Distribution System Operators Research Articles

Refinement of Recloser Operation and Safety Enhancement in Distribution Systems: A Study Based on Real Data

Refinement of Recloser Operation and Safety Enhancement in Distribution Systems: A Study Based on Real Data

Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution Systems

In this paper, a multi-objective grey wolf optimization (GWO) algorithm based Bidirectional Long Short Term Memory (BiLSTM) network machine learning (ML) model is proposed for finding the optimum sizing of distributed generators (DGs) and shunt capacitors (SHCs) to enhance the performance of distribution systems at any desired load factor. The stochastic traits of evolutionary computing methods necessitate running the algorithm repeatedly to confirm the global optimum. In order to save utility engineers time and effort, this study introduces a BiLSTM network-based machine learning model to directly estimate the optimal values of DGs and SHCs, rather than relying on load flow estimates. At first, a multi-objective grey wolf optimizer determines the most suitable locations and capacities of DGs and SHCs at the unity load factor and the same locations are used to obtain optimum sizing of DGs and SHCs at other load factors also. The base case data sets consisting of substation apparent power, real power load, reactive power load, real power loss, reactive power loss and minimum node voltage at various load factors in per unit values are taken as input training data for the machine learning model. The optimal sizes of the DGs and SHCs for the corresponding load factors obtained using GWO algorithm are taken as target data sets in per unit values for the machine learning model. An adaptive moment estimation (adam) optimization approach is employed to train the BiLSTM ML model for identifying the ideal values of distributed generations and shunt capacitors at different load factors. The efficacy of the proposed ML-based sizing algorithm is demonstrated via simulation studies.

Enhancing distribution system performance by optimizing electric vehicle charging station integration in smart grids using the honey badger algorithm

The global surge in electric vehicle (EV) adoption has driven significant research into electric vehicle charging stations (EVCS) due to their environmentally friendly attributes, including low CO₂ emissions. However, integrating EVCS into existing distribution grids presents challenges such as power losses and voltage instability, especially with the increasing incorporation of renewable distributed generation (RDG) sources and battery energy storage systems (BESS). This study introduces a novel honey badger optimization algorithm (HBOA), designed to enhance solution convergence and optimize multi-objective criteria efficiently. HBOA strategically places EVCS while considering vehicle-to-grid (V2G) capabilities and user driving behaviors over a full 24-hour cycle, effectively addressing uncertainties and dynamic conditions. Simulations on modified IEEE 69-bus and Indian 28-bus radial distribution systems (RDS) demonstrate significant results: in the IEEE 69-bus system, power loss is reduced by 62.0%, the voltage stability index (VSI) increases from 0.7139 to 0.8311, and CO₂ emissions decrease by 66.0%. In the Indian 28-bus system, power loss decreases by 55.5%, with VSI improving from 0.7394 to 0.9964, leading to a 50.0% reduction in CO₂ emissions. The proposed smart microgrid (MG) structure incorporates interconnected MGs for residential, commercial, and industrial sectors, emphasizing the efficacy of RDGs in mitigating the impact of EVCS on RDS. The advantages of the HBOA method lie in its superior optimization capabilities, which enhance system performance, reduce operational costs, and promote sustainability, highlighting the proposed methodology’s potential for future integration of EVCS in distribution networks.

Dynamic Energy Management and Control of Networked Microgrids based on Load to Grid Services and Incentive-Based Demand Response Programs: A Multi-Agent Deep Reinforcement Learning Approach

This study has presented the energy management paradigm in a networked microgrid structure based on L2G services and considering incentive-based load response (IBDR) programs and energy market requirements to reduce operating costs, control and restore voltage and frequency index, providing the benefits of subscribers and distribution system operators. In this study, multi-objective functions such as optimal operation based on IBDR structure and energy market requirements, risk assessment, and L2G service approach are configured in the framework of central and local controllers. Optimal operation and risk assessment are analyzed by a multi-task learning algorithm based on multi-objective function and L2G service policies are evaluated based on multi-agent deep reinforcement learning. Control policies are sent by the communication system to the components affecting the optimal power distribution as well as the voltage and frequency controllers. L2G services have been evaluated in different scenarios such as plug-and-play operating conditions, load fluctuations, and operating in island mode. The results of optimal operation based on L2G services show that the IBDR program implementation reduces the total operation cost by 21%. Also, the total operating cost of the proposed framework is 13.97% less than the RL method and 27.8% less than the ANN method.

