IEEE 33-bus Distribution System Research Articles

ADMM-Based Multi-Objective Control Scheme for Mitigating the Impact of High Penetration DER Integration in the Modern Distribution Systems

The high penetration of renewable energy sources in modern distribution networks poses challenges for grid voltage regulation. In this study, a multi-agent distributed voltage control strategy based on the proximal Jacobian alternating direction method of multipliers (PJ-ADMM) is proposed for distribution power systems with a high penetration of photovoltaic (PV) resources coordinated with battery energy storage systems (BESS). In this context, all active and reactive power outputs of the PV are locally optimized through smart inverters to improve the grid voltage with minimum power loss. Uncertainties associated with the solar energy generation and load demands are considered in the defined scenarios. This study consists of two phases. In the first phase, the voltage control problem is formulated as an optimization problem to regulate the voltages within an acceptable limit with fast convergence. In the second phase, a coordinated voltage control strategy for smart PV inverters and BESS is proposed to allocate the power capacity of the battery energy storage systems and the active power loss reduction. Finally, the proposed method is tested on modified IEEE 13-bus, 33-bus and 141-bus distribution systems using MATLAB/Simulink and MATPOWER. A comparison of the results of the voltage profiles with and without the control algorithm demonstrated the efficacy, robustness, and scalability of the distributed scheme for voltage improvement and optimal utilization of PV power under different scenarios.

Safety-Integrated Online Deep Reinforcement Learning for Mobile Energy Storage System Scheduling and Volt/VAR Control in Power Distribution Networks

In coupled power distribution and transportation (CPT) system, a joint scheduling framework for mobile energy storage systems (MESSs) and Volt/VAR control (VVC) ensures reliable power distribution grid operations while supporting electric vehicle loads at electric vehicle charging stations (EVCSs). However, conventional model-based optimization methods for MESS scheduling and VVC may yield suboptimal solutions and greater computation times because of MESS operation and VVC in uncertain environment of CPT systems. To resolve this issue, this study proposes a model-free deep reinforcement learning (DRL) framework. In this framework, smart inverters of MESSs and solar photovoltaic (PV) systems cooperate to minimize the real power loss and mitigate the violations of both MESSs’ state of charge (SOC) and voltage in the power distribution network, while MESSs travel via the transportation network to satisfy EV loads at EVCSs. A MESS routing algorithm based on Dijkstra’s algorithm is developed to determine the optimal destinations of the MESSs. In addition, two safety modules are developed to ensure that neither SOC nor voltage violations occur by adjusting real and/or reactive power of MESSs and PV systems during the training process. The developed MESS routing algorithm and safety modules are integrated into the proposed DRL framework, wherein the DRL agent performs the desired MESS scheduling and VVC through safe exploration during the training procedure. The proposed approach is tested in coupled IEEE 33-bus power distribution and 15-node transportation systems and coupled IEEE 57-bus power distribution and 42-node transportation systems. Numerical examples demonstrate the advantages of the proposed approach in terms of training convergence, real power loss, and SOC/voltage violation.

Priority-based Residential Demand Response for Alleviating Crowding in Distribution Systems

The dynamic pricing environment offers flexibility to the consumers to reschedule their switching appliances. Though the dynamic pricing environment results in several benefits to the utilities and consumers, it also poses some challenges. The crowding among residential customers is one of such challenges. The scheduling of loads at low-cost intervals causes crowding among residential customers, which leads to a fall in voltage of the distribution system below its prescribed limits. In order to prevent crowding phenomena, this paper proposes a priority-based demand response program for local energy communities. In the program, past contributions made by residential houses and demand are considered as essential parameters while calculating the priority factor. The non-linear programming (NLP) model proposed in this study seeks to reschedule loads at low-cost intervals to alleviate crowding phenomena. Since the NLP model does not guarantee global optima due to its non-convex nature, a second-order cone programming model is proposed, which captures power flow characteristics and guarantees global optimum. The proposed formulation is solved using General Algebraic Modeling System (GAMS) software and is tested on a 12.66 kV IEEE 33-bus distribution system, which demonstrates its applicability and efficacy.

Time-Domain Protection Scheme for Microgrids With Aggregated Inverter-Based Distributed Energy Resources

This paper proposes a time-domain-based protection scheme for radial and loop microgrid systems with inverter-based resources (IBRs), such as solar photovoltaic (PV) and type-4 wind turbines. The protection scheme is designed to function during both grid-interconnected and grid-isolated modes. The proposed scheme provides an ultra-high-speed sub-cycle directional element aided with low bandwidth communication between relays. Like directional comparison schemes, relays identify whether faults are in-zone or out-zone. The directional element is based on time-domain superimposed quantities and Park’s transformation algorithms. More specifically, the element calculates the superimposed positive-sequence direct component of transient energy during faults. Superimposed voltage and current quantities are calculated using delta filters and decoupled double synchronous reference frame (DDSRF) filters. The proposed filtering method improves the reliability of the superimposed directional element when IBRs are present. The protection scheme is evaluated on a modified IEEE 34-bus distribution system simulated using an electromagnetic transients program.

