Active Distribution Systems Research Articles

Stochastic multi-benefit planning of mobile energy storage in reconfigurable active distribution systems

This paper proposes a multi-benefit planning framework for mobile energy storage systems (MESSs) in reconfigurable active distribution systems (DSs). The goal of this framework is to improve the DS operation and reliability through achieving four objectives: (1) minimizing the DS costs, (2) minimizing the DS energy losses, (3) improving the DS voltage profiles, and (4) minimizing the expected energy not supplied (EENS) in the system. The developed framework determines optimal decisions associated with the network reconfiguration as well as the MESS sizes and locations, taking into account the variations in DS loads, energy prices, and renewable energy sources. The planning problem in this study is formulated as a stochastic multi-objective mixed-integer nonlinear programming problem, which is solved using a genetic algorithm (GA)-based hybrid optimization method. The proposed framework is tested using a case study of an active DS that includes various types of energy sources and load demands. Comparing the results of the developed planning framework with the base-case system demonstrates the efficacy of the planning framework and the techno-economic benefits of MESSs, where the proposed framework with MESSs successfully achieved significant improvements in the DS operating cost, energy losses, voltage profiles, and EENS. The advantages of MESSs are also verified through a comparison with stationary energy storage systems.

A proposed fault identification-based fuzzy approach for active distribution networks with photovoltaic systems

Recently, the issue of protection in active distribution networks has been greater than before due to mushrooming use of photovoltaic (PV) generation units. PV systems coupled to power electronics devices and their control strategies causes significant changes to fault current characteristics leading to maloperation of fault identification schemes. Therefore, this paper proposes a robust self-adaptive fault identification scheme that incorporates the presence of PV systems and considers the effect of uncertainty arising due to their high penetration levels. The real-time voltage and current measurements are captured locally by advanced one-end measuring facilities, considering the uncertain temperature and irradiation conditions, of solar generation units. First, the proposed scheme employs wavelet transform, statistical alienation concept, and fuzzy logic to identify the fault criteria. Then, it uses fuzzy concept-based the system features to obtain the optimal protection scheme setting. For simulation analysis, a 10 MW solar farm-connected to a real-world active distribution system is emulated in PSCAD-EMTDC simulator to create intensive study cases, and the proposed scheme is implemented in MATLAB software. Several fault parameters are examined in the scheme comprising different fault types, fault locations, and transition resistances. Also, the uncertainty arising from PV plants and different loading conditions are taken full consideration in the developed scheme. Simulation results have verified the validity of fault identification scheme; nonetheless, the computational burden is kept reasonable. The proposed scheme is investigated under white gaussian and impulsive noise. Furthermore, sensitivity analysis and comparative study are conducted in terms of accuracy, high decision speed, and sampling frequency variation. The proposed scheme succeeds to detect and classify internal faults for both on/off-grid operation modes and differentiate them from external ones in the case of on-grid mode. The proposed scheme has dominance performance including high speed fault detection within, a half cycle, and provides accuracy more than 98 %.

Day-Ahead Operational Planning for DisCos Based on Demand Response Flexibility and Volt/Var Control

Considering the integration of distributed energy resources (DER) such as distributed generation, demand response, and electric vehicles, day-ahead scheduling plays a significant role in the operation of active distribution systems. Therefore, this article proposes a comprehensive methodology for the short-term operational planning of a distribution company (DisCo), aiming to minimize the total daily operational cost. The proposed methodology integrates on-load tap changers, capacitor banks, and flexible loads participating in demand response (DR) to reduce losses and manage congestion and voltage violations, while considering the costs associated with the operation and use of controllable resources. Furthermore, to forecast PV output and load demand behind the meter at the MV/LV distribution transformer level, a short-term net load forecasting model using deep learning techniques has been incorporated. The proposed scheme is solved through an efficient two-stage strategy based on genetic algorithms and dynamic programming. Numerical results based on the modified IEEE 13-node distribution system and a typical 37-node Latin American system validate the effectiveness of the proposed methodology. The obtained results verify that, through the proposed methodology, the DisCo can effectively schedule its installations and DR to minimize the total operational cost while reducing losses and robustly managing voltage and congestion issues.

