IEEE 33-bus Distribution Test System Research Articles

A collaborative restoration strategy of resilient distribution system with the support of electric bus clusters

The widespread deployment of electric buses (EBs) offers substantial flexibility resources to support the service restoration and enhance the resilience of distribution system (DS). Therefore, this paper presents a comprehensive and collaborative service restoration strategy for resilient DS, considering the support of EBs. First, the restoration flexibility model for EBs is derived, which quantifies the restoration capability of EBs by dividing effective EB clusters. This model is integrated into the DS restoration process. Then, a two-step collaborative restoration method of DS is proposed with the aim of minimizing the load curtailment cost. The first step addresses the restoration planning problem to determine the dispatch decisions for EB clusters. Based on above planning solutions, the second step focuses on the real-time restoration problem of DS, obtaining optimal restoration results through coordinating EB clusters and other local resources. In this stage, the impact of natural disasters on the EB cluster dispatch in transportation system is considered, and a rolling optimization method is employed to deal with uncertainties from renewables, load, and EB cluster dispatch time. Finally, numerical simulations are conducted using the modified IEEE 33-bus distribution test system and a 29-node transportation system to verify the effectiveness of the proposed restoration method.

Hybrid Siting and Sizing of Distributed Generators and Shunt Capacitors with System Reconfiguration using Wild Horse Optimizer

Injection of Distributed generators (DGS) and Shunt capacitors (SCS) simultaneously with system reconfiguration significantly promotes smart grid performance. In addition, system reconfiguration increases the injected distributed generation capacity in the system. This work proposes a wild horse optimizer (WHO) for the optimal siting and sizing of DGS and SCS in parallel with network reconfiguration. The proposed method aims to attain single and multi-objectives: minimizing active power loss, maximizing voltage stability index (VSI), and minimizing voltage deviation index (VDI). Five operational cases are introduced to elucidate the superior performance of the proposed method. The five cases are executed on IEEE 33-bus standard radial distribution test system. The single-objective function results are compared with other optimization algorithms. The simulation results belay that the proposed WHO optimizer has the best results for unfixed DGS and SCS locations.

A conditional value at risk based stochastic allocation of SOP in distribution networks

Soft open points (SOPs) have been noticed to enhance the operation efficiency of distribution networks due to their capability of exchanging power between feeders as well as reactive power compensation. The uncertainty of distributed renewable energy sources (DRESs) and load demands make its optimal allocation and active power flow settings difficult. Uncertainties should be modeled through appropriate techniques to give more reliable schemes for SOPs allocation and regulation. However, in the probabilistic environment, decreasing the risk of violations from objectives and constraints is also important to guarantee confident solutions.This paper finds the optimal location of an SOP and its power flow setpoints, considering the uncertainty of DRESs and load demands. The mentioned uncertainties are handled by the efficient K-medoids data clustering technique. In addition to considering the expected value of losses and voltage deviation as objective functions, the conditional value at risk (CVaR) of voltage deviation is considered, too. This ensures that in the final solution, the probability of voltage deviation in most worst scenarios will not be considerable. The proposed optimization model is implemented in a multi-objective optimization frame and solved by the multi-objective particle swarm optimization (MOPSO) algorithm and tested on the modified IEEE 33-bus test distribution system and the effect of considering the CVaR index on results is comprehensively discussed.

Strategic decision making of energy storage owned virtual power plant in day-ahead and intra-day markets

In this paper, a service-oriented aggregator like a virtual power plant is modeled and designed to facilitate the integration of distributed generations in both day-ahead and intra-day power markets and simultaneously collaborate with the distribution system operator to manage the grid utilization problem. To do this, a proper plan of interactions between power system and market operators, customers, and aggregators concerning participation in the electricity market is suggested and explained. The optimal arrangement of the real and reactive power flows of VPP sources is a solution used to manage the congestion of the distribution network with the aim of maximizing the integration of renewable energy into the energy market. The VPP utilizes the rolling horizon technique to decrease the uncertainties related to renewables in the scheduling horizon. To prevent unwarranted wind and solar power generation curtailments, VPP behaves flexibly and reschedules its resources according to the contracts that are concluded. In this work, a modified IEEE 33-bus distribution test system is utilized to investigate the efficiency and proficiency of the proposed method. The simulation and numerical results demonstrate that the suggested VPP model can not only improve solar and wind power integration in the energy markets but also alleviate congestion in the distribution networks.