A three-stage resilient dispatch of mobile emergency generators in a distribution system against hurricanes

Mobile emergency generators (MEGs) have been widely used to enhance the resilience of distribution systems. However, the traditional two-stage MEG dispatch strategies focus on only the prepositioning and reallocation before and after hurricanes, neglecting the resilient operation during hurricanes. Therefore, this paper proposes a novel three-stage MEG dispatch model to enhance the resilience of distribution systems. To improve the survivability of distribution systems during hurricanes, the fault isolation operations by remote-controlled switches (RCSs) and forced cut-off of wind power are coordinated with MEG dispatch, which is prepositioned as a preparedness measure before hurricanes. Based on the realization of fault scenarios during hurricanes, MEG reallocation and fault isolation by manual switches (MSs) are conducted to boost the restoration of the distribution system after hurricanes. Considering the uncertainties of wind power cut-off and line faults, the proposed method is incorporated into a stochastic dispatch model, which takes the conditional value-at-risk (CVaR) as an objective function to improve robustness in extreme cases. In addition, the computational burden with multiple scenarios is reduced by the modified progressive hedging algorithm (PHA). Finally, the proposed method is validated on a modified IEEE 33-bus distribution system. Simulation results show that the survivability and restoration of the distribution system can be enhanced by the proposed approach.

Modeling Optimal Energy Exchange Operation of Microgrids Considering Renewable Energy Resources, Risk-based Strategies, and Reliability Aspect Using Multi-objective Adolescent Identity Search Algorithm

The inherent volatility and unpredictable nature of renewable resources and load demand have posed significant challenges to the optimal energy exchange of microgrids (MGs). Therefore, this study presents a novel risk-based multi-objective energy exchange optimization for the networked MGs from economic and reliability standpoints under load consumption and renewable power generation uncertainties. For this purpose, three different risk-based strategies are distinguished using the conditional value at risk approach. The proposed model is specified as an objective function in two distinct ways. The first function minimizes operation and maintenance costs, the cost of exchanging power between the upstream grid and MGs, and the cost of power loss, whereas the second function minimizes the amount of energy that is not supplied. Moreover, a stochastic scenario-based approach is incorporated into this approach to control uncertainties. Also, the scenario reduction method is implemented to reduce the computational burden. Finally, a developed multi-objective adolescent identity search algorithm (AISA) is proposed. Pareto theory and non-dominated sorting are employed to develop the proposed model. In the proposed data development model, it has been tried to improve the local and global search in a better way in order to solve local points and early convergence. The fuzzy model is used to select the optimal solution from the set of solutions. The modified IEEE 33-bus distribution system is used to implement the suggested model and assess its efficacy. The obtained results show that the proposed risk-based model can control the risk of the EMS problem due to the stochastic behavior of the input parameters. In this paper, for risk assessment high consequences and low probability events are considered extreme events. In the risk aversion strategy, the cost of the event is significantly reduced compared to the risk strategy, while the additional cost is much less than this is.

Double-layer optimized configuration of distributed energy storage and transformer capacity in distribution network

In order to solve the problem of low utilization of distribution network equipment and distributed generation (DG) caused by expansion and transformation of traditional transformer capacity, considering the relatively high cost of energy storage at this stage, a coordinated capacity configuration planning method for transformer expansion and distributed energy storage (DES) is proposed in this paper. First, the energy storage capacity requirements is analyzed on the basis of the transformer overload requirements, and analyzing the correspondence between different capacities of energy storage and transformer expansion capacities. Besides, taking into account the impact of different action mechanisms of energy storage on the node load within a day, a location optimization method for DES based on the standard deviation of network loss sensitivity is introduced. Then, considering the net cost of coordinated planning of energy storage and transformer are minimum and the benefit of energy storage operation is maximum, a two-layer optimization model of distributed energy storage and transformer capacity is established. Finally, the solution method of the two-layer optimization model is proposed, and the feasibility of the proposed model is verified by IEEE33 bus distribution system. The results show that the coordinated planning method proposed in this paper can greatly reduce the investment cost, and the net cost of the coordinated planning scheme is reduced by 17.558 million yuan compared with the scheme of separate configuration for energy storage, which effectively improves the economics of energy storage configuration.