Adaptive multi-agent reinforcement learning for dynamic pricing and distributed energy management in virtual power plant networks

This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant (VPP) networks using multi-agent reinforcement learning (MARL). As the energy landscape evolves towards greater decentralization and renewable integration, traditional optimization methods struggle to address the inherent complexities and uncertainties. Our proposed MARL framework enables adaptive, decentralized decision-making for both the distribution system operator (DSO) and individual VPPs, optimizing economic efficiency while maintaining grid stability. We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay. Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods, including Stackelberg game models and model predictive control, achieving an 18.73% reduction in costs and a 22.46% increase in VPP profits. The MARL framework shows particular strength in scenarios with high renewable energy penetration, where it improves system performance by 11.95% compared with traditional methods. Furthermore, our approach demonstrates superior adaptability to unexpected events and mis-predictions, highlighting its potential for real-world implementation.

Distribution network topology identification method based on state estimation with mixed integer programming and structural equation model

Distribution network topology identification (DNTI) is an important prerequisite for distribution system operation. Frequent topology changes and limited measurement equipment make identifying the correct topology unavoidably challenging. To this end, this paper proposes a state estimation method for distribution network topology identification using a small amount of phasor measurement unit (PMU) measurement data. Firstly, this paper introduces the application of the structural equation model (SEM) in distribution networks and explains the relationship between branch currents and topology. Then, auxiliary variables are introduced for the feasible domain problem present in the algorithm, which improves the solvability of the algorithm. In addition, this paper reconsiders the operation mode constraints in terms of nodes from the perspective of distribution network operation mode. The Structural Equation Modeling-based Mixed Integer Programming (SEM-MIP) method proposed in this paper can be solved using existing commercial solvers, and its effectiveness has been verified by simulation in IEEE 33-node test system and IEEE 123-node test system.

Evaluating the role of prosumers in providing energy systems with flexibility products

AbstractIncreasing the penetration of renewables on the demand side causes operational challenges in distribution grids due to their variable and uncertain behaviour. These resources have increased distribution grid net load fluctuation during recent years, which needs to be covered by demand side flexibility (DSF). DSF should be quantified and measured by a suitable index to show the level of a system DSF in different situations. In this paper, the flexibility area index of distribution systems as a suitable metric for prosumers’ DSF evaluation, especially in the presence of renewable sources, is calculated. This index is defined first for one prosumer and then extended to a distribution system by a combination of prosumer indices. The mentioned index is decomposed into two components, one for ramp‐up (negative flexibility) and another for ramp‐down (positive flexibility). To calculate and manage prosumers’ flexibility, a novel flexibility‐oriented stochastic framework for prosumers is suggested. The amount of load shedding is under control by the risk‐management strategy in the proposed framework, which can facilitate distribution system operator operation. The results obtained from the implementation of the case study validate the major features of the proposed framework more efficiently.

Development of a Machine-learning-based Framework to Assess the Performance of Water Distribution Systems

Recent statistics indicate that the national water supply coverage is approximately 99%, reflecting a high level of service. However, the aging infrastructure continues to cause issues such as pipe failures and water quality concerns, creating operational difficulties. To solve these issues, performance assessments are used to quantitatively evaluate the system conditions and ensure effective maintenance. In Korea, these assessments are based on a scoring system that combines direct and indirect assessments. However, direct assessment of all pipes is limited by cost and time constraints. To address this issue, a deep neural network is used to assess the aging of water distribution systems. This study develops a framework to estimate direct results from indirect data by analyzing the correlation between indirect and direct assessments. Additionally, data augmentation is considered to compensate for the lack of training data in direct assessments, improve the reliability of performance assessments, and support better prioritization in system maintenance.