Mobile energy storage systems with spatial–temporal flexibility for post-disaster recovery of power distribution systems: A bilevel optimization approach

In recent years, the damage to power distribution systems caused by the frequent occurrence of extreme disasters in the world cannot be ignored. In the face of the customer’s demand for high power supply reliability and high power quality, it is urgent to establish a resilient distribution network that can not only resist extreme disasters and quickly recover the power distribution system loads, but also ensure a high voltage quality of the distribution system during recovery process. Therefore, mobile energy storage systems with adequate spatial–temporal flexibility are added, and work in coordination with resources in an active distribution network and repair teams to establish a bilevel optimization model. The objective of the upper-level optimization model is minimum the total load curtailment of the distribution system after the disaster. And the objective of the lower-level optimization model is minimum the voltage offset of power supply buses during the recovery stage. A modified IEEE 33-bus distribution test system is used to verify this model. The simulation results show that the bilevel optimization strategy proposed in this research can effectively reduce the voltage offset during the recovery process of distribution systems, and ensure the minimum total load curtailment simultaneously.

An Optimal Multi-Objective Approach for Distributed Generation and AVR Placement in Distribution Networks Using IPMBSA

Inclusion of automatic voltage regulators (AVRs) and distributed generation units (DGs) in the distribution system (DS) will enhance the system performance if allocated optimally. Optimal allocation of AVRs and DGs is a complex combinatorial problem requiring a powerful optimization algorithm for solving the optimization problem. This paper proposes an improved pareto multi-objective backtracking search algorithm (IPMBSA) based Pareto front approach for optimal siting and sizing of DGs and AVRs. The proposed approach considers DS technical (such as minimizing of voltage deviation, power losses and voltage stability index), cost and emission indices as objective functions while solving the allocation problem. To evaluate the performance of the proposed approach, five different operational scenarios are considered for analysis on IEEE 33-bus, IEEE 69-bus and practical 43-bus Indian distribution test systems. The effectiveness of the proposed approach is highlighted by comparison of results obtained using a multi-objective approach based on NSGA-II and weighted sum method (WSM). Results propounded that allocation of DG along with AVR yielded better DS performance compared to the scenarios where only DG or AVRs are placed in DS.

An operationally induced approach to reliability-oriented ACOPF-constrained planning of interconnected multicarrier energy hubs: An MILP formulation

Energy hubs (EHs) are considered as prospective frameworks for future high-reliability energy systems, as interfaces for generation, conversion, storage, and consumption of different energy carriers. Interoperability and interconnection of the EHs may improve the reliability of the system, therefore, the system planning and operation should be constrained by AC optimal power flow (ACOPF), to ensure connectivity of the EHs and optimality of the operation. The main contribution of this paper lies in introducing an innovative operationally induced approach to reliability-oriented ACOPF-constrained planning of interconnected EHs. This manuscript presents an efficient mixed integer linear programming (MILP) formulation, aiming to result in optimal reliable and economic planning and scheduling of the microgrids including interconnected EHs, while reducing the computational effort. Further, this research investigates the effect of EHs interconnection and demand response (DR) on optimal planning and scheduling of microgrids. This is implemented on the IEEE 33-bus distribution test system by operating EHs in interconnected mode, revealing a significant improvement in reliability (CIC: customerinterruption cost, ENS: energy not supplied, and EIR: energy index of reliability), economic (DSOBenefit, EnergyRevenue, INVC &OPC: investment and operation costs), renewable energy share (REC: renewable energy curtailment, REUF: renewable energy utilization factor, and WPPCC: Wind-photovoltaic generation Pearson correlation coefficient), and microgrid-maingrid energy exchange.

