IEEE 33-bus Distribution System Research Articles

Optimal Probabilistic Allocation of Photovoltaic Distributed Generation: Proposing a Scenario-Based Stochastic Programming Model

The recent developments in the design, planning, and operation of distribution systems indicate the need for a modern integrated infrastructure in which participants are managed through the perceptions of a utility company in an economic network (e.g., energy loss reduction, restoration, etc.). The penetration of distributed generation units in power systems are growing due to their significant influence on the key attributes of power systems. As a result, the placement, type, and size of distributed generations have an essential role in reducing power loss and lowering costs. Power loss minimization, investment and cost reduction, and voltage profile improvement combine to form a conceivable goal function for distributed generation allocation in a constrained optimization problem, and they require a complex procedure to control them in the most appropriate way while satisfying network constraints. Such a complex decision-making procedure can be solved by adjusting the dynamic optimal power flow problem to the associated network. The purpose of the present work is to handle the distributed generation allocation problem for photovoltaic units, attempting to reduce energy and investment costs while accounting for generation unpredictability as well as load fluctuation. The problem is analyzed under various scenarios of solar radiation through a stochastic programming technique because of the intense uncertainty of solar energy resources. The formulation of photovoltaic distributed generation allocation is represented as a mixed-integer second-order conic programming problem. The IEEE 33-bus and real-world 136-bus distribution systems are tested. The findings illustrate the efficacy of the proposed mathematical model and the role of appropriate distributed generation allocation.

Inverter reliability-constrained Volt/Var optimization control of distribution network with high-level PV-storage generation

The involvement of renewable energy inverters in regulating the reactive voltage of the distribution network is an efficient approach to enhance the operational security and reliability of high-penetration renewable energy distribution networks. Nonetheless, the reactive power assistance offered by renewable energy inverters leads to an elevation in the maximum junction temperature of photovoltaic inverters and intensifies temperature fluctuations, which will subsequently impact the reliable and secure operation of both the inverters and the distribution network. In order to address this issue, this paper introduces a control strategy for optimizing reactive power and voltage in photovoltaic-storage (PV-storage) distribution networks with significant penetration, taking into account the constraint of IGBT junction temperature. Firstly, the IGBT junction temperature is calculated by the CatBoost algorithm, which improves the efficiency of IGBT junction temperature calculation and avoids the dependence of the traditional junction temperature algorithm on IGBT thermal model parameters. Then, a multi-objective reactive power optimization model of an active distribution network considering IGBT junction temperature constraint is established. The maximum output power of PV-storage power supply under IGBT junction temperature constraint is solved by dichotomy, so the transformation from IGBT junction temperature constraint to output apparent power second-order cone constraint is realized. Finally, the efficiency of the suggested approach is validated using the IEEE 33-bus typical distribution system. At the same time, the setting principle of IGBT junction temperature limit of PV-storage power supply considering the network loss of distribution network and the reliability of PV-storage power supply is proposed.

A distributionally robust approximate framework consider CVaR constraints for energy management of microgrid

As the penetration of intermittent renewable energy increases in microgrid systems, flexible power generation resources cause the power imbalance problem. To improve the absorption of renewable energy, this paper proposes a distributionally robust (DR) approximate framework considering conditional value-at-risk measures for energy management of microgrids based on cooperative scheduling energy storage and direct load control operations. First, combined with CVaR and DR theory, DR CVaR constraints that can quantitatively the power balance risk of microgrid is constructed. Then, to improve the efficiency of solving DR CVaR constraints, it is tractably approximated using Jensen’s​ inequality, and the approximate error of DR CVaR constraint is analyzed by comparing two different Jensen’s inequality gap expressions so that the decision maker can select the appropriate approximate DR CVaR constraint according to the actual needs. Finally, the appropriate DR CVaR framework of the microgrid is transformed into mixed-integer linear programming that can be directly solved by CPLEX. The approximate framework has the characteristics of risk aversion measurement, linear efficient solving, and flexible decision-making. And it is also verified on the IEEE 33-bus distribution system through a wide range of different tests.

Fault Reconfiguration in Distribution Networks Based on Improved Discrete Multimodal Multi-Objective Particle Swarm Optimization Algorithm.

