Smart Distribution System Research Articles

Smart and sustainable city logistics: Design, consolidation, and regulation

Growing e-commerce delivery increases traffic congestion and demands for smart last-mile city distribution systems. In this context, municipalities and logistics service providers aim towards sustainable and profitable city logistics, which are often conflicting goals. Whether to use single-echelon or two-echelon systems with micro-depots and city freighters as well as quantifying the benefits of shipment consolidation between independent providers under different system designs are unanswered questions in academia and practice.We provide a general methodological framework based on continuous approximation theory to study the design of city logistics networks with multiple logistics service providers that choose transportation strategies, each defined by a network structure and specific transportation modes. We provide analytical results on the comparison of different transportation strategies. By providing thresholds on the benefit of consolidated systems, we analytically quantify shipment consolidation effects in city centers. We then embed this continuous approximation into a mixed-integer non-linear program and study parameter sensitivities for a real-world case study.Our results show that higher customer density and longer distances between depots and distribution areas promote 2-tier transportation strategies. Consolidation between logistics service providers allows for emission reductions independent of the transportation strategy and correlate with cost reductions up to 50% on the lower echelon. Outsourcing promotes 2-tier last-mile deliveries with city freighters and in a city with heterogeneous districts, it is beneficial to use a hybrid network that includes both single- and two-echelon strategies using multiple transportation modes for different demand classes.

Explicit reliability incorporated switches planning of smart distribution system with high flexibility

Different types of switches play different roles in the fault management procedure in particular context of smart grid, which have different impact on system reliability. The motivation behind this paper is to develop an explicit reliability assessment method adapting to flexible deployment of circuit breakers (CBs), remote-controlled switches (RCSs), and manual switches (MSs), which can be further integrated into switch planning models analytically. The peculiar of this paper is to use the propagation constraints of fictitious power flows to model three stages of fault management procedures. Based on the fictitious power flows, the placement and action for CBs, RCSs, and MSs are modeled in detail as linear constraints, considering power flow constraints in fault management procedure. Therefore, this MILP model can be easily integrated into an exact mathematical optimization model. Finally, the proposed method is tested in 37, 85, 137, 417 node distribution network systems to verify the efficiency of the algorithm. Besides, the necessity of considering flexible location of three types of switches and its application on switch planning are verified.

Storage-integrated virtual power plants for resiliency enhancement of smart distribution systems

With emergence of Flexible Renewable Virtual Power Plants (FRVPPs) as the aggregator of renewable energy systems and flexibility resources such as demand response programs and electric vehicles (EVs) in the Smart Distribution Network (SDN), FRVPPs are expected to have significant capability resiliency enhancement against natural disasters. This paper focuses on the resilient operation of SDN influenced by FRVPPs for flood and earthquake conditions and minimizes the total cost of the SDN against natural disasters. The constraints of the problem include AC optimal power flow equations, resiliency constraints and FRVPP constraints. The uncertainties of load, energy prices, renewable power generation, EVs demand, accessibility of SDN devices and FRVPP components are modeled using a hybrid stochastic-robust strategy. A hybrid metaheuristic optimization algorithm, based on combination of krill herd optimization and sine-cosine algorithm, has been used to solve the proposed optimization model. The achieved results on the IEEE 69-bus SDN approve the significant capability of FRVPP's in improving the resiliency of the SDN against flood and earthquake. Based on the simulation results, the mentioned hybrid algorithm is capable of extracting the optimal point in lower computing time compared to non-hybrid algorithms. Its standard deviation in the final response is very low, around 0.93 %, which means that the above-mentioned solver extracts an almost unique solution. In addition, the proposed design with energy management of FRVPPs has been able to improve resilience by about 95 % compared to power flow studies. This number is about 33 % and 42 % to improve the state of energy loss and voltage profile, respectively. Of course, these characteristics obtained for FRVPP are the result of the optimal performance of mobile energy storage (EVs) along with the demand response program.

