Test Feeder Research Articles

Management of prosumers using dynamic export limits and shared Community Energy Storage

Integration of Distributed Energy Resources (DERs) can introduce challenges such as Over-Voltage (OV) and line congestion in distribution networks. Recently, the concept of dynamic export limits as well as Community Energy Storage (CES) have gained attention as potential solutions for these challenges. Within these concepts, this paper presents a novel framework where the Distribution Network Service Provider (DNSP) provides prosumers with dynamic export limits for networks with a CES. Moreover, this framework enables prosumers (through leasing) and the DNSP to collaboratively manage the CES. In each time step, the DNSP calculates the charging/discharging power for the unleased portion of the CES and collects data on the expected export and charging/discharging power from prosumers leasing the CES. Subsequently, while considering network voltage and current limits and a fairness objective, the DNSP computes and communicates dynamic export limits to prosumers, allowing them to manage their assets accordingly. The performance of the proposed framework is evaluated via various case studies conducted on a real-world LV test feeder with 49 prosumers. The results emphasize the critical role of dynamic export limits in resolving OV and congestion issues in DER-rich networks. Furthermore, the CES proves to be effective in reducing active power curtailment and increasing total generation, with a notable increase of 4.6%.

Solid-state transformer modelling in power flow calculation

This article proposes a novel Solid-State Transformer (SST) power flow (PF) model which is implemented with Modified Augmented Nodal Analysis (MANA) formulation using Newton-Raphson (NR) algorithm. Any network topology can be handled by the MANA formulation, and it can be easily and methodically expanded to include other components, like SST. Since various types of buses can have various type converters and SSTs, the sum of line flows are used instead of classical bus power injections equations. The proposed approach is coded in MATLAB and tested on an IEEE 33 bus distribution test feeder. The results are compared with Holomorphic Embedding PF method (HE-PFM) and with existing methodology in literature. In line with the findings, the proposed approach achieves accurate representation of SST, better convergence characteristics with a faster simulation speed compared to HE-PFM.

Aplicación del algoritmo de enjambre de salpas al balance de fases en sistemas trifásicos asimétricos

This research addresses the optimal phase-balancing problem by applying a master-slave optimization methodology. The master stage defines the load connections per node using a discrete codification, while the slave stage evaluates each load configuration provided by the master stage via a three-phase power flow. For the master stage, the salp swarm algorithm (SSA) was selected, which is an efficient bio-inspired technique to deal with continuous and discrete nonlinear optimization problems. The slave stage employed the matricial backward/forward power flow method for three-phase asymmetric networks. Numerical simulations in IEEE test feeders composed of 8, 25, and 37 nodes confirm the effectiveness of the SSA approach in finding efficient solutions regarding the expected grid power losses after optimal load phase-swapping. Numerical comparisons with the vortex search algorithm, the Chu & Beasley genetic algorithm, and the crow search algorithm demonstrate the effectiveness of the proposed methodology in dealing with the studied problem. All numerical validations were carried out in the MATLAB programming environment.

Multi-agent graph reinforcement learning for decentralized Volt-VAR control in power distribution systems

Volt/Var control (VVC) is a crucial function in power distribution systems to minimize power loss and maintain voltages within allowable limits. However, incomplete and inaccurate information about the distribution network makes model-based VVC methods difficult to implement in practice. In this paper, we propose a novel multi-agent graph-based deep reinforcement learning (DRL) algorithm named MASAC-HGRN to address the VVC problem under partial observation constraints. Our proposed algorithm divides the power distribution system into several regions, each region treated as an agent. Unlike traditional model-based or global-observation-based DRL methods, our proposed method leverages a practical decentralized training and decentralized execution (DTDE) paradigm to address the partial observation constraints. The well-trained agents gather information only from their interconnected neighbors and realize decentralized local control. Numerical studies with IEEE 33-bus and 123-bus distribution test feeders demonstrate that our proposed MASAC-HGRN algorithm outperforms the state-of-art RL algorithms and traditional model-based approaches in terms of VVC performance. Moreover, the DTDE framework exhibits flexibility and robustness in extensive robustness experiments.

Resilience of modern power distribution networks with active coordination of EVs and smart restoration

AbstractIn this modern era of cyber–physical–social systems, there is a need of dynamic coordination strategies for electric vehicles (EVs) to enhance the resilience of modern power distribution networks (MPDNs). This paper proposes a two‐stage EV coordination framework for MPDN smart restoration. The first stage is to introduce a novel proactive EV prepositioning model to optimize planning prior to a rare event, and thereby enhance the MPDN survivability in its immediate aftermath. The second stage involves creating an advanced spatial–temporal EV dispatch model to maximize the number of available EVs for discharging, thereby improving the MPDN recovery after a rare event. The proposed framework also includes an information system to further enhance MPDN resilience by effectively organizing data exchange among intelligent transportation system and smart charging system, and EV users. In addition, a novel bidirectional geographic graph is proposed to optimize travel plans, covering a large penetration of EVs and considering variations in traffic conditions. The effectiveness is assessed on a modified IEEE 123‐node test feeder with real‐world transportation and charging infrastructure. The results demonstrate a significant improvement in MPDN resilience with smart restoration strategies. The validation and sensitivity analyses evidence a significant superiority of the proposed framework.

