Power Distribution System Research Articles

Key technologies of active distribution networks control

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

Assessment of topological boundaries for microgrids in an active power distribution network during emergencies

Microgrid (MG) operation has become an intriguing possibility to enhance the sustainability, reliability, and resilience of electricity distribution systems in emergencies. However, identifying suitable physical boundaries of MGs from an active power delivery infrastructure is a highly challenging task. This study describes a novel approach for determining the topological boundary of multiple MGs to restore power supply in a power distribution system during the unavailability of grid power supply. This method tries to enhance the self-adequacy of the MGs by increasing power flow within the MG boundaries. In this context, possible demand at the nodes of the power delivery infrastructure during an emergency is anticipated by an autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) network-based time series forecast models. A modified backward-forward sweep (MBFS) power flow analysis, tailored for islanded mode, is employed to assess the power flow status in the MGs during emergencies. The MGs are supplied with locally available dispatchable distributed generators (DDGs). A particle swarm optimization (PSO) algorithm intertwined with the MBFS power flow method has been employed to determine the boundary of MGs by identifying minimum power flow line in the shortest path between the consecutive DDGs. Further, this study demonstrates the evaluation of the shortest path between paths between the consecutive DDGs with the help of a breadth-first search (BFS) algorithm. The proposed strategy has been tested on a modified 33-bus, 69-bus and 118-bus power distribution test systems. The experimented results are verified with the results obtained from similar existing approaches to explore the opportunities in the proposed methodology.

Virtual Inertia Implemented by Quasi-Z-Source Power Converter for Distributed Power System

This paper proposes a novel virtual inertia control strategy for distributed power systems with high penetration of renewable energy sources. The strategy uses a quasi-Z-source power converter to emulate the inertia response of a synchronous generator by regulating the DC-link capacitor voltage in proportion to the grid frequency deviation. This paper analyzes the effect of inertia on the frequency regulation of a single-area power system and derives the parameter design method and limitations of the virtual inertia. The paper also introduces the working principle and modulation technique of the quasi-Z-source power converter and presents the virtual inertia control scheme based on a voltage-frequency controller. The paper verifies the feasibility and effectiveness of the proposed strategy through MATLAB/Simulink simulations and dSPACE semi-physical experiments. The results show that the proposed strategy can reduce the frequency deviation and rate of change of frequency (RoCoF) by 20% and 50%, respectively, under load disturbances. The paper demonstrates that the quasi-Z-source power converter can provide flexible and adjustable virtual inertia for distributed power systems without additional energy storage devices.

Developing an improved chameleon swarm algorithm for combined cooling, heating and power micro-grid system

Combined cooling, heating, and power system has been widely applied in the micro-grid system. However, the micro-grid system reliability and safety may be adversely affected when renewable energy resources are connected to the grid. This has made the power distribution system more challenging to manage for grid operators. The proposed energy optimization management model aims to maintain a stable and reasonable power grid distribution due to biogas power, photovoltaic power, and micro gas turbine involvement to realize better utilization. For this reason, the improved chameleon swarm algorithm is developed to solve the optimal operation cost of the system by integrating different improvement strategies. Therefore, the operation strategy can achieve both economic and environmental costs more effectively. It is verified by the objective function constructed by the economic cost, environmental cost, and renewable energy utilization. Based on the specific arithmetic analysis, the use of the strategy proposed in this study can reduce the economic cost by 1.67% in summer, 6.89% in winter, and 3.42% in transition season compared to the traditional strategy, which confirms the operational cost-effectiveness of the combined cooling, heating, and power micro-grid system in different scenarios.

A Review on Automatic Control in Power System

Power systems are the foundation of contemporary society, providing energy for businesses, homes, and technological advancements. Precision, adaptability, and reliability are essential for these systems' complicated network of parts to satisfy the rising needs of our energy-hungry society. In this review paper, the vital function of automatic control systems in power systems is examined, along with how very important they are to maintaining stability, dependability, and sustainability. In the opening section of the essay, the basic building blocks of power systems generators, transformers, transmission lines, distribution networks, substations, and control centers are explained. The complex interactions between these components highlight how dynamic and complicated power systems are, posing several problems that need for clever control measures. Different control methods are examined, from conventional ones like proportional-integral-derivative (PID) control to cutting-edge ones like model predictive control and adaptive control methods. Each approach is evaluated for its unique benefits and innate weaknesses, providing a thorough grasp of its applicability in various operational settings. The command and control of the current power grid depends on Wide-Area Monitoring and Control (WAMS) systems, which are made possible by synchrophasor technology and reliable communication networks. Grid operators have the tools they need to maintain stability and respond quickly to emergencies thanks to their capacity to offer real-time data and improve situational awareness.