Integrated market scheduling with flexibility options

This work presents a generic optimization framework using advanced mixed-integer programming techniques to integrate day-ahead and balancing markets with distributed energy resources such as storage, electric vehicles, demand response, and Transmission and Distribution System Operators' coordination schemes. The day-ahead model determines optimal initial energy scheduling, while the balancing model optimizes energy and reserve products for three market designs with varying Transmission and Distribution System Operators’ coordination. The framework is applied to the interconnected Greek-Bulgarian-Romanian power system, considering 2030 installed capacities.The model outputs illustrate market coupling scenarios among Romania, Bulgaria, and Greece, highlighting clear price signals and distributed energy resources flexibility. Results reveal the significance of a diverse energy mix for energy security and show that Transmission and Distribution System Operators’ coordination significantly influences ancillary service prices, with reductions of up to 80 % in certain scenarios. Net demand values determine electricity flow direction. Flexibility providers like storage can cover up to 100 % of the upward congestion management requirements and 11 % of upward balancing energy, helping smooth energy allocation from intermittent renewables. The impact of electric vehicle penetration on generation scheduling is minimal. The proposed model offers valuable insights for system operators, market participants, and policymakers, enabling them to provide accurate price signals and optimize resource allocation.The integrated day-ahead and balancing market models support efficient renewable energy integration, emissions reduction, enhanced grid stability, and investment in low-carbon technologies. This aligns with the United Nations Framework Convention on Climate Change goals, contributing to the development of sustainable and resilient energy systems.

Enhanced predictive PDF control of stochastic distribution systems with neural network compensation and its application

Compared to the conventional control algorithms that use mean and variance as indicators, predictive probability density function (PDF) control can effectively handle the output PDF control problem of non-Gaussian stochastic distribution systems. However, the existing predictive PDF control method does not consider the construction error between output PDF and weight, thus the control performance is still unsatisfactory. Therefore, this paper proposes a new Enhanced Predictive PDF control method (En-PDF) to improve the output PDF control performance of stochastic distribution systems. The proposed method mainly consists of two parts: the predictive PDF control part and the neural network compensation control part aiming to reduce the bias of output PDF. First, the Radical Basis Functions (RBFs) are used to approximate the PDF of the stochastic systems output, and then a prediction model representing the relationship between input and weight is established using the subspace identification algorithm to design the predictive PDF control for the stochastic systems. Next, the Kullback-Leibler (KL) divergence is used to measure the similarity between the output PDF and the set PDF, combined with the weight error and compensation to design a new performance index. Based on this, the parameters of the neural network are adjusted using the gradient descent algorithm to obtain the optimal compensation, and the stability and tracking performance of the proposed algorithm are analyzed using inductive reasoning method. Finally, the predictive PDF control input with the compensation work together on the controlled plant to achieve high-performance control of the output PDF of non-Gaussian stochastic distribution systems. Both simulation experiments and physical control experiments validate the effectiveness and superiority of the proposed method.

An Efficient Archerfish Hunting Optimizer (AHO)-Based Power Quality Improvement in Distribution System Connected UPQC

These days, because power quality (PQ) issues are costing industrial customers a lot of money, both electrical utilities and end-users are paying a lot of attention to them. The most significant and frequent PQ concerns for sensitive loads are voltage sag and swell. This manuscript proposes an optimization technique for enhancing the control system of a unified power quality conditioner (UPQC) connected solar photovoltaic system in the distributed generation system. The proposed strategy is the archerfish hunting optimizer (AHO). The objective of the AHO approach is to optimizing UPQC operation in distribution systems, focusing on targeted power quality enhancement. The optimal control pulses are produced by the series active power filter and the shunt active power filter, depending on the load-side and source circumstances. During the load variation states the AHO technique manages the power loss, total harmonic distortion (THD), and voltage instability issue respectively. The performance of the AHO methodology is carried out on the MATLAB platform and compared with existing methods, like particle swarm optimization (PSO), latent semantic analysis (LSA), and ant-lion optimizer (ALO). The existing methods THD are 6.35%, 7.85%, and 9.19%. The proposed method of THD is 4.22%. From the outcome, it concluded that the proposed method based on the THD is low contracted to present techniques.