Distribution network reconfiguration involves altering the topology structure of distribution networks by adjusting the switch states, which plays an important role in the smart grid since it can effectively isolate faults, reduce the power loss, and improve the system stability. However, the fault reconfiguration of the distribution network is often regarded as a single-objective or multi-objective optimization problem, and its multimodality is often ignored in existing studies. Therefore, the obtained solutions may be unsuitable or infeasible when the environment changes. To improve the availability and robustness of the solutions, an improved discrete multimodal multi-objective particle swarm optimization (IDMMPSO) algorithm is proposed to solve the fault reconfiguration problem of the distribution network. To demonstrate the performance of the proposed IDMMPSO algorithm, the IEEE33-bus distribution system is used in the experiment. Moreover, the proposed algorithm is compared with other competitors. Experimental results show that the proposed algorithm can provide different equivalent solutions for decision-makers in solving the fault reconfiguration problem of the distribution network.

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.

Node Vulnerability-Aware co-deployment of D-PMUs and FTUs for active distribution networks

This study proposes a co-deployment strategy of distribution-level phasor measurement units (D-PMUs) and feeder terminal units (FTUs) for active distribution networks (ADNs) considering node vulnerability to efficiently monitor and perceive the operational state of ADNs. The proposed measurement deployment strategy preferentially configures measurement devices with high precision and superior monitoring functions on vulnerable nodes while ensuring state estimation (SE) accuracy and cost economy. Based on complex network theory and risk assessment, a vulnerability index reflecting the “bridge” characteristics of nodes was defined. Considering the system monitorability, SE accuracy, and measurement cost, a measurement co-deployment strategy based on a combination weighting method was developed. An improved discrete particle swarm optimization algorithm was proposed to solve the co-deployment model by establishing a new particle coding format, improving the dynamic inertia weight and learning factors, and introducing chaos theory. Simulation results based on the IEEE 33-bus and 119-bus distribution systems verify the effectiveness and show that the proposed strategy not only improves system monitorability but also enhances SE robustness.

A Bi-Level Decision Framework for Incentive-Based Demand Response in Distribution Systems

In a growing retail electricity market, demand response (DR) is becoming an integral part of the system to enhance economic and operational performances. This is rendered as incentive-based DR (IBDR) in the proposed study. It presents a bi-level decision framework under the ambit of multiple demand response providers (DRPs) in the retail competition. It is formulated as a multi-leader-multi-follower game, where multiple DRPs, as the DR stakeholders, are strategically interacting to optimize load serving entity cost at the upper level, and individual DRP as the aggregated customers is optimizing its cost at the lower level. The strategic behavior of DRPs is modeled in a game-theoretic framework using a generalized Stackelberg game. Further, the existence and uniqueness of the game are validated using variational inequalities. It is presented as a nonlinear problem to consider AC network constraints. An equilibrium problem with equilibrium constraints is used as a mathematical program to model the multi-leader-multi-follower, bi-level problem, which is simultaneously solved for all DRPs. The diagonalization method is employed to solve the problem. The detailed numerical analyses are conducted on IEEE 33-bus test and Indian-108 bus distribution systems to demonstrate the applicability and scalability of the proposed model and the suggested method.

Multi-objective random-fuzzy optimal power flow of transmission-distribution interaction considering security region constraints

In order to deal with the static voltage stability region problem of the power system caused by the penetration of distributed energy resources, this paper presents an interactive random-fuzzy optimal power flow method for transmission-distribution network considering security region constraints. Firstly, based on the random-fuzzy theory, the output of wind power and multi-energy coupling energy hub connected to transmission-distribution network are obtained. On this basis, the security region model of transmission-distribution network is established. As the interaction and cooperation between multiple distribution networks and transmission network throughout the system are described, the global random-fuzzy model of transmission-distribution interaction is established. Then, the security domain opportunity constraints are incorporated into the multi-objective optimization model of transmission-distribution network. Finally, based on the improved IEEE30 bus transmission system and IEEE33 bus distribution system, the power flow of transmission-distribution interactive network under the proposed model is calculated. The simulation results validate the effectiveness of the proposed model and algorithm. The safe operation distance of the power grid system can be improved and the active power network loss and power generation cost can be reduced.

