Performance Of Distribution Network Research Articles

Allocation of charging stations in Kota city using data analysis/machine learning

ABSTRACT This paper proposes the allocation of charging stations of optimal capacity at appropriate locations within Kota city in Rajasthan state of India. The location of charging stations has been identified by considering various parameters including population density, family income, distribution network performance, number of electric vehicles and transformer capacity. The proposed approach of allocation of charging stations has been modeled mathematically and also by machine learning algorithms such as data mining, clustering with all the possible scenarios and constraints. In identifying suitable places for positioning charging stations in the city by observing different parameters and aspects, i.e. geo-statistical approaches, minimum traveling time and travel distance for charging stations has been included in the proposed approach. The proposed approach has been tested in MATLAB/Simulink environment.

A fast multi-objective pelican optimizer for optimal coordination of DGs and DSTATCOM considering risk penetration level of wind

In this study, a new variant, the fast pelican optimizer (FPO), is proposed to improve the performance of the radial distribution network (RDEN). The proposed variant is characterized by creating a dynamic interaction between two phases, exploration and exploitation, during the search process. The modifications introduced within the standard algorithm allow the proposed new variant, namely FPO, to be fast and adaptive to efficiently solve various complex optimization problems. In the first stage, the proposed FPO is newly adapted and applied to solve the optimal locations of various types of distributed generation based renewable sources and multi shunt compensators, namely the DSTATCOM devices based FACTS technology, and in the second stage, the proposed FPO is applied to optimize the active power of DG units in coordination with the reactive power of multi DSTATCOM. Three objective functions, such as the total power losses, the total voltage deviation, and the margin stability, are optimized individually and in coordination to enhance the performances of the practical radial distribution network (RDEN) 33-bus. A deep comparative study in terms of solution quality and convergence accuracy based statistical analysis is elaborated to demonstrate the competitive aspect of the proposed FPO.

Integrated Sustainability Index for Assessing the Performance of Water Distribution Network

Integrated Sustainability Index for Assessing the Performance of Water Distribution Network

Energy Optimization in Water Distribution System and Pump Scheduling

Efficient management of water distribution systems are critical for providing consistent water supply while reducing energy usage and operational expenses. This research proposes a unique method for improving water distribution and pump scheduling. The suggested system leverages ultrasonic sensors to monitor water levels in storage tanks, then dynamically changes pump operation and valve settings depending on real-time demand and tank levels. A mobile application gives users remote access to system controls, allowing them to work from anywhere. By combining sensor data with intelligent algorithms, the system optimizes pump scheduling to fulfill water demand while conserving energy and lowering system losses. The creation and implementation of such a system have tremendous potential to improve the performance and sustainability of water distribution networks.

A MILP model for optimal switch placement to enhance power distribution system performance in normal and hurricane conditions

Severe hurricanes can inflict significant damages in the order of several times the common network failures on both the distribution companies and consumers. Network reconfiguration and condition-based switching are the common actions for enhancing distribution network performance in both normal and emergency conditions. In this paper, a novel approach is presented for simultaneous improvement in resiliency, reliability, and power losses. A practical strategy is proposed by considering the coordination between load restoration and repair of damaged lines in optimal switch placement problems. The use of both remote-controlled switches (RCS) and manual switches (MS) as well as considering practical constraints related to line repair and load restoration leads to the increased number of decision variables and consequently the degraded performance of meta-heuristic methods to obtain the global optimum solutions, especially for larger scale networks. Therefore, the proposed approach uses the exact method of MILP to solve the optimization problem. In this context, the uncertainty related to damage status and repair time of lines during High-Impact Low-Probability (HILP) events is the main challenge in linear modeling of the problem for which new methods are provided in the study. The programming is coded in MATLAB and tested on a modified IEEE 33-bus network.

