Operation Of Power Distribution System Research Articles

Dynamic assessment approach for intelligent power distribution systems based on runtime verification with requirements updates

The study aims to address the challenge of dynamic assessment in power systems by proposing a design scheme for an intelligent adaptive power distribution system based on runtime verification. The system architecture is built upon cloud–edge-end collaboration, enabling comprehensive monitoring and precise management of the power grid through coordinated efforts across different levels. Specifically, the study employs the adaptive observer approach, allowing dynamic adjustments to observers to reflect updates in requirements and ensure system reliability. This method covers both structural and parametric adjustments to specifications, including updating time protection conditions, updating events, and adding or removing responses. The results demonstrate that with the implementation of adaptive observers, the system becomes more flexible in responding to changes, significantly enhancing its level of efficiency. By employing dynamically changing verification specifications, the system achieves real-time and flexible verification. This research provides technical support for the safe, efficient, and reliable operation of electrical power distribution systems.

Collaborative scheduling method of active-reactive power for rural distribution systems with a high proportion of renewable energy

Large-scale distributed renewable energy connected to the rural distribution network has given birth to a new rural distribution system with a high proportion of new energy typical characteristics, and the optimal scheduling of the new rural distribution system has become an important issue to ensure the safe and stable operation of the power grid. This paper proposes a method of active-reactive power collaborative optimization scheduling for rural power distribution system with a high proportion of renewable energy. Firstly, the active support capability evaluation model is established, and the active power support capability and reactive power support capability of rural power distribution system are quantitatively evaluated, which provides data basis and boundary conditions for the scheduling part. Then, considering power-loss cost, distribution network operation cost, and penalty cost, a method of active-reactive power collaborative optimization scheduling for rural power distribution systems with a high proportion of renewable energy is proposed. Finally, the active support capability evaluation and regulation platform of the rural power distribution system is built to provide technical support services for the safe and stable operation of the rural power distribution system. Given the problems of overload and overvoltage faced by rural power distribution systems with a high proportion of renewable energy, this paper aims to solve the key technical challenges of optimization and regulation of new rural power distribution systems. The results show that the optimized control method proposed in this paper has better security and economy, and is conducive to promoting the construction and operation of the new rural power distribution system.

Distributionally robust PV planning and curtailment considering cyber attacks on electric vehicle charging under PV/load uncertainties

A manipulated charging behavior of electric vehicles (EVs) due to an adversary along with the uncertain photovoltaic (PV) generation outputs and loads may lead to unstable power distribution system operations with voltage and current violations. To resolve this issue, this paper proposes an optimization framework where the following two types of uncertainties are addressed: (i) the natural uncertainties of PV generation outputs/loads and (ii) artificial uncertainties of load altering attacks (LAAs) on EV charging stations (EVCSs) via the manipulation of EV charging control signals. The proposed framework is formulated as a Wasserstein metric-integrated distributionally robust optimization (DRO)-based Volt/VAR optimization (VVO) problem. The proposed DRO-based VVO framework combined with PV planning and curtailment aims to minimize substation energy and voltage imbalance along with the complete removal of the constraint violations while handling uncertain PV generation outputs/loads and LAAs. To use off-the-shelf optimization solvers, tractable reformulation of the chance constraints of the voltage, current, and curtailed PV real power of the original DRO problem is provided. Numerical examples tested over IEEE 13-bus and 37-bus systems with PV systems and EVCSs show the efficiency of the proposed DRO framework in terms of substation energy, voltage imbalance, and PV planning/curtailment cost under stochastic LAAs.

Strategies for Improving the Resiliency of Distribution Networks in Electric Power Systems during Typhoon and Water-Logging Disasters

Coastal cities often face typhoons and urban water logs, which can cause power outages and significant economic losses. Therefore, it is necessary to study the impact of these disasters on urban distribution networks and improve their flexibility. This paper presents a method for predicting power-grid failure rates in typhoons and water logs and suggests a strategy for improving network elasticity after the disaster. It is crucial for the operation and maintenance of power distribution systems during typhoon and water-logging disasters. By mapping the wind speed and water depth at the corresponding positions in the evolution of wind and water logging disasters to the vulnerability curve, the failure probability of the corresponding nodes is obtained, the fault scenario is generated randomly, and the proposed dynamic reconstruction method, which can react in real-time to the damage the distribution system received, has been tested on a modified 33-node and a 118-node distribution network, with 3 and 11 distribution generators loaded, respectively. The results proved that this method can effectively improve the resiliency of the distribution network after a disaster compared with the traditional static reconstruction method, especially in the case of long-lasting wind and flood disasters that have complex and significant impacts on the distribution system, with about 26% load supply for the 33-node system and nearly 95% for the 118-node system.