Age-dependent resilience assessment and quantification of distribution systems under extreme weather events

The impact of extreme weather events on power system resilience can be seen in historical electrical disruptions. Due to the increasing intensity and frequency of extreme weather events following global warming and climate change, a greater focus is required to design power systems with high resilience and low cost through assessing and quantifying power system resilience. In this way, we propose a transparent methodology and a set of metrics to quantify the resilience of distribution systems subjected to hurricanes. This methodology includes a probabilistic and detailed approach to modelling the failure probability of overhead lines and the restoration time of damaged lines. Moreover, the fragility analysis of the power system infrastructure is considered age-dependent, and the system’s resilience by considering different lifetimes is assessed. The time-dependent resilience assessment constitutes an essential component in risk-informed decision-making for resilience enhancement strategies in the future. Specifically, this paper provides two resilience curve-based metrics: vulnerability rate and restoration rate. The first metric indicates the power system’s ability to resist extreme events, and the second indicates its ability to bounce back to normal performance. Simulations are performed on the IEEE 69-bus distribution test system to validate the suggested methodologies. The results indicate the capability of the proposed methodology and metrics to precisely assess and quantify the power system resilience. A significant correlation between the age of power distribution systems and the system’s resilience can be seen. These findings can support decision-making concerning system management and plans such as expansion planning and resource allocation.

Optimal Integration of Hybrid Energy Systems: A Security-Constrained Network Topology Reconfiguration

The integration of distributed energy resources, such as wind farms (WFs) and energy storage systems (ESSs), into distribution networks can lower the economic cost of power generation. However, it is essential to consider operational constraints, including loading margin, which ensures the security line contingency. This study aims to develop a comprehensive hourly distribution network reconfiguration (HDNR) model to minimize the economic cost for the power generation company. The model considers the optimal allocation of WFs and ESSs in terms of capacity and location, as well as the hourly status of the distribution network switches, based on security constraints. The proposed model is applied to an IEEE 33-bus distribution test system, and the capacities and locations of WFs and ESSs are determined. The impacts of security constraints on the optimal capacities and locations of WFs and ESSs, and the hourly configuration of the distribution network, are analyzed based on two case studies. In Case Study I, the model is solved with HDNR conditions, while Case Study II is solved without these restrictions, for comparison purposes. The results show that the optimal allocation of WFs and ESSs is affected by security constraints when HDNR is considered and highlight the crucial role of security constraints under contingency conditions, such as line outages. In the test system, three WFs and two ESSs are optimally allocated, with changes in capacity and location as the loading margin varies.

Reliability and efficiency enhancement of a radial distribution system through value-based auto-recloser placement and network remodelling

The electric distribution system (EDS) is prone to faults leading to power interruptions. The present energy market demands that electricity utilities invest more in different measures to improve the performance of the EDS. The approach proposed here details a composite dual-phased methodology to improve the reliability and efficiency of the power delivered by the EDS. In the first phase, the optimal allocation of auto-reclosers (AR) is undertaken by employing a newly formulated algorithm. The determination of the total number and location for AR placement is based on the economic analysis of two factors, i.e., AR investment-maintenance cost and total benefit earned in terms of reliability improvement due to AR placement. The analysis also takes into account the impact of power outages on different load types, the load growth rate, and the inflation rate. Further, to enhance the efficiency of the AR-incorporated EDS, the technique of Radial Distribution System Remodelling is employed in the second phase. This method searches for a radial configuration that delivers power at minimum line losses. These phases comprising complex combinatorial operations are aided by a fresh hybrid of the Sine Cosine Algorithm, Krill Herd Algorithm, and a genetic operator of Differential Evolution. The results obtained from its application on the IEEE 69-bus distribution test system prove the credibility of the suggested formulation.