Reliability analysis in distribution system by deep belief neural network

Rapid increase in the usage of intermittent renewable energy, ongoing changes in electrical power system structure and operational needs posing growing problems while ensuring adequate service reliability and retaining the quality of power. Power system reliability is a pertinent factor to consider while planning, designing, and operating distribution systems. utilities are obligated to offer their customers uninterrupted electrical service at the least cost while maintaining a satisfactory level of service quality. The important metrics for gauging the effect of distributed renewable energy on distribution networks is reliability analysis. Reliability analysis in distribution systems involves evaluating the performance and robustness of electrical distribution networks. An artificial intelligence approach is implemented in this paper to improve reliability analysis with dispersed generations in distribution network. Deep belief neural networks (DBNNs) are a type of artificial neural network that can be used for various tasks, including analyzing complex data such as those found in power distribution systems. This paper integrated a DBNN using a particle swarm optimization (PSO) technique. The proposed model performance is assessed using mean square error, mean absolute error, root mean square error, and R squared error. The findings reveal that reliability analysis with this novel technique is more accurate.

Chaos-Based NMPC as a Novel MPPT Approach for Organıc Photovoltaıcs

Environmental degradation, alongside the dire consequences of climate change such as the melting of glaciers and rising sea levels, highlight the severe challenges associated with reliance on fossil fuels. As such, the shift towards sustainable and clean renewable energy sources, like photovoltaic systems, is becoming increasingly critical. This paper introduces a novel approach leveraging a chaotic-based nonlinear model predictive control to maximize the power output from organic photovoltaic cells. This method is distinguished by its rapid tracking capabilities and its effectiveness in enhancing the distribution network's performance under fault conditions. Utilizing a feedback-driven recursive control strategy, this approach efficiently predicts and adjusts to the optimal operating state, thereby minimizing its cost function. It comprises two primary phases: estimating the reference point and subsequently adjusting the operating point in alignment with this reference. In this process, the Lagrange function plays a pivotal role in optimizing the estimator's performance, while a chaotic neural network model predictive controller manages the boost converter's operation. Implementing this chaos-based nonlinear model predictive controller has successfully reduced overvoltage incidents by more than 1.3%. Notably, in the absence of such control methods, the penetration of OPV panels leads to voltage fluctuations beyond acceptable limits. The findings demonstrate that, alongside a decrease in network losses, there is an increase in the capacity of distribution feeders and a notable enhancement in the system's overall efficiency.

Optimal Allocation of Photovoltaic-Green Distributed Generation for Maximizing the Performance of Electrical Distribution Networks

Optimal Allocation of Photovoltaic-Green Distributed Generation for Maximizing the Performance of Electrical Distribution Networks

A multi-objective artificial hummingbird algorithm for dynamic optimal volt-var controls for high electric vehicle load penetration in a photovoltaic distribution network

Renewable energy systems (RESs) and electric vehicles (EVs) have become global necessities to meet the sustainable development goals (SDGs). Because they are intermittent and stochastic, these two technologies have caused power grid operation and regulation challenges despite their environmental benefits. A new combined dynamic optimal network reconfiguration (DONR) and capacitor bank switching (CBS) optimization technique reduces solar and multi-type load effects on the distribution network performance. Considering substantial EV load penetrations, ONR and CBS should coordinate to reduce energy loss, improve the voltage profile, and save money during 24 h operation. The artificial hummingbird algorithm (AHA) solves this difficult optimization problem for the first time. An enhanced IEEE 33-bus benchmark test system was used to simulate various instances to evaluate the computational characteristics of the AHA and compare it with other similar algorithms. The obtained results demonstrate the superiority of the combined DONR and CBS for real-time scenarios over any individual approach.