Modeling of high uncertainty photovoltaic generation in quasi dynamic power flow on distribution systems: A case study in Java Island, Indonesia

Integrating uncertainties associated with photovoltaic (PV) generation is an important aspect used to ensure the planning and operation of power distribution systems. Therefore, this research proposed an uncertainty model for PV generation by combining the methods of change point detection, cyclic k-means clustering (KMC), Monte Carlo simulation (MCS) with freedman diaconis estimator (FDE), and KMC with soft-dynamic time warping (DTW). Firstly, a seasonal split was performed using change point detection techniques and cyclic KMC to identify shifts in global horizontal irradiance (GHI) points. Secondly, the uncertainty of the GHI was generated using MCS for each season and the FDE method to optimize the number of bins of the data distribution. Finally, the PV generation curve from MCS was simplified through KMC with the soft-DTW metric, which facilitated a more straightforward representation of a PV profile. The impact of PV profile integration on quasi-dynamic power flow was tested on an IEEE 33 Bus system. The voltage profile on the feeder significantly impacted the PV integration, specifically during hours when high PV power is produced. For instance, at 11:00 a.m., voltage values on buses 18, 17, and 33 increased from 0.933, 0.934, and 0.935, respectively, to 0.982, 0.980, and 0.972. Similarly, with the value of power losses, the greater the PV power profile produced at a certain hour, the smaller the losses generated. The experimental results of the uncertainties PV model proposed in this research indicated that changes in electrical parameters change over time according to the input PV power profile produced.

Chance-Constrained Pre-Contingency Joint Self- Scheduling of Energy and Reserve in VPP

With the increasing penetration of primarily inertia-less distributed energy resources, allowable delay in the fast frequency reserve provision to ensure marginal stability condition can become comparable with the requisite dead-time for fast-switching in a radial power distribution system (PDS) connected to the transmission system. Consequently, the prevailing short-duration load-generation imbalance might propagate as system-wide frequency excursion in the future power system, which traditionally has not been observed. Furthermore, during the fast-switching, some of the fast-acting reserve (FAR) providing local generators remain inaccessible to the bulk power system (BPS). This work has answered the questions of requisite reserve requirements to cater to such events through efficient energy and FAR provision joint-scheduling. Stochasticity of fast-switching, with temporary faults as a use-case, requires modeling of load-generation imbalance and local resource unavailability, vis-á-vis FAR requirement problem, as a chance-constraint. Due to the limited visibility at the BPS level, PDS operators must ensure sufficient FAR availability for a given confidence level of the chance-constraint. Individual chance-constraints are used to ensure mathematical simplicity. Here, the PDS is operated as a virtual power plant (VPP), where the operator can procure resources locally or from the wholesale market with the aim of profit maximization before the contingency occurs. Results show that FAR requirements can influence the local energy schedule and raise energy costs. Furthermore, the confidence level of the chance-constraint can impact overall profitability, and FAR should be scheduled carefully. Comparative analysis with a modified benchmark IEEE 33-node radial test system and a 98-node test system shows the superior performance of the proposed approach. The impact of the limited available FAR is also demonstrated.

Synergetic renewable generation allocation and 5G base station placement for decarbonizing development of power distribution system: A multi-objective interval evolutionary optimization approach