EFFECTS OF WIND SPEED AND MOUNTING TYPE ON PV MODULE IN UNBALANCED DISTRIBUTION SYSTEMS

This paper assesses the effects of wind speed and mounting type on the performance of photovoltaic (PV) modules in the three phase unbalanced IEEE 34 node distribution system. The study was conducted in OpenDSS considering ZIP load model and residential load shape. The module temperature was calculated considering the wind speed and mounting type of the PV panel. The impact of wind speed on PV has been analyzed using three different wind data sets. Furthermore, free standing and flat roof mounting types were considered to evaluate the effect of mounting configuration. It was found that integrating PV into the distribution system reduced substation demand and energy losses. Results also show that the PV produced more power in high wind speed scenarios than in low wind speed scenarios. Regarding the mounting configuration, the PV incorporated with free standing configuration generated more power than the flat roof mounting type.

Online EV charging controlled by reinforcement learning with experience replay

The extensive penetration of distributed energy resources (DERs), particularly electric vehicles (EVs), creates a huge challenge for the distribution grids due to the limited capacity. An approach for smart charging might alleviate this issue, but most of the optimization algorithms has been developed so far under an assumption of knowing the future, or combining it with complicated forecasting models. In this paper we propose to use reinforcement learning (RL) with replaying past experience to optimally operate an EV charger. We also introduce explorative rewards for better adjusting to environment changes. The reinforcement learning agent controls the charger’s power of consumption to optimize expenses and prevent lines and transformers from being overloaded. The simulations were carried out in the IEEE 13 bus test feeder with the load profile data coming from the residential area. To simulate the real availability of data, an agent is trained with only the transformer current and the local charger’s state, like state of the charge (SOC) and timestamp. Several algorithms, namely Q-learning, SARSA, Dyna-Q and Dyna-Q+ are tested to select the best one to utilize in the stochastic environment and low frequency of data streaming.

An Efficient Modular Optimization Scheme for Unbalanced Active Distribution Networks With Uncertain EV and PV Penetrations

Integrated control strategies with Network Reconfiguration (NR), Demand Response (DR), and voltage control can reduce peak demand, energy loss, and system-wide unbalances in modern three-phase active Electric Distribution Networks (EDNs). However, simultaneous handling of these strategies is computationally complex and challenging. This paper presents a stochastic optimization formulation that slices the problem, solves the sub-problems separately, and splices them back to find optimal solutions efficiently. At the outset, all constraint-violating configurations are eliminated using a graph-theory-based edge traversal search developed from the classic Knuth’s Algorithm-S. Subsequently, deploying suitable indices, cyclic NR-DR assignments are performed to find near-optimal topologies and load schedules concerning the minimum loss, peak load, and unbalances. Further, Voltage Regulators (VRs) are set to achieve loss and unbalance reduction with minimal tap operations. The proposed scheme is tested on the modified IEEE 123-node test feeder with extensive EV and PV penetrations. The results show that this modular scheme provides superior solutions with an almost 75% reduction in time than the conventional co-optimization method. Various other case studies illustrate the effectiveness of the proposed scheme.

PQ-Based Local Voltage Regulation for Highly Resistive Radial Distribution Networks

Modern distributed energy resources (DER) consisting of energy storage coupled with a voltage source converter (VSC) provides flexible and dynamic operations in active and reactive power quadrants. These technologies present significant opportunities to provide distribution network support instead of traditional approaches if effective controls are in place. Although voltage regulation using reactive power is typical, active power is more effective at manipulating voltage in highly resistive distribution networks. This paper proposes a local controller that regulates VSC terminal voltage and allows fast switching of the VSC power factor output, with minimized disturbance on the terminal voltage. This improves the effectiveness of the VSC in managing voltage in highly resistive distribution networks using both active and reactive power and provides a strategy to manage upstream power flow. The approach uses gain equations derived from the voltage sensitivity to power injection and the point on wave switching in the stationary reference frame. The control strategy is verified through detailed simulation in MATLAB/Simulink using a modified IEEE 4-node test feeder, including performance during a switched load, DER disturbance and controller interaction via voltage droop. The small-signal model and stability analysis of the proposed approach is also presented.

Safe Deep Reinforcement Learning for Microgrid Energy Management in Distribution Networks With Leveraged Spatial–Temporal Perception

Microgrids (MG) have recently attracted great interest as an effective solution to the challenging problem of distributed energy resources’ management in distribution networks. In this context, despite deep reinforcement learning (DRL) constitutes a well-suited model-free and data-driven methodological framework, its application to MG energy management is still challenging, driven by their limitations on environment status perception and constraint satisfaction. In this paper, the MG energy management problem is formalized as a Constrained Markov Decision Process, and is solved with the state-of-the-art interior-point policy optimization (IPO) method. In contrast to conventional DRL approaches, IPO facilitates efficient learning in multi-dimensional, continuous state and action spaces, while promising satisfaction of complex network constraints of the distribution network. The generalization capability of IPO is further enhanced through the extraction of spatial-temporal correlation features from original MG operating status, combining the strength of edge conditioned convolutional network and long short-term memory network. Case studies based on an IEEE 15-bus and 123-bus test feeders with real-world data demonstrate the superior performance of the proposed method in improving MG cost effectiveness, safeguarding the secure operation of the network and uncertainty adaptability, through performance benchmarking against model-based and DRL-based baseline methods. Finally, case studies also analyze the computational and scalability performance of proposed and baseline methods.