An Irradiance Driven Adaptive PQV Droop for Voltage Regulation in Active Distribution System

Gradual increasing installation of solar photovoltaic inverters (SPVIs) at the low voltage (LV) network causes over-voltage issue at the SPVI-connected point-of-common-couplings (PCCs). Further, this problem deteriorates the voltage regulation of distribution transformers in active distribution system (ADS) due to high R/X ratio of the lines. To mitigate such issues, IEEE-1547-2020 recommends the constant QV-droop and PV-droop characteristics in the SPVIs. However, due to lack of adaptability of such characteristics with the variable irradiance level, the controller in SPVIs may under-utilize capacity of the SPVIs. In this paper, an irradiance driven adaptive PQV (IAPQV) droop control technique is proposed for the SPVIs to improve the voltage regulation of an ADS as compared to IEEE-1547-2020 recommended droop characteristic. Moreover, a Two-Bit-Priority (TBP) algorithm is proposed for distributed coordination among the SPVIs. During overvoltage situations, the TBP assists in minimizing active power curtailment at the SPVIs. Performance of the proposed method is verified under different scenarios in ADS. The results are validated through MATLAB/Simulink to show that the proposed IAPQV is more effective as compared to the IEEE-1547-2020.

Precise Fault Location in Active Distribution Systems Using Unsynchronized Source Measurements

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

Active distribution system planning considering non-utility-owned electric vehicle charging stations and network reconfiguration

This paper introduces a new mixed-integer linear programming formulation for planning active distribution networks and non-utility-owned electric vehicle charging stations (EVCS), using network reconfiguration as a planning option. The method employs multi-objective optimization to address the commercial interests of the utility and the EVCS owner separately. In this manner, the utility makes investment decisions for network assets, such as replacing overloaded conductors and installing capacitor banks and voltage regulators, whereas the EVCS owner decides on the purchase of land and infrastructure for the EVCS installation. Furthermore, as an outstanding feature, the proposed formulation incorporates network reconfiguration within the planning options. The proposed model aims to minimize the total expected cost for both owners. A travel simulation algorithm provides the EVCS load profiles. At the same time, scenario-based optimization addresses uncertainties related to substation energy prices, wind speed, solar irradiance, electricity demand, adoption rate, and loading profile of plug-in electric vehicles. The effectiveness of the proposed model has been demonstrated in a 69-node network. The findings show that when deciding where to install the EVCS (network node), the objectives of both owner’s clash. This discrepancy, however, can be reduced through network reconfiguration. Finally, the scalability of the proposed method was evaluated using a real 134-node system.

Active Distribution System Synthesis via Unbalanced Graph Generative Adversarial Network

Active Distribution System Synthesis via Unbalanced Graph Generative Adversarial Network

A Multi-objective Optimization Planning Framework for Active Distribution System Via Reinforcement Learning

The effective planning of active distribution networks is crucial for utility companies to make informed decisions regarding investments in distributed generation, reliability assessment, reactive power planning, substation revisions, and feeder repositioning. However, the dynamic nature of the solution space makes it challenging for model-based optimization methods to ensure computational performance in active distribution network planning. To address this issue, this study proposes a planning method that focuses on improving computational performance through the continuous updating of the planning model’s solution space during the reinforcement learning training process. Based on simulations conducted on the IEEE 33-bus test system, the proposed planning strategy successfully enhances computational performance while minimizing investment costs compared to other strategies. With the proposed method, the investment cost and the operation cost are reduced by 32.42% and 23.91%, respectively.

Performance analysis of distributed power systems using error dynamics passive output feedback control

This paper presents a novel control approach for integrated converters using Exact Tracking Error Dynamics Passive Output Feedback control and Exact Static Error Dynamics Passive Output Feedback control. By continuously injecting active distributed power systems (DPS) power sources into the utility grid and loads, the approach reduces harmonic current distortion and maintains unity for the grid’s power factor. The paper compares existing control methods, such as P, PI, proportional–integral–derivative (PID), energy structuring and damping infusion and interconnection and damping assignment—passivity-based control and considers the impacts of instantaneous fluctuations in reference current elements on the AC side and oscillations in DC voltage generated by the DC-link voltage. Relevant switching state functions are designed, and if the maximum power can be fed exceeds the required power from grid-connected loads, the true, reactive, and harmonic current elements of loads are adjusted dynamically, resulting in in-phase sinewave grid currents. The performance analysis is performed using MATLAB/Simulink, and a smaller research prototype is built and discussed.