The growing penetration of 5G base stations (5G BSs) is posing a severe challenge to efficient and sustainable operation of power distribution systems (PDS) due to their huge energy demand and massive quantity. To tackle this issue, this paper proposes a synergetic planning framework for renewable energy generation (REG) and 5G BS allocation to support decarbonizing development of future PDS. The problem is formulated as a multi-objective interval optimization (MOIO) model, wherein the 5G BS siting and sizing are co-optimized with the REG capacity allocation and their operating strategies to minimize the total economic cost and carbon emissions of the PDS. The potential flexibility benefits achievable from 5G BS operation (as responsive load demands to PDS) are explicitly considered in the proposed planning formulation by accounting for the effects of both transmit power control and on-site energy storage battery dispatch. Moreover, we adopt an interval approach to capture the uncertainties involved in the system (including REG output, market prices, carbon emissions factor, and power/communication load demands). In order to solve the proposed MOIO problem efficiently, a constrained interval multi-objective optimization evolutionary algorithm based on decomposition (CIMOEA/D) is developed by combining constraint handling, interval-based Tchebycheff aggregation function and interval ensemble comparison, which could achieve Pareto optimal solutions and an interval-formed Pareto front to the problem with all the uncertain information fully preserved in the decision-making process. The proposed model is demonstrated on a modified IEEE 33-bus test case, and the simulation results verified the effectiveness of the suggested approach.

Methodology for integrating flexibility into realistic large-scale distribution network planning using Tabu search

A successful energy transition will be firmly based on the effective integration of distributed energy resources and on the integration of new flexibility providers into the energy system. To make this possible, a deep transformation in the design, operation and planning of power distribution systems is required. Currently, a lack of comprehensive planning tools capable of supporting operators in their investment plan options exists. As a result, reinforcements of conventional grid assets are common solutions put in place. This paper proposes a methodology to obtain cost-optimal distribution network expansion plans, by modelling a single-stage distribution network planning tool, using conventional assets as well as flexibility contracting from demand response. A Tabu Search metaheuristic has been implemented in order to solve the optimization problem. A case study based on a realistic large-scale city network model is presented, for a planning horizon of ten years with significant load growth due to electromobility penetration. Results show that, in the case study analysed, the use of load flexibility in combination with conventional reinforcements can reduce the total expansion network cost by about 7.5 %. Furthermore, a sensitivity analysis on the cost of flexibility contracting is undertaken. Remarkably, the methodology presented generalises to further alternative solutions by providing a straightforward financial benchmark between the latter and conventional grid expansion.

Real-Time Coordinated Operation of Power and Autonomous Electric Ride-Hailing Systems

Integration of self-driving functions in electric vehicles is radically changing the transportation systems, and representing an opportunity for power utilities to develop innovative solutions for harnessing the spatio-temporal charging flexibility of autonomous electric vehicles (AEVs). This paper develops a multi-agent reinforcement learning model for intelligent real-time coordinated operation of interdependent autonomous electric ride-hailing system (AERS) and power distribution system (PDS), which enables the coordinated routing and charging of AEVs while ensuring the quality of service by meeting spatio-temporal passenger demand and regularly charging the batteries. The proposed model adopts a modified deep Q-network method with multi-agent rollout to determine the near-optimal solution for the coordinated operation problem that determines the routing and charging of numerous AEVs in real-time. The proposed model is tested on a 13-node transportation network, adopted from the Salt Lake City transportation system, and the IEEE 33-bus test power distribution system to showcase the efficiency of the proposed model in determining the real-time routing and charging decisions for AEVs while meeting the operational constraints of both AERS and PDS.

Application of signal processing techniques and intelligent classifiers for high-impedance fault detection in ensuring the reliable operation of power distribution systems

High-impedance fault (HIF) is always a threat and the biggest challenge in the power transmission and distribution system (PTDS). For a PTDS to operate effectively, HIF diagnosis is essential. However, given the HIF’s nature and the involved complexity, detection, identification, and fault location are difficult. This will be even more complicated in conventional PTDSs as they are inefficient and highly vulnerable. Given the importance and urgent need for HIF diagnosis in PTDS, this study reviews state-of-the-art HIF phenomenon and detection techniques and proposes the use of “various signal processing techniques for fault feature extraction” and “ different classifiers for identifying HIF.” First, HIF current/voltage signals are analyzed using signal processing techniques, which include the discrete wavelet transform (DWT), pattern recognition, Kalman filtering, TT transform, mathematical morphology (MM), S transform (ST), fast Fourier transform (FFT), principal component analysis (PCA), linear discriminant analysis (LDA), and wavelet transforms, such as dual-tree, maximum overlap discrete wavelet transform (MODWT), and lifting wavelet transform (LWT). Second, the various HIF and non-HIF faults are classified using intelligent classifiers. The intelligent classifiers include artificial neural networks (ANNs), probabilistic neural networks (PNNs), genetic algorithms (GAs), fuzzy logic, adaptive neuro-fuzzy interface system, support vector machine (SVM), extreme learning machine (ELM), adaptive resonance theory, random forests (RFs), decision trees (DTs), and convolution neural networks (CNNs). In addition to the comparative discussion of various classifier techniques, their evaluation criterion and performance are prioritized. Third, this review also studied different test systems, such as radial distribution network, mesh distribution network, IEEE 4 node, IEEE 13 node feeder, IEEE 34 node feeder, IEEE 39 node feeder, IEEE 123 node feeder, Palash feeder, and test microgrid systems, to assess the pertinence of various HIF detection schemes and the behavior along with methods to locate the HIF. Overall, we believe this review would serve as a comprehensive compendium of advanced techniques for HIF diagnosis in different test systems.