A hierarchy model to use local resources by DSO and TSO in the balancing market

Due to the increasing penetration level of local generating resources at the distribution network, the transmission system operator (TSO) and distribution system operators (DSOs) need to coordinate their operations to use these resources and provide the required energy and regulation for the power system. This work proposes a hierarchy model for scheduling the local flexible resources at the distribution and transmission levels. In the first stage, the optimal bidding strategy of balance service providers (BSPs) and the distribution balancing market-clearing problem are formulated by a bi-level optimization problem, respectively. At this stage, the lower-level sub-problem is replaced by the Karush–Kuhn–Tucker (KKT) conditions, and the bi-level problem is formulated as a mathematical programming with equilibrium constraints (MPEC) problem. Results of the first stage are considered as fixed parameters for the second stage problem, which specifies the feasible bids of local resources that the TSO can dispatch. At the second stage, DSO checks the feasibility of remaining bids, which are not dispatched in the distribution balancing market, and determines the maximum regulation capability of the local resources that the TSO can use. Simulation results indicate that in the IEEE 33-bus distribution test system with the total imbalance energy of 4.672 MWh, 32.36% and 67.64% of the required power are compensated by transmission balancing market and local resources, which results in a reduction of 24.32% in balancing costs.

Service restoration for resilient distribution system with coordination of battery swapping stations and distributed generations

Due to sufficient energy storage resources and central dispatch, the battery swapping station (BSS) offers a promising solution for the service restoration and resilience enhancement of the distribution system (DS). This paper proposes a joint service restoration strategy for the DS by coordinating BSSs and distributed generations (DGs) with distribution network reconfiguration. Firstly, the spatio-temporal distribution of battery swapping demand for electric vehicles (EVs) is analyzed. On this basis, the participation of the BSS in the DS service restoration is investigated, and the model of restoration capacity for the BSS is established, which can describe the power supply ability of the BSS to support the DS restoration. Secondly, a mathematical model for the resilient DS restoration with coordination between BSSs and DGs is established, aiming to minimize the total system cost by optimally allocating BSSs and DGs during the restoration stage. The second-order cone programming (SOCP) relaxation is also adopted to transform the proposed restoration model into a mixed-integer convex formulation, which can be tractably solved. Finally, numerical simulations are conducted based on the modified IEEE 33-bus distribution test system. The results demonstrate the effectiveness of the proposed strategy for the service restoration and resilience enhancement of the DS.

BONMIN solver-based coordination of distributed FACTS compensators and distributed generation units in modern distribution networks

The problems of increasing electricity consumption and atmospheric pollutants emissions are targeted as critical challenges for power transfer in modern distribution networks. In order to tackle this issue, many researchers have proposed the incorporation of Distributed Generator (DG) units, as well as different types of reactive power compensation devices, which will contribute to the increase of networks efficiency through loss minimization, voltage profile improvement, or even increasing transmission line’s capacity. This paper addresses the optimal allocation/coordination of DG, Distributed Static VAR Compensator (D-SVC), and Distributed Thyristor-Controlled Series Compensator (D-TCSC) to reduce total power loss in the distribution network. The allocation problem is formulated as a mixed-integer nonlinear programming (MINLP) and is solved using a basic open-source MINLP (BONMIN) solver embedded in GAMS. The proposed method was tested in IEEE 16-bus and 33-bus distribution test systems, where two cases were considered. Firstly, BONMIN solver is used for allocation of D-SVC, and D-TCSC, respectively, where the results showed that incorporation of these devices leads to reduction of the active power losses, while the minimal losses and highest efficiency increase for both test networks occurred in the case where 3 D-TCSCs allocated. Secondly, a coordinated allocation of DGs and D-SVCs, as well as DGs and D-TCSCs, is presented, where it was shown that coordination of these devices leads to even greater loss reduction, i.e. increased efficiency. Also, it was concluded that DG and D-TCSC coordination is better suited for loss reduction in the 16-bus system, while DG and D-SVC coordination leads to greater loss reduction for the 33-bus system. Furthermore, the comparison between BONMIN and metaheuristic methods was performed for DG allocation and DG and D-TCSC coordination in IEEE 33-bus system, where it was concluded that BONMIN performs better in terms of minimal power loss, compared to the results which were presented in the available literature. Finally, the BONMIN solver’s performance is evaluated using three parameters: number of nodes, number of iterations, and total execution time, where the numerical analysis showed that BONMIN is efficient in solving the addressed problems.