Optimal Location and Sizing of Photovoltaic-Based Distributed Generations to Improve the Efficiency and Symmetry of a Distribution Network by Handling Random Constraints of Particle Swarm Optimization Algorithm

Distributed generators (DGs) are increasingly employed in radial distribution systems owing to their ability to reduce electrical energy losses, better voltage levels, and increased dependability of the power supply. This research paper deals with the utilization of a Particle Swarm Optimization algorithm by handling its random constraints to determine the most appropriate size and location of photovoltaic-based DG (PVDG) to keep the asymmetries of the phases minimal in the grid. It is thus expected that this algorithm will provide an efficient and consistent solution to improve the overall performance of the power system. The placement and sizing of the DG are done in a way that minimizes power losses, enhances the voltage profile, i.e., bringing symmetry in the voltage profile of the system, and provides maximum cost savings. The model has been tested on an IEEE 33-bus radial distribution system using MATLAB software, in both conditions, i.e., with and without PVDG. The simulation results were successful, indicating the viability of the proposed model. The proposed PSO-based PVDG model further reduced active power losses as compared to the models based on the teaching–learning artificial bee colony algorithm (TLABC), pathfinder algorithm (PFA), and ant lion optimization algorithm (ALOA). With the proposed model, active power losses have reduced to 17.50%, 17.48%, and 8.82% compared to the losses found in the case of TLABC, PFA, and ALOA, respectively. Similarly, the proposed solution lessens the reactive power losses compared to the losses found through existing TLABC, PFA, and ALOA techniques by an extent of 23.06%, 23%, and 23.08%, respectively. Moreover, this work shows cost saving of 15.21% and 6.70% more than TLABC and ALOA, respectively. Additionally, it improves the voltage profile by 3.48% of the power distribution system.

Multi-functional energy storage system for supporting solar PV plants and host power distribution system

Energy storage systems (ESS) will play a critical role in the ongoing development of the future electrical grid, especially as penetration of renewable energy generation increases. Since the costs of ESS are still high, it is imperative to research diverse control modes of ESS so as to use them in an effective manner, thereby offsetting their high costs. ESS technologies can diminish curtailment of renewable generators and provide much needed storage capabilities for supporting the grid, such as providing voltage regulation, relieving congestion, and improving power quality. This study develops six control modes for a battery ESS (BESS), namely, Current Limiting, Power Limiting, Load Leveling, Voltage Regulation, Power Factor Correction, and Simultaneous Real and Reactive Power Supply. The control modes are verified by simulation using a realistic utility 2.8-MW/5.6-MWh BESS and three solar PV plants connected to a power distribution grid. The study results demonstrate that the BESS functions properly in all the control modes. It can be used in all four quadrants of real and reactive power, i.e., it can provide any combination of real and reactive power amount within its rating for supporting the solar PV farms and the grid. The control modes enable effective operation of the BESS, which is a new tool to solve power system critical operational issues, thereby helping ensure the grid resilient, efficient, and reliable operation.

Collaborative Service Restoration with Network Reconfiguration for Resilience Enhancement in Integrated Electric and Heating Systems

Coordinated fault recovery is essential for the resilience enhancement of integrated electric and heating systems (IEHS) following natural catastrophes as the linkage of the power distribution system (PDS) and district heating system becomes tighter. DHS reconfiguration is a viable method for service restoration because it could adjust the energy between energy sources and achieve uninterrupted energy supplies. In this paper, a collaborative service restoration model considering DHS reconfiguration is proposed to achieve better recovery after natural disasters. DHS reconfiguration could guarantee interrupted power supply in non-fault regions by shifting electric loads between power sources and accomplish optimal service restoration by adjusting the power output of combined heat and power units. Numerous case studies are undertaken to demonstrate the performance of coordinated reconfiguration on resilience enhancement and to confirm the efficacy of the proposed paradigm.

A Mathematical Model of an Automated Control System for Heat Regulation in a Building

In this study, a mathematical model of an automated control system for heat regulation in a building was developed. A method of mathematical modeling of the centralized heating control system based on mathematical models of distributed power systems and experimental studies has been developed, which allows for determining the parameters of the coolant when the outdoor temperature changes, qualitative regulation of heat in autonomous sources, quantitative regulation in automated individual heating devices, etc. The method makes it possible to study the interaction of an automated individual heating point to increase the efficiency of the management of distributed power systems of buildings. As a result of studying the controller created by the R2 control unit, for the PI controller, the calculated heat consumption of the building imperceptibly increases from 1.08502 kJ to 1.085888 kJ when an oscillatory transient occurs, and for the I controller, the calculated heat consumption of the building remains at the same level. The level, as for the PI controller, increases slightly during the oscillatory transition from 1.08456 GJ to 1.08535 GJ.