Decision-Making Method for Mine Cable Insulation Monitoring and Grounding Fault Diagnosis

Real-time monitoring of the power cable state has tremendous significance for ensuring the safe and economic operation of mine power distribution systems. However, due to the harsh conditions of underground coal mines, it is difficult for the cable monitoring system operating in underground coal mines to carry out large-scale calculations to diagnose the grounding fault of the cable. Additionally, there are many types of cable grounding faults, such as single grounding fault and two-phase ground fault. Therefore, how to determine the type of grounding fault quickly through effective calculations and alarms to select the grounding cable is always a difficult task. In this study, to reduce the complexity of cable insulation state classification, we develop a novel classification method based on the decision tree algorithm. Concerning the zero-sequence network under different insulation conditions, the first calculation’s positive and negative values were generated to identify whether the cable insulation was symmetrical. Then, the insulation degradation phase is identified by the relationship between the three-phase voltage phase angle and the current difference variation between the beginning and end of the line. By substituting the correlation quantities collected by a wide-area synchronous measurement system into different equations, the whole grid’s decision tree was constructed in different insulation states. Then, the insulation state of each line was evaluated according to the conductance value. The effectiveness of the proposed method was verified using a 35/6 kV mine power distribution system model based on the MATLAB/Simulink platform. The test results illustrate that the method can accurately diagnose the power cable insulation state based on the decision tree of whether the grid three-phase loads operate in an ungrounded mode or not.

Assessment of energy losses in power distribution systems with individual prosumers and energy communities

AbstractAchieving the energy transition target will require the extensive engagement of consumers and the private sector in investment and operation of renewable‐based power systems. Support of individual prosumers and proliferation of energy communities are efficient ways to implement this engagement. To facilitate smooth integration of prosumers with photovoltaic (PV) installations, while satisfying effective operation of the power distribution system (PDS), it is important to fundamentally assess energy losses for different grid development scenarios. In this paper, the energy losses are compared for two alternatives: (a) when prosumers with PV installations act as individual grid users, and (b) when prosumers become participants of a solar energy community. To achieve this goal, modeling of loads, prosumers’ behavior and PV generation was performed. Furthermore, it has been analyzed how the physical topology of the distribution network can be harmonized with the underlying bidirectional power flows for each alternative, while complying with system constraints. The IEEE 123 Node Test Feeder was employed to capture the performance of the PDS and to estimate the energy loss with various penetration levels of prosumers. The results indicate that reduction of energy losses is only possible until a certain PV penetration level, after which additional PV capacities cause increase in energy losses and complicate the PDS's operation. Lower energy losses were supervised for the cases with dispersed PV generation, compared to the cases focused on energy communities. This means that the PDS demonstrates better tolerance to evenly distributed prosumers, rather than to single sources of a larger scale installed in one node. The outputs of this study will help the energy planners and utilities in improving performance of the PDS in terms of loss minimization and loadability maximization.