A Market Framework for Collaboration Between Distribution System Operator and Green Bank Agent

This paper proposes a market framework to approach economic dispatch as a subproblem in smart distribution systems. Four subproblems are associated with four different submarkets (SMs) in the presented framework. Market participants are eligible to bid in different submarkets based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">type</i> of their distributed generation (DG) unit. To effectively tackle the conflicts among objective functions of different submarkets, the corresponding formulated subproblems are assigned to two different agents, namely distribution system operator (DSO) and a green bank agent (GBA). A communication mechanism is designed within the proposed framework such that DSO and GBA <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">compromise</i> their solutions and find a tradeoff to reasonably address the concerns of the four subproblems within SMs 1-4. The effectiveness of the proposed market framework is validated on a well-known 136-bus distribution system, which is modified to contain wind turbine DG aggregators, photovoltaic DG aggregators, and diesel-engine DG aggregators.

Multi-Stage Resilience Management of Smart Power Distribution Systems: A Stochastic Robust Optimization Model

Significant outages from weather and climate extremes have highlighted the critical need for resilience-centered risk management of the grid. This paper proposes a multi-stage stochastic robust optimization (SRO) model that advances the existing planning frameworks on two main fronts. First, it captures interactions of operational measures with hardening decisions. Second, it properly treats the multitude of uncertainties in planning. The SRO model coordinates hardening and system operational measures for smart power distribution systems equipped with distributed generation units and switches. To capture the uncertainty in the incurred damage by extreme events, an uncertainty set is developed by integrating probabilistic information of hurricanes with the performance of overhead structures. A novel probabilistic model for the repair time of damaged lines is derived to account for the uncertainty in the recovery process. A solution strategy based on the integration of a differential evolution algorithm and a mixed-integer solver is designed to solve the resilience maximization model. The proposed approach is applied to a modified IEEE 33-bus system with 485 utility poles and a 118-bus system with 1841 poles. The systems are mapped on the Harris County, TX, U.S. Results reveal that optimal hardening decisions can be significantly influenced by resilience operational measures.

Image Information Recognition and Classification of Warehoused Goods in Intelligent Logistics Based on Machine Vision Technology

To sort out warehouse management problems in smart factories, smart warehousing and in-plant smart distribution systems are needed to achieve the goal of lean logistics and distribution in smart factories. There are still some pressing problems in the research on images of warehoused goods in intelligent logistics. For example, a solution hasn’t been found yet to recognise multiple types of warehoused goods in different shapes and colours; static vision image processing solutions have a poor performance in optimising recognition speed and classification accuracy. In response, this paper unveils a study on the image information recognition and classification of warehoused goods in intelligent logistics based on machine vision technology. It presents a process related to warehouse management in intelligent logistics and a corresponding system architecture. It also constructs a YOLOv3 model for the image information recognition and classification of warehoused goods in intelligent logistics. The paper elaborates on the prior box settings and loss function correction methods, and finishes optimising the YOLOv3 model. Experimental results verified the effectiveness of the constructed model.

Optimal scheduling of DG and EV parking lots simultaneously with demand response based on self‐adjusted PSO and K‐means clustering

AbstractRecently, the proliferation of distributed generation (DG) has been intensively increased in distribution systems worldwide. In distributed systems, DGs and utility‐owned electric vehicle (EV) to grid aggregators have to be efficiently scaled for cost‐effective network operation. Accordingly, with the penetration of power systems, demand response (DR) is considered an advanced step towards a smart grid. To cope with these advancements, this study aims to develop an innovative solution for the day‐ahead sizing approach of energy storage systems of EVs parking lots and DGs in smart distribution systems complying with DR and minimizing the pertinent costs. The unique feature of the proposed approach is to allow interactive customers to participate effectively in power systems. To accurately solve this optimization model, two probabilistic self‐adjusted modified particle swarm optimization (SAPSO) algorithms are developed and compared for minimizing the total operational costs addressing all constraints of the distribution system, DG units, and energy storage systems of EV parking lots. The K‐means clustering and the Naive Bayes approach are utilized to determine the EVs that are ready to participate efficiently in the DR program. The obtained results on the IEEE‐24 reliability test system are compared to the genetic algorithm and the conventional PSO to verify the effectiveness of the developed algorithms. The results show that the first SAPSO algorithm outperforms the algorithms in terms of minimizing the total running costs. The finding demonstrates that the proposed near‐optimal day‐ahead scheduling approach of DG units and EV energy storage systems in a simultaneous manner can effectively minimize the total operational costs subjected to generation constraints complying with DR.