A fuzzy-based fault section identification method using dynamic partial tree in distribution systems

In a distribution management system (DMS), fault section identification (FSI) is an essential function for the self-healing capability of smart distribution networks. Conventional FSI methods based on the flag of fault indicators (FIs) cannot be properly utilized for distribution networks with distributed energy resources (DERs) since false FIs, unintentionally activated by fault currents flowing from the downstream of a fault location due to the presence of wye-delta transformers for the DER interconnection, are expected to be generated. For more accurate FSI, this paper proposes a fuzzy-based FSI method using the fault current information measured on feeder remote terminal units (FRTUs) installed at automatic switches in distribution lines (DLs). The proposed method takes advantage of the fuzzy evaluation to provide an intuitive index for decision-making by quantifying the possibility of fault for each candidate section. Furthermore, to achieve faster FSI by reducing waiting time for FSI, the partial trees using only validated FIs with fault current information for the faulty DL are dynamically generated, thereby enabling the fault section can be identified regardless of whether the fault current information from every FRTU is available or not. Case studies on IEEE 33-bus distribution system considering various fault conditions are conducted and simulation results show that the proposed method can accurately identify correct fault sections under false FI and communication failure situations with the improved time required for the FSI.

Distribution System State Estimation Using Hybrid Traditional and Advanced Measurements for Grid Modernization

Distribution System State Estimation (DSSE) techniques have been introduced to monitor and control Active Distribution Networks (ADNs). DSSE calculations are commonly performed using both conventional measurements and pseudo-measurements. Conventional measurements are typically asynchronous and have low update rates, thus leading to inaccurate DSSE results for dynamically changing ADNs. Because of this, smart measurement devices, which are synchronous at high frame rates, have recently been introduced to enhance the monitoring and control of ADNs in modern power networks. However, replacing all traditional measurement devices with smart measurements is not feasible over a short time. Thus, an essential part of the grid modernization process is to use both traditional and advanced measurements to improve DSSE results. In this paper, a new method is proposed to hybridize traditional and advanced measurements using an online machine learning model. In this work, we assume that an ADN has been monitored using traditional measurements and the Weighted Least Square (WLS) method to obtain DSSE results, and the voltage magnitude and phase angle at each bus are considered as state vectors. After a period of time, a network is modified by the installation of advanced measurement devices, such as Phasor Measurement Units (PMUs), to facilitate ADN monitoring and control with a desired performance. Our work proposes a method for taking advantage of all available measurements to improve DSSE results. First, a machine-learning-based regression model was trained from DSSE results obtained using only the traditional measurements available before the installation of smart measurement devices. After smart measurement devices were added to the network, the model predicted traditional measurements when those measurements were not available to enable synchronization between the traditional and smart sensors, despite their different refresh rates. We show that the regression model had improved performance under the condition that it continued to be updated regularly as more data were collected from the measurement devices. In this way, the training model became robust and improved the DSSE performance, even in the presence of more Distributed Generations (DGs). The results of the proposed method were compared to traditional measurements incorporated into the DSSE calculation using a sample-and-hold technique. We present the DSSE results in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values for all approaches. The effectiveness of the proposed method was validated using two case studies in the presence of DGs: one using a modified IEEE 33-bus distribution system that considered loads and DGs based on a Monte Carlo simulation and the other using a modified IEEE 69-bus system that considered actual data for loads and DGs. The DSSE results illustrate that the proposed method is better than the sample-and-hold method.

Using Deep Transfer Learning Technique to Protect Electrical Distribution Systems Against High-Impedance Faults

The dependence of high-impedance faults (HIFs) detection methods on a large amount of training data has always been a fundamental problem in electrical distribution systems. This article proposes a novel protection system based on the transfer learning technique and GoogleNet architecture to reduce this dependence. The proposed protection system uses a small amount of data to extend the knowledge of pretrained GoogleNet architecture to the HIF detection problem. In this system, a small amount of third harmonic angle data of the current at the measurement point are obtained from the understudy electrical distribution system. Then, the preprocessing phase is performed, and the extracted data are converted to image data using the Wigner–Ville distribution. Afterward, these converted images are fed to the GoogleNet architecture as an input dataset to update the GoogleNet pretrained knowledge. Finally, the process of fault detection and classification is accomplished only by transferring the GoogleNet pretrained knowledge. The simulation results of the modified IEEE 13-bus and 34-bus distribution systems in EMTP-RV and MATLAB indicate the high accuracy of the proposed protection system despite the use of a small amount of input training data.