Sieci elektryczne przedsiębiorstw energochłonnych: ochrona linii 6–10 kV i miejsc dostępu przed przepięciami

1. Asi et al. 2020 – Asi, D.K., Seifi, A.R., Rastegar, M. and Mohammadi, M. 2020. Optimal energy flow in integrated energy distribution systems considering unbalanced operation of power distribution systems. International Journal of Electrical Power & Energy Systems 121, DOI: 10.1016/j.ijepes.2020.106132. CrossRef Google Scholar

On the Application of Artificial Neural Network for Classification of Incipient Faults in Dissolved Gas Analysis of Power Transformers

Oil-submerged transformer is one of the inherent instruments in the South African power system. Transformer malfunction or impairment may interpose the operation of the electric power distribution and transmission system, coupled with liability for high overhaul costs. Hence, recognition of inchoate faults in an oil-submerged transformer is indispensable and it has turned into an intriguing subject of interest by utility owners and transformer manufacturers. This work proposes a hybrid implementation of a multi-layer artificial neural network (MLANN) and IEC 60599:2022 gas ratio method in identifying inchoate faults in mineral oil-based submerged transformers by employing the dissolved gas analysis (DGA) method. DGA is a staunch practice to discover inchoate faults as it furnishes comprehensive information in examining the transformer state. In current work, MLANN was established to pigeonhole seven fault types of transformer states predicated on the three International Electrotechnical Commission (IEC) combustible gas ratios. The designs enmesh the development of numerous MLANN algorithms and picking networks with the optimum performance. The gas ratios are in accordance with the IEC 60599:2022 standard whilst an empirical databank comprised of 100 datasets was used in the training and testing activities. The designated MLANN design produces an overall correlation coefficient of 0.998 in the categorization of transformer state with reference to the combustible gas produced.

Coordinated operation of pumped-storage hydropower with power and water distribution systems

Small pumped-storage hydropower (PSH) units have gained popularity as distributed energy storage options that can provide flexibility to the operation of power distribution systems. Optimal operation of small PSH units is not only dependent on the energy storage provided to power distribution system, but also on the inflow and outflow of water from and to the water distribution system. In this context, this paper develops an optimization model for coordinated operation of PSH units with power and water distribution systems. The proposed model optimizes the operation of water tanks, variable-speed pumps and PSH in pumping and generating modes to minimize the operation cost of power distribution system, while respecting the power flow constraints of power distribution and hydraulic constraints of water distribution system. Appropriate electricity tariffs are implemented to avoid additional expenses in water distribution system that can be enforced by its coordinated operation in favor of power distribution system. The proposed model is implemented on a 33-bus and a 123-bus test power distribution system connected to a 16-node test water distribution system. Results demonstrate the effectiveness of proposed model in tapping PSH flexibility to reduce the operation cost of power and water distribution systems, while meeting the power and water demands.

Integration of Solar Photovoltaic Distributed Generators in Distribution Networks Based on Site’s Condition

The significance of Distributed Generators (DGs) in the technical and economic operations of electric power distribution systems cannot be overemphasized in recent times. This is essential as a result of the incessant increase in electrical energy demand, which is becoming considerably difficult to meet with the conventional means of energy supply. Thus, DGs offer better alternatives for providing a quality supply of energy near the site of consumption. This type of energy supply is cleaner and cheaper most of the time due to the lessened transmission losses, which consequently reduced the cost of operation at the transmission and distribution levels of the power system. In this work, an approach for placement and sizing of solar PV DGs into radial distribution networks (RDN) based on the solar PV capacity factor of the site was analyzed using particle swarm optimization. The aim of this study is to analyze the effect of the approach on the real and reactive power losses within the network as well as the bus voltage profile. Constraints on credible system operation parameters, which includes bus voltage limits, power balance, and power flow limits, are considered in the formulation of the optimization problem. In order to verify the viability of the deployed approach, steady-state performance analyses were executed on IEEE 33-bus RDN; and the results obtained were compared with the results from other approaches reported in the literature.