Optimal operation of three-phase unbalanced active distribution system based on semidefinite relaxation and convex-concave procedure

The three-phase distribution optimal power flow (D-OPF) problem has attracted much attention in recent years due to practical requirements for managing distributed energy resources and multi-energy customers in unbalanced active distribution systems. To deal with the strong nonconvexity of the three-phase OPF problem, this paper develops the semidefinite programming (SDP) formulation of three-phase OPF problems considering the mutual coupling in multi-phase networks. Instead of directly dropping the rank-one constraints, a sequential convex optimization algorithm based on the convex-concave procedure (CCP) is proposed for recovering a feasible and optimal solution of three-phase power flow. The proposed iterative algorithm not only has high computational efficiency but also guarantees the optimality when the semidefinite relaxation or second-order cone relaxation method is inexact. Case studies on various IEEE testing distribution systems verify that the proposed algorithm recovers the feasible and optimal power flow solution. It also exhibits a relatively low number of iterations and short solving time in large-scale IEEE testing systems.

Real-time risk assessment of distribution systems based on Unscented Kalman Filter

The continuous growth of renewable energy and the load level has posed increasingly severe operational risks to distribution systems. In view of this, this paper combines state estimation with risk assessment, and uses the results of distribution system state estimation based on Unscented Kalman Filter as the input of risk assessment. With the combination, the sampling based on probability distributions in traditional risk assessment methods is no longer needed, thus avoiding the difficulty of updating probability distributions timely according to the latest information in real-time operation. By applying the proposed risk assessment method, the real-time assessment of operational risks in perspectives of bus voltage, branch power, and renewable energy utilization is achieved. Meanwhile, the weight of each risk index is properly determined according to both subjective and objective knowledge by using Analytic Hierarchy Process method and entropy weight method. Case studies show that the proposed method achieves effective assessment of comprehensive risks in the operation of distribution system.

ОРГАНІЗАЦІЙНО-ЕКОНОМІЧНІ СКЛАДОВІ ПРОГРАМ РОЗВИТКУ ЕЛЕКТРИЧНИХ МЕРЕЖ

In the article researched the indicators that directly depend on the state of electrical networks. The first of them is the level of reliability of electricity supply, the indicators of which are the SAIDI and SAIFI indices. The domestic indicators of the specified indices significantly exceed the level in the countries of the European Union and to date have no signs of a decrease. Plans for reducing the indicator SAIDI have been analyzed in relation to the method of incentive tariffs for operators of distribution systems. The next considered indicator is high technological losses of electrical energy. It was analyzed that the volume of these losses has a growing trend and also significantly exceeds the average European level. The main reason for these phenomena is the outdated and worn-out condition of electrical grids, which, given the damage caused by missile strikes and shelling, tends to worsen. Proposals have been identified that in the future may help improve the condition of grids and eliminate a number of problematic phenomena Their main problems and approximate volumes of priority grids modernization were summarized. Possible practical measures aimed at helping to reduce technological losses of electricity are considered. The structure of the proposed traditional measures necessary to manage the development of networks and consumers were analyzed. It has also recently started in Ukraine development and approval of a set of documents which are directed to the implementation of the Energy Transition scenario and the implementation of the Smart Grid concept. The further need for the development of system and program documents at both the state and regional levels was identified. It is proposed to include in these documents a number of organizational and economic components which will most effectively contribute to the achievement of the set goals.