Adaptive Coordination of Relays in AC Microgrid Considering Operational and Topological Changes

Conventional protection schemes are vulnerable to varying changes in a microgrid with different types of distributed generators (DGs), which alter the fault current levels, leading to miscoordination of directional over-current relays (DOCRs). Due to limited storage in numeric relays, having one setting for each network scenario in the relays is infeasible. Also, schemes with one setting considering grid-connected mode (GCM) and islanded mode (IM) of operation with multiple scenarios may be infeasible for a microgrid with inverter-interfaced DGs due to low short-circuit level in IM. Thus, this article proposes an adaptive trip characteristics-based relay coordination scheme for a microgrid with different DGs, where two trip characteristics are assigned to each DOCR, one each for GCM and IM. Also, a novel framework for selecting the suitable trip characteristic is proposed. Hardware-in-loop simulations using Numerical Relay Development Environment for a meshed CIGRE microgrid, modified IEEE 34-bus distribution system, and comparison with previous works validate the proposed method's performance for various scenarios, such as the outage of a DG or line, variations in solar irradiation and wind speed, and network reconfiguration for different fault types during GCM and IM.

Decentralized coordination between active distribution network and multi-microgrids through a fast decentralized adjustable robust operation framework

Due to the autonomous characteristic and heterogeneity of the individual agents in active distribution network (ADN) with multi-microgrids (MMG), this paper proposes a fully decentralized adjustable robust operation framework achieving the coordinated operation between ADN and MMG. The improved linear decision rules (LDRs) based microgrid adjustable robust operation model is proposed to reduce the solution conservatism in dealing with renewable energy uncertainty. The LDRs model is then reformulated as a computationally tractable solution such that the proposed adjustable robust extension of decentralized operation can handle renewable energy uncertainty while reducing the computation burden of decentralized optimization. Then, a tailored fast alternating direction method of multipliers algorithm with a predictor–corrector type acceleration step is developed to improve the convergence rate of decentralized optimization. The effectiveness of the proposed model is validated on a modified IEEE 69-bus distribution system with four microgrids.

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.

Data-Driven Combined Central and Distributed Volt/Var Control in Active Distribution Networks

This paper proposes a two-stage combined central and distributed Volt/Var control (VVC) strategy to coordinate both utility-scale and customer-owned photovoltaics (PVs) for voltage regulation in active distribution networks (ADN). In the first stage, with day-ahead PV and load predictions, the central controller (CC) performs optimal power flow (OPF) to determine the hourly dispatch of on-load tap changers (OLTC) and capacitor banks (CBs). In the second stage, the ADN is partitioned into different zones only based on PV and load types. The zone controllers (ZC), which is formulated by deep neural networks (DNN), then communicate with their neighbors and track the optimal total reactive power support of PVs in each zone. In the offline training process, an analytical derivation to minimize power losses is also provided that can help the input and output selection of DNN. To online dispatch reactive power for individual utility-scale and customer-owned PVs in corresponding zones, a multi-mode reactive power allocation strategy is proposed. Three different modes namely loss reduction mode (LRM), fair power sharing mode (FPM) and voltage security mode (VSM) are designed to meet varying operation requirements, which can mitigate voltage violations with fair participation for customers and reduced power losses for system operators. Numerical simulations are performed on the IEEE 33-bus distribution system to demonstrate the effectiveness of the proposed method in finding a trade-off between voltage security and loss minimization with fairness.

An Optimization-Based Intentional Islanding Scheme for Service Restoration in Distribution Systems Considering Anti-Parallel Operation of Distributed Generations

An islanding operation of distributed generations (DGs) in emergencies due to a fault in distribution systems can be a means of power supply for important loads in outage areas by facilitating the self-sufficient capability of DGs forming microgrids. This paper presents an optimization-based intentional islanding scheme to derive a near-optimal service restoration (SR) plan. The anti-parallel operation of DGs is considered a new constraint that avoids more than two DGs in an island thereby, enabling simpler control and operation of the distribution system in an emergency. Each island is created by an island partitioning scheme based on the tree representation of the network and fast searching scheme for the tree structure considering load importance, and a genetic algorithm (GA) is utilized to explore possible SR solutions. Case studies on IEEE 69-bus distribution system according to various fault locations are conducted, and the simulation results show that the proposed scheme can restore more loads with higher priority in outage areas by the intentional islanding of DGs. Furthermore, the time for deriving the optimal solution can be reduced since the evaluations for infeasible solutions are not performed.

Collaborative planning of cyber physical distribution system considering the flexibility of data centers

With the development of energy transition and smart grid, the traditional distribution network has transformed into a cyber–physical distribution network (CPDS), which is multidimensional and heterogeneous. The increasingly abundant cyber and physical resources and deep coupling characteristics provide new challenges for distribution network planning. Based on a distribution network with multiple distributed resources and data centers, this paper proposes a cyber–physical collaborative planning method for a distribution network. First, according to the overall architecture of CPDS and the cross-layer control manner, the multiple distributed resources model and spatial–temporal migration model of data loads are established. Then, taking the minimal annual cost of distributed resources as the objective function, the collaborative planning of energy storage systems (ESSs) and corresponding smart terminal units (STUs), spatial–temporal migration strategy of data load, and communication network topology is carried out. Finally, the economics and feasibility of the proposed planning scheme are verified by the modified IEEE-33 bus distribution system. The results show that the collaborative planning method proposed in this paper can reduce the investment cost of distribution network planning and solve the voltage variation problem to a certain extent, so as to improve the economy and operational reliability of distribution network, and provide theoretical guidance for planners.