Joint optimization of Volt/VAR control and mobile energy storage system scheduling in active power distribution networks under PV prediction uncertainty

Mobile energy storage systems (MESSs) are becoming crucial devices to maintain stable power distribution system operations under the operation of voltage regulators including on-load tap changers, capacitor banks, and smart inverters of photovoltaic (PV) systems. This study proposes a joint framework in which the operation of voltage regulators and the road routing of MESSs are co-optimized for Volt/VAR control (VVC) in both power distribution and transportation networks. The proposed framework aims to minimize real power loss and voltage deviation along with peak shaving in power distribution networks, and to reduce the MESS traveling cost in transportation networks. Under the uncertainty of the predicted error of the PV generation output, the proposed framework is formulated as a chance-constrained optimization problem using mixed-integer linear programming. The proposed joint optimization algorithm was simulated in the IEEE 13-bus and IEEE 33-bus systems, which were coupled with 9-node and 15-node transportation systems, respectively. The results show that, in contrast with a VVC method without MESS, the proposed algorithm using MESS reduces the total real power loss and peak load by 9.7% and 15.92%, respectively, in the IEEE 13-bus system, and 2.55% and 3.47%, respectively, in the IEEE 33-bus system.

A Heuristic Methods-Based Power Distribution System Optimization Toolbox

This paper proposes a toolbox for simulating the effective integration of renewable energy sources into distribution systems. The toolbox uses four heuristic methods: the particle swarm optimization (PSO) method, and three recently developed methods, namely Gray Wolf Optimization (GWO), Ant Lion Optimization (ALO), and Whale Optimization Algorithm (WOA), for the efficient operation of power distribution systems. The toolbox consists of two main functionalities. The first one allows the user to select the test system to be solved (33-, 69-, or 141-bus test systems), the locations of the distributed generators (DGs), and the voltage regulators. In addition, the user selects the daily active power output profiles of the DGs, and the tool solves the voltage deviation problem for the specified time of day. The second functionality involves the simulation of energy storage systems and provides the optimal daily power output of the resources. With this program, a graphical user interface (GUI) allows users to select the test system, the optimization method to be used, the number of DGs and locations, the locations and number of battery energy storage systems (BESSs), and the tap changer locations. With the simple user interface, the user can manage the distribution system simulation and see the results by making appropriate changes to the test systems.

A collaborative voltage optimization utilizing flexibility of community heating systems for high PV penetration

The increasing penetration of photovoltaic generation exacerbates the risk of voltage violations in distribution networks. One viable tactic for increasing the flexibility of regional distribution networks is to use the thermal inertia of community heating systems (CHSs). However, the slow heat dynamics in the heating network brings difficulties in synchronous joint operation decisions of the integrated system. In this paper, we propose a novel power-to-temperature sensitivity model (PTSM), equating the whole CHS to a flexible electric load with thermodynamic characteristics. It denotes the direct relationship between the average water temperature change and the electric power consumed by ground-source heat pumps (GSHPs). A robust collaborative voltage optimization model is then developed from the perspective of power distribution system operators. The PTSM provides accurate prediction of CHS temperature variations to construct the operation constraints of GSHPs. This mixed integer programming problem is solved by the column-and-constraint generation algorithm. The test results of an actual residential community demonstrate that the PTSM can ensure the prediction errors of the CHS average temperature within ±0.4 °C in case of the optimal time resolution and aggregated heating load location. An IEEE 69-bus distribution network with several CHSs is studied to show that, the flexibility of CHSs can achieve an extra 17.25% reduction of the daily voltage deviation. The proposed method has significant advantages in computation time and robustness.

Optimal Energy Management Framework for Truck-Mounted Mobile Charging Stations Considering Power Distribution System Operating Conditions.

A high charging demand from many electric vehicles (EVs) at a fixed charging station (FCS) with a limited number of charging poles can increase the waiting time of EVs and yield an abnormal power grid condition. To resolve these challenges, this paper presents an optimization framework in which a mobile charging station (MCS) is dispatched to the overloaded FCS to reduce the number of waiting EVs while maintaining normal power grid operation. Compared to existing MCS scheduling methods that do not consider actual power distribution system operations, the proposed framework takes into account the (i) active/reactive power flow and consumption of EVs, (ii) reactive power capability of MCS, and (iii) voltage quality in power distribution systems. In coupled transportation and power distribution systems, the proposed algorithm conducts optimal operation scheduling of MCS for both road routing and charging and discharging, thereby leading to the reduction of waiting EVs within the allowable voltage range. The proposed MCS optimization algorithm was tested in IEEE 13-bus and 33-bus distribution systems coupled with 9-node and 15-node transportation systems, respectively. The test results demonstrate the effectiveness of the proposed algorithm in terms of number of waiting EVs, voltage magnitude deviation, and reactive power of the MCS.