Centralized optimal management of a smart distribution system considering the importance of load reduction based on prioritizing smart home appliances

AbstractThe distribution system's economic operation is significantly impacted by the management of distributed generation (DG) resources, energy storage (ES), and controllable loads. The paper employs a smart distribution system that incorporates dispatchable and non‐dispatchable DG resources, as well as battery storage, in addition to the demand response (DR) scheme. New modelling was performed in hourly steps to achieve the optimal unit commitment. In smart homes, appliances are prioritized and classified into four types: adjustable, interruptible, shiftable, and uncontrollable loads. Load reduction in smart homes is also considered based on load prioritization and customer participation in the DR scheme to achieve the proposed scheme's objectives. The proposed method considers costs associated with microturbines (MTs), including manufacturing, start‐up, shutdown, and pollution. Additionally, planning is conducted to purchase or sell electricity to the upstream network. The simulation is run on an IEEE 33‐bus system to demonstrate the proposed method's effectiveness. The system is assumed to be capable of operation in both island and grid‐connected modes. The results demonstrate the proposed approach's efficacy in load reduction, operation cost, and execution time.

Concepts, Configurations, and Challenges of Solid-State Transformer: A Review

Abstract: Rapid and suddenly increasing use of small distributed renewable energy resources (DRER) and distributed energy storage (DES), reduced compact dynamic electrical power consumer-like electrical vehicle loads, the requirement of bidirectional power flow communication network to exchange critical information, other ancillary services, etc. has to be envisaged and fulfilled by a future smart distribution grid system. A solid-state transformer (SST) has more potential solutions to provide stable and efficient distribution system operation to overcome the above problem.From various designs of converter control schemes, the solid-state transformer concept has been evaluated and summarised. The benefits and downsides of converters in the AC/DC, DC/DC, and DC/AC stages are studied for the best configuration. The High Frequency Transformer(HFT) is a main part of SST.The design and optimization of a HFT is approached on the objective of minimising the weight and size of the SST.Researchers also face several challanges in the prototype design and implementation of SST before operating effectively in the distribution system which is presented . Some of expected solutions and future recommendations for the establishment of the solid state transformer for future smart electrical distribution systems are discussed.

A Multi-Agent Approach for Self-Healing and RES-Penetration in Smart Distribution Networks

Smart grid technology has gained much consideration recently to make use of intelligent control in the automatic fault-detection and self-healing of electric networks. This ensures a reliable electricity supply and an efficient operation of the distribution system against disasters with minimum human interaction. In this paper, a fully decentralized multi-agent system (MAS) algorithm, for self-healing in smart distribution systems, is proposed. The novelty of the proposed algorithm, compared to related work, is its ability to combine the zone and feeder agents, specified for system self-healing, with micro-grid agents. This enables the system to successfully achieve functions of fault locating and isolation along with service-restoration using expert rules while considering both operational constraint and load priorities. Meanwhile, managing the power flow and controlling the distributed generator (DG) contribution, in the considered network, is a bonus merit for the proposed algorithm. Hence, system self-healing as well as strengthening energy security and resiliency are guaranteed. The proposed algorithm is tested on a 22 kV radial distribution system through several case-studies with/without a DG wind-energy source. The employed agents are implemented in the Java Agent Developing Framework (JADE) environment to communicate and make decisions. Power system simulation and calculations are carried out in MATLAB to validate the agents’ decisions.