Mobile compressed air energy storage for active distribution systems

Efficient energy storage technology is one of the key elements to enhance the flexibility, economy, and security of the power system. With the continuous development of energy storage technology, containerized mobile energy storage is coming into view, which has offered promising opportunities to improve distribution network (DN) performances and grid operating factors against emergencies. This paper proposes the concept of mobile compressed air energy storage (CAES) for an electric DN. The movable air storage tanks with stored energy are transported by trucks and placed at some distribution nodes/buses to improve DN performance. To overcome routing challenges for trucks, the configuration of the grid is mapped on the urban region of the city of Sydney, Australia, using Google map’s Application Programming Interface. This approach can accurately model distances between current and targeted locations, unavailability of tanks during traveling, route congestions, and fuel consumptions. A new heuristic mathematical method is proposed in this paper to convert the constraints of the mobile CAES (MCAES) model into feasible search spaces, which significantly improves the convergence quality and speed. Operating results for both stationary and mobile CAESs are presented and compared. The methodology is applied to IEEE 136-bus DN in addition to IEEE 33-bus DN to demonstrate the competence of MCAES for larger-scale grids in optimizing the total operating profit, active power loss, energy not supplied, and voltage stability index of the grid. The crucial application of the proposed model can deal with natural disasters to avoid large-scale power outages and, eventually, mitigate the power system damages.

Impact Assessment of Optimal Integration of Combined DG and D-STATCOM Allocation for Active Distribution System Enhancement with Loading Variations

Impact Assessment of Optimal Integration of Combined DG and D-STATCOM Allocation for Active Distribution System Enhancement with Loading Variations

Dynamic event-based forecasting-aided state estimation for active distribution systems subject to limited communication resource

In this paper, the dynamic event-based forecasting-aided state estimation (FASE) method is developed to deal with the state estimation problem of the active distribution system (ADS) subject to communication constraints and non-linear measurements. The proposed method first constructs a state-space model of the ADS to describe system state time evolution. Secondly, to use communication resources more effectively, the dynamic event-triggered scheme (ETS) is exploited to schedule the data transmission. Aiming at the problem of the ADS in the presence of the non-linear measurement, the Gaussian integral is approximated by the spherical cubature rule to obtain the mean and covariance of the state variables after non-linear transformation. Moreover, the upper bound of the estimation error covariance containing non-triggering errors is derived, and then minimized by suitably designing filter gain, thus developing the dynamic event-triggered cubature Kalman filter (DET-CKF) algorithm to perform state estimation for ADSs. Finally, a series of simulation experiments are conducted to verify the effectiveness of the developed FASE method.

Adaptive Directional Overcurrent Relay Coordination in Microgrid Considering Optimal Full Setting Parameters

This article proposes a novel approach for the optimal coordination of adaptive directional overcurrent relay (ADOCR) for modernized active distribution system (ADS) with microgrid (MG) functions. The goal is to keep the coordinating circumstances while reducing the protective relay operation time, as much as possible. With the proposed round-off mixed-integer particle swarm optimization (ROMI-PSO), the setting parameters are not limited to only relay time multiplier setting (TMS) and pickup current setting (PS) but can include the discrete parameter as relay characteristic curve setting (CS). In the proposed framework, The CS can be both IEC and IEEE standard curves in the proposed framework. Four buses radial system and IEC-benchmark MG system were used in a number of simulation experiments. Comparing the suggested methodology to other current methods revealed that it is more effective at determining the minimum relay operating times and ensuring the proper operation of protections under various fault allocations and operating circumstances.