Distributed Measurement-Based Optimal DER Dispatch With Estimated Sensitivity Models

This paper presents a distributed measurement-based method to determine distributed energy resource (DER) active-and reactive-power setpoints that minimize bus voltage deviations from prescribed references, bus active-and reactive-power deviations from desired values, as well as cost of DER outputs. The proposed method partitions the system into multiple areas and performs per-area computations in parallel, thus mitigating scalability concerns of centralized implementations. A linear sensitivity model for each area is first estimated from measurements via the recursive weighted partial least-squares algorithm. The estimated sensitivity models are then embedded in an optimization problem, the structure of which is amenable to decomposition into per-area subproblems. The subproblems are solved in parallel using consensus-based alternating direction method of multipliers to obtain optimal DER setpoints. Both the estimation and optimization tasks require only the exchange of information at boundary buses among adjacent areas. Numerical simulations involving the IEEE 33-bus distribution test system illustrate the ability of the proposed method to determine optimal DER setpoints that adapt to operating-point changes in a distributed fashion. Additional numerical simulations involving the IEEE 123-bus system demonstrate computational scalability and the application to multi-phase systems in a practical scenario.

Optimal Placement of Measuring Devices for Distribution System State Estimation Using Dragonfly Algorithm

This paper presents the challenges of optimal measurement devices placement (MDP) in the distribution system by considering the improvement of accuracy and speed for state estimation (SE) in the presence of distributed generations (DGs). We assumed that active and reactive power measurements (both injection and flow) with voltage magnitude measurements were used to estimate the power system’s state. The paper employed phase measurement unit (PMU) and smart meters, which are the two commonly used measuring devices. For numerical evaluation of the system, the power system states are based on the angle and magnitude of voltages at every bus. The issues normally experienced in the optimal measurement devices placement in distribution networks were investigated using the binary dragonfly algorithm (BDA), in this study. As a way forward to proffer solutions to these issues, a fair compromise between accuracy, speed, and the number of measurements (NoMs) was reached, and the proposed solution was tested in two different scenarios applied in the IEEE 33-bus distribution test system. The results illustrate that by increasing the accuracy, NoMs and the cost are going to rise as well. On the other hand, by escalating the speed, NoMs decrease and the accuracy falls dramatically.

Time of use electricity pricing in power system planning and operation: Case study of Nepalese power system

The difference between load demand in the off-peak and peak period is usually high in the context of Nepal, and the high peak load for a short time is the main reason for insufficiency in electricity, frequent electricity outage, and poor power quality. This paper investigates the effectiveness of the time of use (TOU) strategy to reduce peak demand and to improve power quality. TOU electricity price is one of the electric demand response (DR) strategies, which may motivate the customers to reduce their consumption in peak periods and shift load in the off-peak period, which propel the electricity industry towards a higher efficiency compared to that of common flat prices. In this paper, a process based on an equal step length iteration algorithm is used to obtain an optimal period partition. Using the particle swarm optimization (PSO) algorithm, a TOU-based optimization model is proposed to find the optimal electricity price. Then, its impact on peak demand reduction and voltage fluctuation is analyzed. The impact of TOU price on peak demand reduction is investigated in the hourly load profile of Nepal, which indicates the significant reduction in peak demand. Similarly, the impact on voltage fluctuation is analyzed through a case study of the IEEE 33-bus radial distribution test system, which shows voltage profile is improved.

A Novel Analytical Approach for Optimal Placement and Sizing of Distributed Generations in Radial Electrical Energy Distribution Systems