Hydraulic and Utility Performance Evaluation of Ataye Town Water Distribution Network, Ethiopia: case for small towns in developing countries

Evaluating hydraulic and utility performance of water distribution network is a way to check the functionality degree of a system. Water CAD software was used in developing a model and evaluating the hydraulic performance of water distribution system of Ataye Town, Amhara Region, Ethiopia. Both steady state and extended period simulation analysis were carried out to determine hydraulic parameters (pressure and velocity). The model was calibrated using Darwin Calibrator and validity was checked by both correlation coefficient and scatter plot. The utility performance was also evaluated using international water association performance indicators. The simulated result for steady state analysis showed that 68.7% of the nodes operated within optimum adopted pressure (15-60 meter) and 93% of the distribution pipes had a velocity of <0.6 m/s which was a minimum adopted velocity. For extended period simulation 34.3% during maximum demand time and 16.8% during minimum consumption hours had pressure <15meters. 10.8% had negative pressure during maximum demand hours located at Sudan, Selama, and Zigba sefer and 14 nodes (8.4%) during low consumption time had a pressure greater than 60meter, which was a maximum adopted pressure. The areas of high pressure were located at around mosque, Worku Hotel and Hamus Gebaya. The distribution system performs within the adopted pressure at minimum demand time (74.7%) and maximum consumption hours (65.1%). 23.5% of the systems had a velocity of 0.6-2 m/s and the rest had a velocity less than 0.6m/s during high demand time. During low consumption hours, 100% of the system velocity was estimated to be <0.6m/s. Based on the IWA performance indicators, water utility of Ataye Town was evaluated and had low, technical, financial, personnel, and environmental performances. Finally, the distribution system was modified and optimized by providing alternative connections with pressure reducing valves and changing pipe diameter to improve the hydraulic performance and reduce system disruption of the Town.
 Keywords: Extended period simulation; Hydraulic performance; IWA Utility performance; Steady state analysis; Water CAD

Real-Time Data Acquisition and Monitoring of Three Phase Load Balance in Electricity Distribution

A real-time monitoring mechanism for three-phase electrical power distribution panels is essentially needed to ensure that the power is evenly distributed across all phases, so that the efficiency of energy usage could be maintained. This paper presents monitoring and data acquisition system for three-phase balance distribution. The system ensures balanced distribution of electrical power in order to enhance the efficiency and reliability of power systems. It leverages advanced technologies to collect, analyze, and report data, providing valuable insights into the performance of the power distribution network. Data acquisition is established by using sensor PZEM004T to measure the electricity parameters which is integrated with Arduino microcontroller. In addition, real-time monitoring system is visualized through dashboard data management for more informative data acquisition and analytic purposes. The performance is evaluated by testing the instrument and application at a distribution panel within a span of five days to monitor electrical parameters. The results show that real-time monitoring system for three phase balance works perfectly to provide actual data and phase balance condition. The phase imbalance values are higher during the peak working hours where the loads are dynamics and extensively used.

Optimizing energy management of hybrid wind generation-battery energy storage units with long-term memory artificial hummingbird algorithm under daily load-source uncertainties in electrical networks

Due to the unpredictable nature of renewable energy sources, it is necessary to have a storage system to store energy during off-peak hours and supply it during peak hours. However, if the storage units in a network are not properly connected, the benefits of the storage system cannot be realized. To improve the performance of radial distribution networks, this research proposes an optimal locating and sizing problem of battery energy storage (BES) and a renewable source of wind turbine distributed generation (WTDG). To solve this allocation problem, a multi-objective function (MOF) based on techno-economic parameters of total active power losses (TAPL), total voltage deviation (TVD), and investment cost of integrated devices is optimized using a modified version of the Artificial Hummingbird Algorithm (AHA) that incorporates a long-term memory component. With this enhancement, the algorithm can make decisions based on multiple past experiences, allowing it to consider a wider range of promising locations during the optimization process. This integration of long-term memory reduces the risk of premature convergence or stagnation and allows the algorithm to gain a broader perspective, potentially leading to improved performance and exploration of the solution space. The results demonstrate the effectiveness of the Long-term Memory Artificial Hummingbird Algorithm (LMAHA) in optimizing the MOF and enhancing the performance of the electrical distribution networks of IEEE 33 and 69 bus.