Coordinated Control of Electric Vehicles and PV Resources in an Unbalanced Power Distribution System

Improving air quality, reducing greenhouse gas emissions, and achieving independence from fossil fuels have led most countries towards deploying solar photovoltaics (PV) in the power distribution grid and electrifying the transportation fleet. Internal combustion engine (ICE) vehicles are, in particular, one of the main culprits of injecting greenhouse gas emissions into the atmosphere, making electric vehicles (EVs) an important tool in combating climate change. Despite their considerable environmental and economic benefits, the integration of PVs and EVs can introduce unique operational challenges for the power distribution grid. If not coordinated, high penetration of PVs and EVs can result in variety of power quality issues, such as instances of overvoltage and undervoltage, frequency fluctuations, and/or increased losses. This paper proposes a mixed-integer multi-objective nonlinear optimization model for optimal energy dispatch in a power distribution grid with high penetration of PV and EV resources. The model proposed here is an extension of the traditional voltage and var optimization (VVO) into a comprehensive and coordinated control of voltage, active power, and reactive power. A modified version of the IEEE 123-bus test distribution system is used to demonstrate the effectiveness of the proposed solution.

Optimal Placement and Sizing of DGs in Distribution Networks Using Dandelion Optimization Algorithm: Case Study of an Algerian Distribution Network

The optimal placement of distributed generation (DG) in power distribution systems involves identifying the best locations for the generators to be installed and sizing them appropriately, to optimize the performance of the system. In this paper, the recently proposed nature-inspired optimization algorithm namely: Dandelion Optimizer (DO) has been used for the optimal placement and sizing of DG in the radial distribution network. The objectives are to minimize active power loss and voltage deviation and to enhance the voltage stability of the distribution network. The efficiency of the proposed method has been verified over the IEEE 33-bus and Algerian 112-bus distribution systems. The result comparisons indicated that the proposed method can obtain higher quality solutions than many other methods for the considered scenarios from the test systems. Therefore, the DO algorithm can be a very effective method for solving the optimal allocation of the DG problem.

Economic scheduling of mobile energy storage in distribution networks based on equivalent reconfiguration method

Compared with traditional stationary energy storage system(SESS), mobile energy storage system(MESS) has power transfer ability in both spatial and temporal dimensions. Thus, it can provide greater flexibility in power system auxiliary services. To simplify and clarify the optimal scheduling problem, a novel “virtual switch” indicator is defined to describe the scheduling of MESS and an equivalent reconfiguration method is proposed by analogy with distribution network reconfiguration. Based on the method proposed, the dynamic economic scheduling of MESS in distribution networks(DNs) can be formulated as a scenario-based stochastic optimization problem, where the uncertainties of traffic flow and renewable energy output are considered by generating scenarios using Monte Carlo simulation. The objective function is to maximize the profit of distribution network operator. The time and cost of MESS scheduling are transformed into those of virtual switch switching in distribution network reconfiguration. Therefore, the operation scheduling problem of a MESS is equivalent to a distribution network reconfiguration problem with a SESS and several virtual switches. In this way, a grid-traffic network coupling problem is simplified to a pure distribution network problem, which provides a novel way to solve the MESS scheduling problems. Finally, simulation results on the IEEE 33-bus distribution system and simple traffic network verify the validity and feasibility of the method proposed.

Restoration of a new age power distribution system

The new age transformed distribution system became the most popular content in the literature. In that regard a simulation is attempted for the load flow with dynamically changing system data. Any topic of interest like electrical vehicle, distributed generators, FACTS devices were generalized and considered as an element and placed at different possible locations to observe their effect. The outcomes of the work were generalized to re-consider on site. As the power industry is undergoing a drastic change throughout the world. Restoration of a distribution system along with switching sequence has become a challenging task. In the last half of the twentieth century, analysis of transmission system was the prime concern and faced many challenges to power engineers. The chosen equivalent IEEE 33-bus electric power distribution system has been considered to analyze the system properties such as its maximum capacity, operating limits, restoration issues. The placement of static var compensator (SVC) was experimented for each and every bus. Considering the minimization of total power loss as a prime objective, the best location was found. This analysis also suggested a ranking of different buses and can be helpful in deciding the switching sequence, provided in case of fault, weak area clustering is done. So, congestion is managed in this distribution system.