Considering the strong influence of distributed generation (DG) in electric distribution systems and its impact on network voltage losses and stability, a new challenge has appeared for such systems. In this study, a novel analytical algorithm is proposed to distinguish the optimal location and size of DGs in radial distribution networks based on a new combined index (CI) to reduce active power losses and improve system voltage profiles. To obtain the CI, active power losses and voltage stability indexes were used in the proposed approach. The CI index with sensitivity analysis was effective in decreasing power losses and improving voltage stability. Optimal DG size was determined based on a search algorithm to reduce active power losses. The considered scheme was examined through IEEE 12-bus and 33-bus radial distribution test systems (RDTS), and the obtained results were compared and validated in comparison with other available methods. The results and analysis verified the effectiveness of the proposed algorithm in reducing power losses and improving the distribution system voltage profiles by determining the appropriate location and optimal DG size. In IEEE 12 and 33 bus networks, the minimum voltage increased from 0.9434 p.u and 0.9039 p.u to 0.9907 p.u and 0.9402 p.u, respectively. Additionally, the annual cost of energy losses decreased by 78.23% and 64.37%, respectively.

A resilience-oriented two-stage recovery method for power distribution system considering transportation network

In recent years, extreme weather events have resulted in a number of severe power outages and economic losses. It is becoming urgent to study approaches to enhance power distribution system resilience. This paper presents a two-stage optimization method for improving resilience of power distribution systems. The two-stage method is influenced by a transportation network, which considers the interdependence between traffic roads and overhead distribution lines. In the first stage, based on the Bayesian network and the Monte Carlo method, the locations of emergency stations, e.g., charging stations and repair stations are pre-positioned to improve the recovery speed of interrupted loads after disasters. Then the second stage is to co-optimize the dispatch of mobile energy storage systems and repair crews on transportation network to minimize the shedding of critical loads while considering operation constraints of power, voltage and power flow for power distribution system. The dispatch of mobile energy storage systems and repair crews are affected by the locations of emergency stations in the first stage. The route of mobile energy storage systems and repair crews on transportation network can be obtained from the results in the second stage. The proposed method is tested on a modified IEEE 33-bus distribution test system with a corresponding transportation network. Simulation results demonstrate the effectiveness of the proposed method in enhancing power distribution system resilience by pre-positioning emergency stations and co-dispatching mobile energy storage systems and repair crews.

Optimal scheduling of a reconfigurable active distribution network with multiple autonomous microgrids

In this paper, a decentralized framework for optimal scheduling of a reconfigurable Active Distribution Network (ADN) is proposed that preserves the autonomy and information privacy of all the agents, i.e., the distribution system and microgrid's operators. In the proposed framework, the scheduling problem is decomposed to a certain number of Local Scheduling Models (LSMs), in such a way that each agent can independently schedule its network. In the proposed LSM, all technical constraints such as AC load flow equations, and radiality constraints are respected. Also, a Multi-Level Analytical Target Cascading (MLATC)-based method is proposed to coordinate the scheduling results of the LSMs. In this framework, by introducing new auxiliary variables, the shared variables that couple the LSMs are limited to only three types. Using the proposed framework, the optimal configuration of the ADN and the operational scheduling of agents are simultaneously calculated. Since all technical constraints are modeled in the proposed LSM, the results are both economic and feasible. To assess the optimality and effectiveness of the proposed method, several simulations are carried on the modified IEEE 33-bus and IEEE 123-bus distribution test systems. Moreover, the results are compared with the centralized approach to validate the optimality of the solution.

Utilization of in-pipe hydropower renewable energy technology and energy storage systems in mountainous distribution networks

Million miles of gravity-fed drinking water and sewage pipelines around the world, especially in rural and urban areas in mountain ranges, have introduced a new renewable energy sources (RES), i.e., in-pipe hydropower systems (IHS). Output power of this technology, similar to other types of RES, suffers from intermittency, while it is still more predictable in comparison to other technologies of RESs. Besides, energy storage systems (ESS) are introduced as a pivotal technology for dealing with the intermittent and non-dispatchable characteristics of IHS through spatio-temporal arbitrage. This paper aims to develop a stochastic mixed-integer linear programming (MILP) formulation that simultaneously determines the optimal location and size of ESS and IHS in a microgrid (MG) considering the correlation between prevailing uncertainties. In this regard, the proposed optimization problem minimizes the expected social cost of MG considering the AC power flow equations and operation constraints. Finally, the efficiency and applicability of the proposed model are illustrated using numerical simulations on the IEEE 33-bus distribution test system.