Towards full electrification of local public transport: A tool to guide strategies for implementing the electric charging network

This paper describes a methodology and a Decision Support Tool (DST) for the assessment of the impacts of future scenarios of public electric mobility on the electric Distribution Network (DN) of a city, aimed to support the implementation strategies of the electric charging network for the Local Public Transport (LPT).The objective of the proposed tool is supporting the decision makers in estimating the effect of the future development of electromobility on the urban electric grid considering the evolution of both electric buses and their charging infrastructure.Some scenarios for the development of electric charging infrastructure for LPT were identified together with the local public transport operator and the energy provider. The results, which are of interest to both the local Public Transport Operator (PTO) and the local Distribution System Operator (DSO), have been quantified for the city of Turin (Italy), but the methodology can be adopted in any urban context: DSOs and PTOs of each city can use the proposed tool as a support to define their public transport electrification strategies.Some results from the case study examined in this paper (Turin, Italy) show that the energy demand related to the electrification of LPT on a typical weekday is about 45 MWh in 2030 (corresponding to 40 % e-bus), and about 90 MWh when the entire bus fleet is electric. It is also shown how more distributed recharging during the day (with intermediate recharging at the terminus) can dampen the energy/power peaks required at the depot compared to overnight recharging alone: a reduction of up to 27 % of energy demand at the depot is observed due to opportunity charging.

Exploiting the determinant factors on the available flexibility area of ADNs at TSO‐DSO interface

AbstractActive distribution networks (ADNs) are consistently being developed as a result of increasing penetration of distributed energy resources (DERs) and energy transition from fossil‐fuel‐based to zero carbon era. This penetration poses technical challenges for the operation of both transmission and distribution networks. The determination of the active/reactive power capability of ADNs will provide useful information at the transmission and distribution systems interface. For instance, the transmission system operator (TSO) can benefit from reactive power and reserve services which are readily available by the DERs embedded within the downstream ADNs, which are managed by the distribution system operator (DSO). This article investigates the important factors affecting the active/reactive power flexibility area of ADNs such as the joint active and reactive power dispatch of DERs, dependency of the ADN's load to voltage, parallel distribution networks, and upstream network parameters. A two‐step optimization model is developed which can capture the P/Q flexibility area, by considering the above factors and grid technical constraints such as its detailed power flow model. The numerical results from the IEEE 69‐bus standard distribution feeder underscore the critical importance of considering various factors to characterize the ADN's P/Q flexibility area. Ignoring these factors can significantly impact the shape and size of Active Distribution Networks (ADN) P/Q flexibility maps. Specifically, the Constant Power load model exhibits the smallest flexibility area; connecting to a weak upstream network diminishes P/Q flexibility, and reactive power redispatch improves active power flexibility margins. Furthermore, the collaborative support of reactive power from a neighboring distribution feeder, connected in parallel with the studied ADN, expands the achievable P/Q flexibility. These observations highlight the significance of accurately characterizing transmission and distribution network parameters. Such precision is fundamental for ensuring a smooth energy transition and successful integration of hybrid renewable energy technologies into ADNs.

A hierarchical framework for aggregating grid-interactive buildings with thermal and battery energy storage

The behind-the-meter (BTM) thermal and battery energy storage can help improve energy efficiency, reduce energy costs, and enhance energy resilience, particularly in rural areas and for disadvantaged communities. Aggregating numerous BTM energy storage systems can act as a price influencer with a significant source of load shifting and peak demand reduction. An integrated and scalable control mechanism is required to effectively utilize energy storage systems and flexible building loads to maximize the economic benefits, considering various distribution system constraints. This paper presents an innovative hierarchical coordination framework for energy storage and flexible load in buildings, considering various factors such as electricity prices, thermal comfort, and distribution system modeling and constraints. At the upper level, a distribution system operator optimizes the power flow to minimize its power procurement costs from the electricity wholesale market, while at the lower level, aggregators determine the optimal dispatch of battery and thermal energy storage systems in multiple buildings on behalf of end-users to minimize operating costs according to the power prices. These problems are solved using a game-theoretic approach through negotiations between the distribution system operator and aggregators as a bi-level decision model. Simulation case studies have been performed for a test distribution network with a number of building end-users using energy storage systems to quantify the performance of aggregators. The results demonstrate that the proposed strategy can reduce peak load for a reliable electricity distribution network while saving electricity bills for customers.