A Novel Approach for Improved Linear Power-Flow Formulation

Fast and accurate power-flow methods are of great importance, especially in near real-time optimal operation of power systems. This importance will be even more highlighted in the presence of more repetitions of power-flow calculations, which cause more computational complexities in optimization problems. As a solution, in this paper, a novel fast and accurate approach of linear power-flow formulation is proposed. Principles of the proposed approach are based on dividing power-flow calculations into base and variable parts. To this aim, at first, system modeling of base and variable parts are presented. For the base-part modeling, utilizing a nonlinear power-flow, an accurate base power-flow (BPF) is extracted. Afterwards, by linearizing the power system around the BPF, variable-part model which is the result of a linear fitting process, is obtained. Then, it is shown that the variable-part of the operating point is always a function of the obtained base-part and variable-part models. In this paper, by focusing on the stochastic application of the proposed approach, different uncertainties in a distribution system are considered. Finally, numerical results carried out in the Matlab environment, for a IEEE 34-bus standard distribution system and then a 1486-bus case study, verify the performance of the proposed approach.

Hierarchical Microenergy Hub Sizing and Placement in Integrated Electricity and Natural Gas Distribution Systems

The integration of different energy carriers is a key factor to improve the energy network performance. A micro-energy hub (μEH) concept has recently been proposed in the integrated electricity and natural gas distribution systems (IENGDSs), for generating, converting, storing, and managing different types of energy at the customer level. To improve the performance of μEH, this paper proposes a hierarchical optimization-based model to optimize the sizes of μEH components and locations of μEHs in the IENGDS. The operation and structural sizing constraints of the μEHs are first determined. Then the optimal electricity network buses and natural gas nodes for installing μEHs are determined, considering the operational constraints of IENGDS. The objective in both steps is to minimize operation and investment costs. Moreover, the impacts of seasonal climate changes on energy demands and prices are considered during the planning horizon. Furthermore, the uncertainties in demands and renewable energy generation are quantified with a two-stage stochastic programming framework. The effectiveness of the proposed method is evaluated on an IENGDS that consists of an IEEE 33-bus distribution system and a Belgian 20-node natural gas system. Numerical simulations demonstrate that the optimal hierarchical planning model can improve total line losses and reduce the total purchased electricity from the upstream network and total imported natural gas from gas wells. The simulation results show that if the hierarchical optimization process is not performed in determining optimal placement of μEHs, then IENGDS may operate with electricity network congestion or natural gas pressure violations during the planning horizon.

Distribution Networks Reconfiguration for Power Loss Reduction and Voltage Profile Improvement Using Hybrid TLBO-BH Algorithm

In this paper, a new method based on the combination of the Teaching-learning-based-optimization (TLBO) and Black-hole (BH) algorithm has been proposed for the reconfiguration of distribution networks in order to reduce active power losses and improve voltage profile in the presence of distributed generation sources. The proposed method is applied to the IEEE 33-bus radial distribution system. The results show that the proposed method can be a very promising potential method for solving the reconfiguration problem in distribution systems and has a significant effect on loss reduction and voltage profile improvement.

A cooperative PSO algorithm for Volt-VAR optimization in smart distribution grids

The penetration level of photovoltaic (PV) systems is set to increase in the following years and already distribution networks (DNs) are straining to overcome the adverse voltage effects caused by reverse power flows and intermittent generation phenomena. Moreover, regular voltage optimization approaches that employ PV inverters as control devices, cannot deal effectively with the escalating dimensionality of the problem. This paper introduces a reactive power optimization method for PV-heavy DNs based on cooperative particle swarm optimization (CPSO). The proposed approach makes use of multiple swarms, each swarm containing a group of design variables that are interrelated with respect to the optimization objective; a community detection algorithm is employed to assign the design variables to the different swarms, by identifying voltage-decoupled zones of the grid. The different swarms cooperate by exchanging information in order to better explore the search space, while still solving the optimization problem as a whole. The feasibility and effectiveness of the proposed scheme are demonstrated through comparisons with other approaches for various load and generation scenarios on the IEEE 123-bus distribution system.

Network cost allocation methods for pay-as-bid peer-to-peer energy trading: A comparison

In pay-as-bid peer-to-peer (P2P) energy trading, various types of prosumers and consumers can participate, regardless of their offers. Thus, various types of participants impact the network differently. However, very few pay-as-bid P2P energy trading studies have specifically discussed appropriate compensation for network usage, although the market is implemented in existing utility-owned grids. Therefore, to improve the performance of pay-as-bid P2P energy trading, it is important to determine the appropriate compensation to utilities for network usage. This study aims to obtain an appropriate network cost allocation method for pay-as-bid P2P energy trading. Hence, the authors present a review of pay-as-bid P2P market mechanisms and various network cost allocation (NCA) methods. Additionally, a comprehensive evaluation framework is proposed to determine the most appropriate NCA method for the pay-as-bid P2P energy trading system. A comparison was made between various NCA methods to investigate the outcomes of the implementation of different NCA methods to various market conditions. The study constructs a case study based on the operator-oriented P2P model to represent the pay-as-bid P2P energy trading system. The simulation of pay-as-bid P2P energy trading with large participant number is applied in the IEEE 69-bus distribution system. The study concluded that applying the appropriate NCA method would improve the performance of pay-as-bid P2P energy trading operation.