Reliability Improvement of the Smart Distribution Grid Incorporating EVs and BESS via Optimal Charging and Discharging Process Scheduling

The use of large-scale electric vehicles (EVs), along with technological advances in battery energy storage systems (BESS), offers numerous technical and economic benefits to the smart distribution system. Parking lots not only provide EV owners with the opportunity to charge their BESS but also contribute power to the grid via vehicle-to-grid (V2G) technology, which significantly improves the reliability of distribution systems. In addition, parking lots equipped with V2G capability can participate in the electricity market as a producer. In this study, the reliability of a smart radial distribution system in the presence of EVs is investigated. It should always be borne in mind that parking lots can improve system reliability if the charging and discharging strategies are optimally and correctly scheduled. On the contrary, the parking lot participates in sending power to the grid in a situation where the profit is appropriate. Accordingly, in this study, first, five optimal strategies for scheduling the process of EV charging and discharging in a parking lot equipped with V2G capability are proposed to maximize parking profits. The proposed strategies include constraints on the amount of power exchange between the parking lot and the distribution system and the random and stochastic nature of quantities, such as when EVs enter and exit the parking lot and their BESS charge amounts when entering the parking lot. The results demonstrate that using the proposed optimal charging and discharging strategies scheduling increases the parking profit by 31% and also improves the reliability of the understudy distribution system by 9%.

Situational Awareness for Smart Distribution Systems

In recent years, the accelerating climate change and intensifying natural disasters have called for more renewable, resilient, and reliable energy from more distributed sources to more diversified consumers, resulting in a pressing need for advanced situational awareness of modern smart distribution systems [...]

Online management and assessment of power quality issues through smart metering in smart grid architecture

With the growing Smart Grid concept, it has become very important to formulate and device the ways of intelligent control and supervision of electrical power distribution systems. As the distribution part is a major area among generation, transmission, and distribution, so without complete automation of the distribution system, a complete smart grid concept cannot be achieved. Therefore, a smart distribution system concept has been developed in this paper, where a vital role plays in the operation of smart meters having communication capabilities. This paper proposed two-level control architecture. MATLAB/SIMULINK model has been developed. The model studied consists of two low voltage (LV) distribution networks falling under one substation, thus two local controllers and one main controller have been modeled. To simulate the functionalities of controllers, different case scenarios such as overcurrent protection, power theft, no shutdown of essential loads (like hospitals, VIPs), available power loss, and phase unbalancing for 60 houses together in two areas have been studied and were compared with the real-time system through OPAL-RT. Performance of Dynamic Voltage Restorer (DVR) in case of voltage sag/swell and harmonics have been shown for enhancing power quality in distribution systems.

Network performance evaluation of smart distribution systems using smart meters with TCP/IP communication protocol

The escalating significance of system reliability and resilience is transforming the planning and operation of contemporary distribution systems. To accomplish self-healing against power outages, advanced emerging technologies and devices such as smart meters are being deployed in the distribution systems. This work attempts to evaluate the performance of the smart distribution networks, where the communication protocol TCP/IP integrated with smart meters plays an essential responsibility. TCP/IP as a communication protocol has been used to send the data to the real world from simulation and receive it back into the simulation without losing data. Two-level control architecture has been proposed. The model has been designed using MATLAB/SIMULINK. To achieve it, the model consists of two LV distribution networks falling under one substation. Thus, two local controllers and one master controller have been modeled. Different single-phase loads (like domestic loads, colony loads having several houses, VIPs loads, shop loads, hospital loads, school loads, college loads, etc.) have been modeled as an amalgamation that has constant impedance and constant power loads. To simulate the functionalities of controllers, different case scenarios such as overcurrent protection, power theft, no shutdown of essential loads (like hospitals, VIPs), available power loss, and phase unbalancing in 42 residential loads altogether in two different areas have been studied and validated with real-time simulator through OPAL-RT.