Consolidation of LVFRT capabilities of microgrids using energy storage devices

The performance and dependability of distribution networks may be enhanced by the incorporation of microgrids (MGs). However, it is necessary to enhance low voltage fault-ride-through (LVFRT), which has the capacity to prevent abrupt grid disconnections during LV occurrences under problematic circumstances. In this study, a control strategy for energy storage elements (ESDs) which includes batteries and supercapacitors is proposed to enhance LVFRT under balanced and unbalanced faults. The MG comprises wind farms and/or photovoltaic arrays. Based on the dynamic simulations using MATLAB/SIMULINK, the ESDs can enhance LVFRT capability. A comparison of the conventional crowbar scheme and ESDs is realized, and the latter has a better performance than the former in retaining the DC-link voltage within satisfactory bounds. For the purpose of maintaining the DC-link voltage at a reference level, the battery stores extra power in the DC-bus of three systems. LVFRT is improved by the crowbar circuit, however the resistance consumes the extra power. Super capacitors (SCs) prevent DC voltage fluctuations, reduce active power oscillations, and hasten system stabilization when present. At an advanced stage of this effort, the coot bird optimizer (CBO) is applied to generate the best gains of bi-directional converter PI-controller and the ESDs ratings to have minimum ripples in the DC-bus voltage and to boost the LVFRT capability of the MGs. The viability of the proposed method based on the CBO’s results is indicated with further validations under different operating scenarios.

A two‐stage scheduling model for urban distribution network resilience enhancement in ice storms

AbstractThis paper proposes a two‐stage stochastic scheduling model for urban distribution network resilience enhancement against ice storms, which coordinates mobile deicing equipment routing and distributed energy resources dispatching. An improved line ice thickness prediction model and a photovoltaic power generation prediction method in accordance with conditional generative adversarial networks are proposed to provide data boundaries for scheduling strategy. Facing the uncertainty of line failure, a two‐stage scenario‐based distribution network optimization model is established. At first stage, the mobile deicing equipment routing strategy is decided to mitigate the impact caused by ice storms. The Monte‐Carlo simulation method is introduced to describe the uncertainty of line failure due to ice acceleration. For the second stage, based on the results of photovoltaic forecasting and possible distribution line failure scenario generated by Monte‐Carlo simulation method, the optimal distributed energy resources dispatching strategy can be obtained through the mixed integer programming. The proposed model is simplified to a mixed integer linear programming model that can be solved by a commercial solver. The test results on the modified IEEE 33‐node system and modified 69‐node system demonstrate that the proposed method can effectively improve the resilience performance of urban distribution network under ice storms.

Assessment of Applying Demand side management Techniques on distribution networks performance: a practical Case

Assessment of Applying Demand side management Techniques on distribution networks performance: a practical Case

Adaptive MPC for Burst Incident Management in Water Distribution Networks

This paper investigates an adaptive control strategy which takes advantage of remotely actuated pumps and valves to improve the performance of water distribution networks (WDN) during burst incidents. The proposed strategy relies on model predictive control (MPC) to switch between problem formulations corresponding to normal operating conditions and burst conditions. The optimal control problems are formulated as non-convex non-linear programs (NLP), where the action of fixed-speed pumps and control valves (non-return and bi-directional) is modeled exactly and burst remedial interventions are represented using time-dependent connectivity matrices. We propose an efficient solution method based on reformulations and approximations of the non-convex objectives and constraints and apply the proposed strategy to the adaptive control of an operational WDN during a burst incident. The numerical experiments show that the computed burst control settings allow to partially or completely restore customer supply following the detection of a burst while maintaining minimum water levels in the network tanks and without increasing the cost of pumping operations. The results suggest that the proposed adaptive control strategy can be implemented alone, to mitigate the impact of bursts incidents in WDNs at minimum cost, or to complement traditional remedial interventions.