Fault diagnosis and protection strategy based on spatio-temporal multi-agent reinforcement learning for active distribution system using phasor measurement units

Active distribution system (ADS) requires intelligent sensors to provide real-time data. Due to the harmonic distortion and sparse reward function, the multi-agent reinforcement learning strategy has the fuzzy characteristic and slow convergence. This work proposes a model-free spatio-temporal multi-agent reinforcement learning (STMARL) strategy for the spatio-temporal fault diagnosis and protection. The augmented-state extended Kalman filter tracks spatial–temporal sequences measured by phasor measurement unit (PMU) and feed into the diagnosis model. The supervised multi-residual generation learning (SMGL) model is constructed to diagnose the single-phase-to-ground fault. Based on spatio-temporal sequences, the SMGL diagnosis model integrates the ADS protection as a Markov decision process and the protection operation is quantified as the STMARL reward. In the hybrid multi-agent framework, the STMARL protection strategy converges faster based on the higher-level agent suggestion without the global reward. The STMARL protection strategy is validated in the IEEE 34-bus distribution test system with 10 PMUs. Comparing with the SOGI, WNN, Sarsa and DDPG algorithms, in the common fault conditions, the STMARL protection strategy shows better performance in the high dynamic environment with the response time 1.274 s and the diagnosis accuracy rate 97.125%. The STMARL diagnosis and protection strategy guides ADS in a stable operation coordinate with all PMUs, which lays foundation for the synchronous measurement application in the smart grid.

Three-stage resilience-oriented active distribution systems operation after natural disasters

Increasing energy demand and the industrialization of communities have been the leading causes of climate change. As a result, natural disasters have increased blackouts in power systems. Climate change mitigation should be a global goal of communities. Since natural disaster occurrence has risen recently, rapid system restoration after natural disasters is essential. Mitigation of global warming is inextricably linked to the mechanisms and architecture of energy systems. Energy resilience is a multi-dimensional solution to this problem. In this research, resilient strategies for dealing with natural disasters have been examined. Implementing distributed energy resources (DERs), smart distribution networks, and defining load priority based on consumers’ preferences have made a viable solution to the post-disaster operation. Previous efforts cared more about the generation and transmission sectors. This study focused on active distribution systems (ADSs) because distribution systems, as a vital part of energy systems, have not sufficiently contributed to crisis management. A framework for resilience enhancement to smartening conventional distribution systems against natural disasters has been proposed. Three-stage socio-technical optimization was investigated, including optimal allocation of battery storage systems (BESSs), intentional islanding, and optimal power flow (OPF) in MATLAB and DIgSILENT. This optimization presents a decision-making tool for distribution system operators to quickly prepare the system against natural disasters. By applying eight scenarios in the proposed system, in the best scenario (CS4), the recovered power value (RPV) has increased from 38% to more than 99% (more than 61% increase in the resilience index). The amount of supplied load increased from 56% to 92%. Furthermore, prioritizing crucial consumers and reconfiguration are two primary crisis management tools. This approach creates a solid ground for avoiding the social consequences of natural disasters like electrocution and power outages of priority consumers. Failure to apply these two techniques would result in a 21% reduction in the RPV.

A multi-objective decentralized optimization for voltage regulators and energy storage devices in active distribution systems

Centralized optimization methods have been widely used to manage the operation of distribution systems. However, these methods have some restrictions, such as the high computational cost, the reliance on a centralized computer system, and the lack of data privacy. To overcome these limitations, decentralized optimization methods have been proposed in recent years. Decentralized methods divide the control variables of the centralized optimization problem over several controllers, and each controller solves its own subproblem independently. This paper presents a multi-objective optimization framework for managing the operation of distribution systems. The primary objective of the proposed method is to optimize the tap positions of voltage regulators and charging and discharging powers for energy storage devices locally through a decentralized coordination process. The used objective functions take into account the voltage profile within the network, the lifetime of the devices, and the energy losses in the systems. Two decentralized methods, based on the Advanced Arithmetic Optimizer algorithm and the Profile Steering approach, are proposed to address the limitations of centralized optimization methods. The decentralized methods aim to improve the reliability and efficiency of the optimization process, while also minimizing communication and computational costs. The proposed methods are evaluated and compared to a centralized approach using the IEEE 33 and 69 bus systems. The results demonstrate that the proposed decentralized methods can effectively resolve voltage problems, minimize energy losses, and find high-quality solutions with improved computational efficiency compared to the centralized approach.