Multi-population evolutionary computing based multi-agent smart distribution system service restoration

Multi-population evolutionary computing based multi-agent smart distribution system service restoration

Optimal Planning of Electric Vehicle Charging Station along with Multiple Distributed Generator Units

Saving energy through the minimization of power losses in a distribution system is a key activity for efficient operation. Distributed Generation (DG) is one of the most efficient approaches to minimize losses. With increase in installation of Electric Vehicle Charging Stations (EVCSs) for Electrical Vehicles (EVs) in larger scale, optimal planning of EVCSs becomes a major challenge for distribution system operator. With increased EV load penetration in the electricity system, generation-demand mismatch and power losses increases. This results in poor voltage level, and deterioration in voltage stability margin. To mitigate the adverse impacts of increasing EV load penetration on Radial Distribution Systems (RDS), it is essential to integrate EVCSs at appropriate locations. The EVs integration into smart distribution systems involves Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) in charging and discharging modes of operation respectively for exchange of power with the grid thus resulting in energy management. The inappropriate planning of EVCSs causes a negative impact on the distribution system such as voltage deviation and an increase in power losses. In order to minimize this, DG units are integrated with EVCSs. The DGs assist in keeping the voltage profile within limitations, resulting in reduced power flows and losses, thereby enhancing power quality and reliability. Therefore, the DGs should be optimally allocated and sized along with the EVCS to avoid problems such as protection, voltage rise, and reverse power flow problems. This paper showcases a method to minimize losses using optimal location and sizing of multiple DGs and EVCS operating in G2V and V2G modes. The sizing and location of different types of DG units including renewables and non-renewables along with EV charging station is proposed in this study. This methodology overall reduces the power losses and also improves voltages of the network. The implementation is done by using the Simultaneous Particle Swarm Optimization technique (PSO) for IEEE 15, 33, 69 and 85 bus systems. The results indicate that the proposed optimization technique improves efficiency and performance of the system by optimal planning and operation of both DGs and EVs.

Fast Coscheduling of Discrete and Continuous Resources in Active Distribution Systems

This article proposes a novel fast method for flexibility scheduling and steady-state voltage control in smart distribution systems enabled with both conventional discrete controllable devices (DCDs), e.g., capacitor banks and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> -load tap changers, and continuous and faster control devices, e.g., distributed energy resources, renewable resources, and static voltage regulators. By approximating the active and reactive losses, a linear branch flow model is presented for the optimal power flow (OPF) problem in distribution systems. Accurate models are also proposed for DCDs which suit the proposed OPF formulation and require fewer binary variables compared to those available in the literature. Conservation voltage reduction (CVR) scheme is modeled in the proposed OPF methodology. The CVR effects on energy saving, peak-load reduction, line losses, and transformer losses are analyzed in the case studies. As shown in various case studies, the proposed method is fast enough for quasi-real-time applications and is efficacious in minimizing the system operation cost, holding an acceptable level of power quality based on the available standards and evading the conflict between control actions.

Energy management and control system for microgrid based wind-PV-battery using multi-agent systems

Energy generation is currently evolving into a smart distribution system that incorporates several green energy resources at a distributed level, ensuring that clean energy is generated without releasing harmful gases, that operational procedures are consistent, and that energy management and supervision arrangements are improved. This paper proposes a multi-agent system-based microgrid energy management and proper control in distributed systems. For the complexity of energy management in distributed systems, a multi-agent system-based decentralized control architecture was developed. The proposed technique is based on several smart agents, each agent is based on the microgrid data for energy management and frequency control. The proposed energy management system based on the multi-agent system was tested by simulation under renewable resource fluctuations and seasonal load demand. The simulation results show that the proposed energy management system proved to be more resilient and high-performance controls than conventional centralized energy control systems.

Quantifying the Capacity Credit of Idc-Based Demand Response in Smart Distribution Systems

Quantifying the Capacity Credit of Idc-Based Demand Response in Smart Distribution Systems

Improved Voltage Control Methodology for Smart Distribution System with Dispersed Generation

Improved Voltage Control Methodology for Smart Distribution System with Dispersed Generation