A PSO-TVAC for optimal installation of multiple distributed generations in a radial distribution system

<span>The integration of distributed generations (DGs) into distribution system networks has seen a significant increase owing to the depletion of conventional energy resources and the growing power demand. However, a high penetration of DGs can adversely affect the stability of the distribution networks due to the intermittent nature of their generation capabilities. Hence, it is crucial to design DGs optimally to support grid voltage regulation and improve distribution networks performance. This study utilizes a particle swarm optimization (PSO) with time-varying acceleration coefficients (PSO-TVAC) technique to optimize the location and size of various distributed generation units while minimizing the total active power loss. The initial system power loss was determined using a distribution load flow analysis based on the backward-forward technique. The PSO-TVAC algorithm was then employed to identify the optimal placement and sizing of DGs within the standard IEEE 33-bus radial distribution network. To assess the proposed algorithm's effectiveness, photovoltaic (PV) and wind turbine (WT) were considered as the DGs. In comparison with other algorithms, PSO-TVAC achieved the lowest power loss, measuring 72.79 [kW] and 12.14 [kW] for <br /> 3-PV and 3-WT installations, respectively. Furthermore, the optimal installation of 3-PV and 3-WT improved the distribution system performance by 65.49% and 94.25%, respectively.</span>

Optimal Placement of μPMUs in Distribution Networks with Adaptive Topology Changes

With the increasing integration of energy sources and the growing complexity of distribution networks, it is crucial to monitor and early detection of topological changes to ensure grid stability and resilience. Current methods, for optimizing the placement of micro Phasor Measurement Units (μPMUs) focus on achieving observability and efficient monitoring. These algorithms aim to minimize the number of μPMUs needed while maintaining system observability or meeting criteria for observability. However, they may not consider all real-world constraints and uncertainties. In this study, we introduce a strategy for placing μPMUs with the objective of enhancing observability and monitoring capabilities. Our proposed algorithm employs a technique that makes optimal decisions at each step to approximate the global optimum. To determine the locations for μPMUs our algorithm takes into account parameters such as network structure, key nodes, and system stability. One distinguishing feature is its adaptability to distribution networks, including changes, in topology or potential device failures. Unlike classical approaches, our algorithm can continuously provide optimal placement solutions even in evolving network conditions. We have demonstrated that our suggested method achieves better results in terms of observability value and the required number of μPMUs compared to the state-of-the-art. By strategically placing μPMUs, operators can improve system observability, quickly detect and locate faults, and make informed decisions for effective network operations. This research helps improve optimal placement strategies for μPMUs by providing practical and effective solutions to improve distribution network reliability, resilience, and performance in the face of changing dynamics.

Integration of distributed generations and static var compensators with static synchronous compensators to reduce power losses

The necessity of power is growing in response to today's growing population. In order to reduce both real and reactive power losses, three devices have been combined in this research. The distributed generations (DGs) and static var compensator (SVC) with static synchronous compensator (STATCOM) play important role for reduction of power losses (both real and reactive power) and improvement of voltage profile. The research outcome presented here outlines that presents genetic algorithm (GA) may be utilised to improve power system performance and voltage profile (VP) of distribution networks by integrating DG, SVC and STATCOM planning properly. On a 37-bus distribution network, the proposed methodology has been applied. The results of this study are helpful to those focusing on the functionality of viable and upcoming electrical grids, as well as those working in the disciplines of DG, SVC and STATCOM integration for improved system performance.

Assessment of Water Supply System Performance Indicators Based on the Customer Bill-Based Approach

Significant efforts have been undertaken over the last two decades by governmental organizations and local and international non-governmental sectors to improve water supply and sanitation structures; however, there are significant challenges to the town's growth and expansion. Chiro town's population is increasing, and the community is affected by water and excreta-borne diseases as a result of a lack of proper sanitation facilities and a water supply system. In this study, billed data from Chiro Town water supply service enterprise customers have been used to assess the performance of the water distribution network over ten years (2011-2020). The findings show that the Town's water coverage is 62.9%, the water demand is 8,121.6m3/day, and the computed annual water production is 4,222.3m3/day. Residents of the town have complained about a lack of access to improved drinking water supplies, low water consumption, and sanitation facilities, particularly in the community of those who live in the town's borders and practice open defecation. For all domestic use, 16.32% and 83.68% of respondents used unprotected and protected water sources, respectively. This study suggested that strong sectoral coordination, community involvement, and sufficient financial resources were required to identify the root causes and provide an efficient water supply system in the study area.