Roadmap to modernization of line protection in active distribution systems

The nature of distribution systems is changing from passive to active networks due to the integration of Distributed Energy Resources, especially Inverter-Based Distributed Generation (IBDG). This change presents challenges for existing fuse-based unidirectional protection schemes, as active networks experience bidirectional flow of power and small fault current contribution from IBDGs. Such fault behavior can impact the protection scheme’s sensitivity and selectivity, compromising the reliability of active distribution networks and leading to customer outages due to improper fault isolation. The problem is exacerbated in networks that allow the islanded mode of operation. Upgrading the entire protection system to microprocessor-based relays is expensive. Instead, this work proposes a planning strategy to optimally upgrade protective devices. The proposed framework is able to incorporate IBDG integration plans, distribution system reliability targets, protection requirements, and economic viability throughout the planning horizon. The obtained strategy determines the optimal number, location, and deployment time of protective devices in consideration of network characteristics. The developed optimization model is formulated as a Mixed-Integer Linear Programming problem. The results verify the proposed framework’s ability to economically upgrade the protection scheme while enhancing the reliability of the active distribution network with IBDGs.

Supply–demand scenario generation of active distribution systems using conditional generative adversarial networks

The intermittency of distributed renewable energy resources (DRERs) and the fluctuation of load demand make the supply–demand scenario of active distribution systems significantly different from that of traditional radial distribution systems. Scenario generation is an effective tool for describing supply–demand uncertainty and is therefore of great significance for the planning and operation of active distribution systems with a high penetration of DRERs. This paper proposes a supply–demand scenario generation method for active distribution systems using conditional generation adversarial networks (CGANs). The proposed method is data-driven and utilizes neural networks to learn and represent distribution laws from data without revealing the expression probability distribution. Specifically, it first extracts the features information of historical scenarios as the conditional control vectors (CCVs), then embeds the CCVs to the input of the generator network and discriminator network to achieve conditional control. The proposed approach is verified by the actual time series wind power data and load profiles. Results show that the proposed method can efficiently generate supply–demand scenarios satisfying various statistical characteristics and capture complex spatial–temporal correlations.

Outage Management of Active Distribution Systems With Data Fusion From Multiple Sensors Given Sensor Failures

Complex scenarios of multiple faults, sensor failures, and the fault current from distributed generations (DGs) pose a challenge for efficient outage management in distribution systems. To solve the issue, this study proposes a new algorithm for sequential determination of outage scenarios with data fusion from multiple sensors, i.e., smart meters and remote fault indicators (RFIs). To this end, an active distribution system is modeled as an undirected graph with vertices representing load zones, protective devices and RFIs, and edges denoting their adjacency. Set operation is proposed to derive the expected RFI alarms for each load zone if faulted and smart meter outage reports for each protection device. This serves as an input to the proposed evidence-driven methodology to sequentially quantify the credibility of the potential outage scenarios supported by RFIs and smart meters. By ranking the credibility, the most credible scenario of the activated protection and fault location, either on the primary feeder or laterals, is determined for expedited power recovery. Test cases of the IEEE System and a utility circuit show that the proposed approach outperforms the state-of-the-art technologies in terms of its accuracy and computational efficiency, leading to more efficient outage management of distribution systems.

A Method for Calculating Aggregate Flexibility Parameters of Virtual Power Plant Based on Improved Column and Constraint Generation Algorithm

The virtual power plant (VPP) can aggregate massive, dispersed, and heterogeneous distributed energy resources (DERs) and provide an effective energy management way for an active distribution system (ADS). In this paper, three parameters of upper and lower limits of active power output, ramping, and discharging energy is used to describe the aggregate flexibility of VPP. Based on this, a two-stage robust optimization (RO) model with dispatch instructions (DI) from the superior power grid (SPG) as uncertainty variables was constructed. Considering that the range of DI from the SPG changes during the iteration of the column and constraint generation (C&CG) algorithm, a position set is proposed to record the boundary information of the uncertain set of DI issued by the SPG when the sub-problem (SP) finds the worst scenario in each iteration, and the corresponding constraints are added to the master problem (MP). For the solution of SP, the bilevel max-min model is firstly transformed into a single-level bilinear programming (BLP) model by the duality theory and then solved by the concave-convex process (CCP) iterative method. Compared with transforming the BLP problem to a mixed integer programming (MILP) problem based on Karush-Kuhn-Tucker (KKT) condition, it can not only meet the engineering accuracy but also greatly save the solution time. Finally, the effectiveness of the proposed model and algorithm was verified on a modified IEEE 33-bus system.