordinated control approach for voltage regulation and congestion management of active distribution networks (ADN) in the presence of photovoltaic (PV) generators and electric vehicle stations (EVS). The proposed scheme operates on rule-based model predictive control (RBMPC) to optimally manage the settings of the regulating devices, i.e., on-load tap changer (OLTC), distribution static synchronous compensator (DSTATCOM), PV generators, and EV inverters that possess different temporal characteristics. The first (hourly time-scale) and the second (one-minute time-scale) stages of the dual-stage coordinated control mechanism are designed to correct the long-term and short-term voltage fluctuations, respectively. The objectives of the proposed approach are to minimize the number of OLTC operations (first stage) and changes in set-points of PV and EV inverters, and DSTATCOM (second stage) in addition to minimization of slack variables, energy loss, and voltage error at EV stations. In both the stages, predefined rules are set for MPC to optimize different objectives on the basis of the magnitudes of bus voltages. Simulations are performed on 33-bus and 38-bus distribution networks to test the efficacy of the control approach. It is observed that the proposed approach could mitigate the voltage variations as well as line congestion for different scenarios.

Real-Time Advanced Energy-Efficient Management of an Active Radial Distribution Network

This article elucidates a new real-time optimization framework for advanced energy-efficient management of active radial distribution networks. The proposed energy management process leverages the benefits of simultaneous deployment of online direct load control (DLC) and conservation voltage reduction (CVR) for decreasing peak energy demand. Initially the proposed problem is designed as a time-coupled stochastic mixed integer nonconvex programming (MINCP) to accommodate long-term offline beneficial aspects in real-time optimization framework, which is later simplified using merger of Queueing theory and Lyapunov optimization. A successive mixed integer linear programming (s-MILP) solution approach is proposed for accurate and fast convergence of the revised MINCP framework. The efficacy of the developed strategy is evaluated after comparing with two-benchmark energy management models (viz. offline and online greedy algorithm) by demonstrating on modified IEEE 69-bus test system. Further scalability of the proposed approach is validated by testing on large distribution networks.