Resilience Analytics for Interdependent Power and Water Distribution Systems

The occurrence of large-scale outages in power distribution systems (PDSs) caused, for instance, by extreme weather events has raised concerns on the cascading impact of the power outages on the resilience of other critical infrastructures, including water distribution systems (WDSs), whose healthy operation highly relies on the availability of power from PDSs. This paper proposes an analytics model for quantifying the interdependence between the resilience of power and water distribution systems. The proposed model first performs spatio-temporal outage analysis on the PDS and WDS to determine the PDS power serving capability and the resulting cascading impact on the WDS water serving capability during the occurrence of disruptions. The resilience interdependence is then determined by a proposed set of six metrics calculated using the power and water serving capability profiles, which trace and quantify the cascading impacts of power outages in the operation of WDSs during the degradation and recovery stages. The proposed analytics model is implemented on the IEEE 33-bus power distribution system supplying power for pumping stations of a 15-node test WDS, considering multiple cases of water storage capacity, single and multiple outage scenarios, and availability of distributed generation. The numerical results demonstrate that the proposed metrics track the temporal and spatial interdependence between the resilience of power and water distribution systems, providing a mechanism to identify weak spots, and assess options to enhance the resilience of the critical water infrastructure.

An improved equilibrium optimiser-based algorithm for dynamic network reconfiguration and renewable DG allocation under time-varying load and generation

This paper presents an improved equilibrium optimiser (IEO)-based method for dynamic hourly network reconfiguration (NR) with optimal allocation of dispatchable and non-dispatchable renewable distributed generator (DG) units in radial power distribution networks. In this work, solar- and wind-based DGs are considered non-dispatchable DG, while biomass DGs are considered dispatchable DG. The output of biomass DGs is adjusted according to the 24-hour load demand curve and that of solar and wind DG follow their hourly power generation. For this purpose, a multi-objective function is developed with consideration of hourly power loss minimisation and profit maximisation without violating the system’s operational constraints. The viability and efficacy of the proposed method are tested on IEEE 33-bus and 69-bus distribution systems for five distinct scenarios of network reconfiguration and renewable DG allocation. The analysis shows that the simultaneous NR and DG have a more substantial impact than considering the cases separately.

Optimal Operational Planning of RES and HESS in Smart Grids Considering Demand Response and DSTATCOM Functionality of the Interfacing Inverters

With countries moving toward renewable energy sources (RES), the need for dispatchability and storage solutions has become more prevalent. The uncertainty associated with wind turbine (WT) units and photovoltaic (PV) systems further complex a system with a high level of intermittency. This work addresses this problem by proposing an operational planning approach to determine the optimal allocation of WT units, PV systems, and hybrid energy storage systems (HESS) in smart grids. The proposed approach considers the uncertainties of the RES and load demand, price-based demand response, and distribution static compensator (DSTATCOM) functionality of the RES interfacing inverters. The operational planning problem is divided into two subcategories: (1) optimal long-term planning and (2) optimal operation. In the first problem, probabilistic models of RES and load reflect on the sizes and locations of the used RES and storage technologies. This allocation is further optimized via the optimal operation of the smart grid. The proposed operational planning approach is formulated as a nested optimization problem that guarantees the optimal planning and operation of the RES and HESS simultaneously. This approach is tested on the IEEE 33-bus distribution system and solved using meta-heuristic and mathematical algorithms. The effectiveness of the proposed approach is demonstrated using a set of case studies. The results demonstrate that the proposed approach optimally allocates the RES and HESS with a 30.4% cost reduction and 19% voltage profile improvement.

Research on grid-connected distributed generation considering relay protection

The access of distributed generation can cause some impacts on the distribution network, especially the change of the magnitude and direction of the short-circuit current may cause the relay protection device in the system to refuse or malfunction. Aiming at this problem, a mathematical model id founded in this paper, whose target is the maximum capacity of distributed generation. Besides normal constraints such as the node voltage and the total capacity, the current protection constraint is also taken into account. The genetic algorithm is used to optimize the capacity and location of the distributed generation in the IEEE33 bus distribution system model, and the obtained results verify the importance of taking into account the relay protection constraint.