Cyber Security for Power Distribution System

Electricity distribution utilities in India are facing major challenges such, as higher revenue realization gap and AT&Closses. One of the ways to overcome this challenge is by adoption of smart technologies and automating the networkfor increased visibility, monitoring and automated control. The adoption of smart technologies merges the conventionalpower distribution system with modern communicable devices having sensing and control features. The use of suchmodern communicable devices makes power systems vulnerable to cyber-attack. This paper presents the SCADA systemarchitecture for power distribution systems, NIST guidelines and framework, cyber-security for power systems andstandards for security implementation for telecontrol equipment and systems.

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.

Multi-scenario design of ammonia-based energy storage systems for use as non-wires alternatives

Energy storage can be used by power distribution system operators as a non-wires alternative to defer infrastructure upgrades and improve feeder reliability. One emerging energy storage technology is energy storage via the synthesis and subsequent consumption of chemicals in internal combustion engines or fuel cells (i.e., ”chemical energy storage”). Some chemicals, such as hydrogen and ammonia, can be synthesized from renewable, carbon-free feedstocks using excess renewable generation. In this work, we develop an optimization model for investing in and using mobile ammonia-powered generators, ammonia storage equipment, and mobile batteries in tandem to provide non-wires alternatives to distribution networks efficiently. Specifically, we develop a mixed-integer quadratically constrained program to optimize the design and operation of distribution systems with ammonia and battery energy storage devices under multiple operational scenarios. This formulation is applied in a case study on a 15-bus test system. Ultimately, we find that ammonia-powered generators enable flexible operation and are best suited for long-term, high-total-energy use cases, whereas batteries are best suited for short-term use cases. Additionally, we find that a combination of chemical and electrochemical energy storage is more cost effective at responding to a variety of operational conditions than either form of energy storage alone.

Sequential Load Restoration With Soft Open Points and Time-Dependent Cold Load Pickup for Resilient Distribution Systems

Global changes in climate conditions and increasing number of weather-related disasters in the world have elevated the quest for better load restorations in power distribution systems. This paper proposes a distribution system restoration (DSR) model where non-exponential time-dependent cold load pickup (CLPU) characteristics and soft open point operations are simultaneously addressed. We develop a time-dependent CLPU model for attaining a more realistic and secure DSR in which we demonstrate that the traditional delayed exponential model (DEM) for a long outage duration is a special case of the adopted CLPU model. The current soft open point (SOP) models would typically relax the operation constraints without devising any tightening procedures, which might lead to inaccurate or even infeasible SOP states. In this paper, we analyze the reasons traditional SOP constraints would lead to unsecure operations and present a set of novel SOP operation constraints for recovering bindings. Also, we adopt the sequential second order cone programming (SSOCP) algorithm to establish the sequential framework of DSR and solve the proposed model effectively. Our presented case studies discuss the rationality of the proposed DSR model and algorithm.

Optimal Design and Configuration Strategy for the Physical Layer of Energy Router Based on the Complex Network Theory

The energy router (ER) is key to realizing the coordinated management and efficient utilization of multiple forms of energy in Energy Internet. This paper proposes a novel design and configuration method for the physical layer of ER by using complex network theory. Firstly, an abstract model of the physical layer of ER is introduced according to its function, and important modules in the model are illustrated in details. Secondly, based on the electrical characteristics of ports, the community structure of the power networks inside ER is analyzed by the improved Girvan-Newman (GN) algorithm to design the independent bus systems (IBSs) and generate the network topology of the physical layer. Then, the optimization model of equipment configuration of the power supply and distribution systems of ER is developed considering the economy, utilization efficiency, and power supply reliability. Finally, two case studies demonstrate that the proposed strategy can effectively accomplish the module-level structure design and device configuration for the physical layer of ER.