Optimal operation of a residential energy hub participating in electricity and heat markets

The integration of electricity and heat networks provides significant benefits by enhancing system flexibility and improving overall energy efficiency. Energy hubs play an important role in these interconnected systems, facilitating the production, conversion, and storage of energy across different forms. Potential flexible loads that may exist in an energy hub can further optimize its resource utilization and operational stability. In this respect, this paper addresses the day-ahead energy management of a residential complex modeled as an energy hub, incorporating medium-scale generation and storage units, as well as must-run and flexible loads. We also consider energy hub operator’s energy transactions in power distribution system and district heating and aim to obtain the optimal bidding strategy of this profit-driven agent. The negotiations among the energy hub operator, distribution system operator, and district heat network operator are modeled as a single-leader multi-follower Stackelberg game. A Nash Equilibrium of this game can be obtained by modeling the interactions among players as a bi-level optimization problem. The lower-level problems account for multi-period optimal power flow, modeled as an exact AC optimal power flow, and multi-period optimal thermal flow. The upper-level problem models the energy management of the energy hub. Replacing the lower-level problems with their optimality conditions, the optimal bidding of the energy hub operator can be obtained by solving the resulted mixed-integer linear programming problem as a mathematical program with equilibrium constraints. Finally, we numerically evaluate the proposed framework in a case study for a large residential complex participating in a power distribution and a heat network.

Cost - benefit analysis of DISCOs by Optimal allocation of DGs and DSTATCOM using GTO algorithm

Electric power Distribution Companies (DISCOs) provide low-cost, reliable, and consistent voltage power from distribution substations. DISCOs have radial transmission lines with all buses as load buses and no generating buses. The voltage at each bus decreases, resulting in larger network losses and voltage variations, which lowers DISCO and customer costs and benefits. This study strategically uses Distributed Generation (DGs) and DSTATCOM to optimize cost-benefit analysis and voltage stability for Distribution Companies (DISCOs). We planned DG unit and DSTATCOM deployment using a new and comprehensive Group Teaching Optimization (GTO) method in this study. The algorithm considers Distribution Company and DG Owner profitability simultaneously. Group Teaching Optimization (GTO) is used on a 33-node test system. MATLAB simulations show the GTO's usefulness in Distribution System Operators.

Strategic investments in mobile and stationary energy storage for low-carbon power systems

The main feature and trend of the distribution system is the integration of renewable energy with high penetration rates. The variability and zero marginal cost characteristics of renewable energy will lead to drastically fluctuating locational marginal prices. In the deregulated electricity market, merchants have incentives to utilize energy storage and price arbitrage. Mobile energy storage has a short capital payback period and is widely recognized for transferring energy in the temporal and spatial dimensions. This paper analyses the interaction between merchants and distribution system operators and presents a hybrid energy storage strategic investment framework using bi-level programming. In the upper-level problem, the merchant formulates the capacity, location, and operation strategy of different energy storage to maximize the market revenue of hybrid energy storage, which is paid for by the locational marginal price. In the lower-level problem, the distribution system operator develops an optimal dispatch strategy considering renewable energy and merchant investments. A joint energy and reserve market clearing procedure is performed to derive the locational marginal prices. In the lower-level problem, a data-driven solution strategy based on machine learning is introduced. It uses deep neural networks to approximate the mapping relationship between distribution system states and locational marginal prices. The presented methodology is implemented in an IEEE-33 node system. The results demonstrate that, compared to the basic case, the hybrid energy storage investment strategy has led to an 8.1 % increase in merchant profits and a 12.9 % reduction in the operational costs of the distribution network. The deep neural network approximator fully avoids the lower-level problem with less than 5 % optimality loss.