Resilience enhancement of distribution network under typhoon disaster based on two-stage stochastic programming

The reliability of power supply in distribution network is vulnerable to extreme weather events such as typhoon. Pre-event preparation can effectively mitigate the deterioration of system resilience. Therefore, we propose a distribution network resilience enhancement decision-making framework which is formulated as a two-stage stochastic mixed-integer linear programming (SMILP) model. The first stage invests coordinately in four strategies, including hardening lines, installing distributed generators (DG), allocating mobile emergency generators (MEG), and deploying switches, etc. And the goal at the first stage is to minimize the investment cost of resilience enhancement strategies. The objective at the second stage is to ensure the minimum expected recourse operation cost of the comprehensive strategies for all typical scenarios. The proposed model can combine distribution network long-term resilience planning with short-term post-event recovery. Furthermore, in order to address the uncertain problems of wind field, line damage and load fluctuations under typhoon disaster, this paper proposes the following solutions. For wind field uncertainty, a detailed wind field model considering time transition, sea-land transition and extreme value distribution is established to deal with wind speed prediction. For line damage uncertainty, an improved stress-strength interference model is set up. And for the load fluctuation uncertainty, scenario generation using load random multipliers is applied to address load uncertainty. The proposed framework is tested in IEEE 33-bus distribution system using historical data from 2018 super typhoon “Mangkhut” in China and demonstrates the SMILP model can significantly reduce the post-event expected recourse operation cost and meanwhile improve the distribution network resilience.

Cyber-physical-social system scheduling for multi-energy microgrids with distribution network coordination

The coordination and integration of the cyber system, physical system, and social system, have shown superiorities in developing the new types of energy systems. This paper proposes a day-ahead scheduling model for tripartite coordination of communication network, multi-energy microgrids (MEMGs), and distribution network, using the proposed cyber-physical-social system (CPSS) framework. First, the day-ahead scheduling is designed for the MEMG with considering the social benefits like demand-side response. The demand response results are used by the cyber system for corresponding power consumption and bandwidth allocation. The MEMGs and the cyber system are connected to the distribution network as loads. Second, the distribution network performs power flow distribution with its own objective. Considering the fact that the actual distribution network is divided into different regions due to geographical reasons and privacy protection characteristics, we partition the entire CPSS into multiple regions which can operate independently with their individual MEMG and cyber subsystem. Next, the energy coordination is carried out through the interaction between different regions, and the optimization problem is completely distributed and solved by the parallel enhanced self-adaptive Alternating Direction Method of Multipliers (ESA-ADMM) algorithm. Experiments are performed on an IEEE 33-bus distribution system, and the results verify the effectiveness and economy of the proposed scheme.

Role of Distributed Inverter-Based-Resources in Bulk Grid Primary Frequency Response Through HELICS Based SMTD Co-Simulation

In the recent years, there has been an increase in distributed inverter-based resources (D-IBRs) in the system that is impacting the grid performance. Dynamic transmission and distribution (T&D) co-simulation is an effective method to evaluate the impact of D-IBRs on the system response, however, implementation is a challenge. This article innovatively achieves accurate T&D model coordination with inclusion of dynamic models of power electronic controls. It also demonstrates that D-IBRs operating with grid-forming (GFM) controls with frequency support capability can aid in primary frequency regulation in a low-inertia power system. The article discusses the fundamentals of dynamic T&D co-simulation to analyze the primary frequency response on the IEEE 39-Bus transmission system coupled to IEEE 123-Bus distribution system with D-IBRs with GFM controls. To demonstrate scalability, a large system (286383 T&D buses) is co-simulated: IEEE 39-bus transmission system with all load buses coupled to a total of 2328 parallel IEEE 123-Bus distribution feeders containing a mix of D-IBRs is considered. The article also discusses the use of parallel computation used for distribution system solutions enabled in the scalable multi-timescale T&D co-simulation framework along with computation time comparisons between the large and small system simulations with GFM inverter dynamics.