Optimize single line to ground fault detection in distribution grid power system using artificial bee colony

The most common power system (PS) distribution network fault, single lineto-ground fault (SLGF), causes residual current (I res) to start an electrical arc and high voltage (HV) three times the rated voltage in other healthy phases. HV from capacitive currents (IC) damages cable insulation and PS appliances. Peterson The neutral point coil (PC) reduces (I res) and extinguishes the electric arc, but the fault current (I fault) remains below the protection devices' threshold. Operations and equipment are riskier. PC adaptive eliminates electrical arcs, making the network safer. This paper detects I faults online using Texas instrument validation in MATLAB and adaptive by artificial bee colony (ABC). This paper discusses Texas instrument fault current detection and MATLAB validation. It improves system reliability, device protection, and copper savings by thousands of tons. ABC intelligently optimizes many mathematical problems. ABC with network neural artificial intelligence (AI) improves algorithm performance (artificial bee colony network neural (ABCNN)). This new method may improve distribution network SLGF detection. This first work can work online in electrical power stations by building the (eZdsp F28335-RS232) into the program to send fault signals to the control when SLGF occurs without damaging devices, equipment, cables, or power outages.

Study on Energy Control for Low-Voltage DC Distribution Systems

This paper focuses on the study of energy control for LVDC distribution systems based on maintaining a stable DC bus voltage. The system includes photovoltaic (PV) arrays, energy storage, and AC/DC loads. The Boost converter is used in the PV array to switch freely between MPPT and constant voltage modes. The MPPT control is applied to the PV modules to ensure maximum power output, and the energy storage unit adjusts the output to stabilize the DC bus voltage and maintain system power balance. The implementation of the proposed control strategy is in a MATLAB/Simulink simulation model to verify its correctness and effectiveness. The simulation results show that the control strategy can maintain a stable DC bus voltage, and improve the efficiency of LVDC system. The study provides valuable insights into the development of efficient and reliable energy control strategies for LVDC power distribution systems.

Detection of Bad Data and Estimation of Missing Parameter Values Using System Synergism

Communication infrastructure is paramount for the optimal control and protection of modern-day distribution systems. However, inherent problems associated with the communication systems such as loss of data packets due to excessive latency and erroneous signal induction result in impairment of the network control and relaying signals. In the present article, a method based on Data Mining is proposed for the detection of Bad/Noisy data. Furthermore, the concept of system synergism is utilized to estimate the value of the signal in which Bad Data is detected. In case of Bad measurements during faults/ transients or load switching, the estimates are observed using Machine Learning (ML) based regression model. XGboost has been found to have the best performance among the ML models. Modbus is considered the communication protocol for the power distribution system under the consideration. Test results are demonstrated using a standard IEEE 33 bus test system. The feasibility of the proposed algorithm in real-time is confirmed using the raspberry pi 3B+ controller using Serial Peripheral Interface (SPI) protocol.

Precise Fault Location in Active Distribution Systems Using Unsynchronized Source Measurements

This article presents a phase-domain based fault location algorithm for unbalanced power distribution systems with distributed generation. Unsynchronized voltage and current measurements, taken from the sources prior and during the fault, are utilized to calculate the equivalent source impedances and the incremental current quantities required. The latter are substituted into the fault location equations, which are solved to determine the fault resistance(s), the synchronization angles, and the possible fault positions. Multiple possible fault position estimations are eliminated by applying an index-based methodology to find the exact fault position. Extensive simulations are performed for a benchmark distribution system with conventional and inverter-interfaced generation units, considering all types of common short-circuit faults, several fault resistance magnitudes, varying load levels, different distributed generation (DG) mixture, and the impact of line segment length. The results show that fault distance is accurately estimated by the proposed algorithm.

Generalized Wardrop Equilibrium for Charging Station Selection and Route Choice of Electric Vehicles in Joint Power Distribution and Transportation Networks

This paper presents the equilibrium analysis of a game composed of heterogeneous electric vehicles (EVs) and a power distribution system operator (DSO) as the players, and charging station operators (CSOs) and a transportation network operator (TNO) as coordinators. Each EV tries to pick a charging station as its destination and a route to get there at the same time. However, the traffic and electrical load congestion on the roads and charging stations lead to the interdependencies between the optimal decisions of EVs. CSOs and the TNO need to apply some tolling to control such congestion. On the other hand, the pricing at charging stations depends on real-time distributional locational marginal pricing, which is determined by the DSO after solving the optimal power flow over the power distribution network. This paper also takes into account the local and the coupling/infrastructure constraints of EVs, transportation and distribution networks. This problem is modeled as a generalized aggregative game, and then a decentralized learning method is proposed to obtain an equilibrium point of the game, which is known as variational generalized Wardrop equilibrium. The existence of such an equilibrium point and the convergence of the proposed algorithm to it are proven. We undertake numerical studies on the Savannah city model and the IEEE 33-bus distribution network and investigate the impact of various characteristics on demand and prices.

Distribution Power System Protected by Petersen Coil: Detection of Single Line to Ground Fault Using Deep Learning

Distribution Power System Protected by Petersen Coil: Detection of Single Line to Ground Fault Using Deep Learning