Location of faults based on deep learning with feature selection for meter placement in distribution power grids

Abstract A problem of great interest for power distribution companies is ensuring uninterrupted service in extensive power distribution systems. Thus, the monitoring of networks and identification of system faults become essential. This work focuses on identifying a fault’s occurrence from a small number of low-cost measurements in a power distribution system. The determination of sensor locations is based on the recent feature selection approach LassoNet, where the measurement locations are ranked. It provides the most informative measures during a fault resulting in a shortening data set. It is used as input to a deep neural network without a significant loss in accuracy. We validate our method on the IEEE 13 and 34 node test feeders for distribution systems to conduct the suggested approach’s experimental studies.

A Distribution Static Synchronous Compensator Application to Mitigate Voltage Variation for Distribution Feeders

With the growing penetration of distributed energy resources (DER), the accompanying challenges have led utilities to limit the hosting capacities of DER installations on distribution feeders. A distribution static synchronous compensator (DSTATCOM) is a power electronic device to provide dynamic injections and absorption of reactive power into the distribution grid with more flexible and reliable voltage control and power quality improvement. A distributed energy resources management system (DERMS) is developed to provide more effective control of a DSTATCOM that can help substantially increase hosting capacity and mitigate overvoltage problems with the existing feeder. A Taiwan power company’s (Taipower) feeder is selected for computer simulation, and the DSTATCOM is employed in the test feeder to demonstrate the effectiveness of the DSTATCOM in improving the overvoltage problems. The voltage/reactive power (Volt/VAR) control of the DSTATCOM helps reduce overvoltage/voltage fluctuations as the DER output increases.

Solución práctica para el problema de reconfiguración en redes eléctricas de distribución: un enfoque heurístico constructivo

The problem regarding the reconfiguration of electrical distribution grids is addressed in this research through the implementation of a practical solution using a constructive heuristic algorithm. The most important characteristic of the proposed heuristic approach is its low-computation effort, given that few power flow solutions are required in order to solve the reconfiguration problem. The constructive algorithm starts its exploration of the solution space by closing all the tie lines form a fully meshed distribution network. The distribution line with the minimum current is permanently opened. A new power flow evaluation is made for the new distribution system, and the distribution line with the minimum current is opened if and only if this action does not generate isolated nodes. This procedure is repeated until the number of closed lines is equal to the number of nodes minus one, which is a condition required to maintain a radial configuration. Numerical validations in test feeders composed of 16, 33, 69, 84, 136, and 415 nodes demonstrate that the proposed constructive algorithm finds adequate solutions with minimum processing times. The proposed approach is practical for distribution companies since its implementation only requires a power flow tool for distribution networks that can deal with radial and meshed configurations.

A New Digital Twins-Based Overcurrent Protection Scheme for Distributed Energy Resources Integrated Distribution Networks

This paper presents a novel overcurrent protection scheme based on digital twins for a distribution network with distributed energy resources. A coordination protection standard is employed to perform settings and coordinate intelligent electronic devices, evaluating the effects of distributed energy resources. In addition, some integration criteria for distributed energy resources are proposed to identify the impact on overcurrent protections. The power hardware-in-the-loop (PHIL) scheme is designed to develop digital twins (DT) that connect the real relays to the simulated network. Moreover, a standard for substation automation is employed to define the communication protocol for reading Generic Object-Oriented Substation Events (GOOSE) messages. Furthermore, the IEEE 13-node test feeder is employed to validate the method and model in the real-time simulation software. The results show a miscoordination of the overcurrent protection scheme installed in the distribution network with the action of different distributed energy resources.

An Investigation on the Integration of a Hybrid Offshore Wind-Wave Energy Conversion System With the Distribution Network

This paper introduces the hybridization of multiple linear permanent magnet generator (LPMG)-based wave energy conversion systems with a doubly-fed induction generator (DFIG)-based wind energy conversion system. A detailed investigation of the impact of the integration of the wind-wave hybrid system with the distribution network is presented in this study. The control scheme for the multiple LPMGs ensures the optimum extraction of the wave power and the ability to maintain voltage balance at the outputs of the LPMGs. Similarly, the back-to-back converters of the DFIG are controlled for the maximum power point tracking of the wind turbine and the dc-link voltage regulation. The turbine rotor blade pitch angle is controlled to minimize the power fluctuations caused by the integration of LPMGs at the common DC bus. The time-domain simulation results confirm the effectiveness of the proposed system by showing a stable operation when integrated with the distribution test feeder. Finally, the steady-state analysis shows that the voltage profile of the distribution network is also improved, and the step voltage regulator of the distribution feeder can be removed in the presence of the proposed system without violating the voltage limits at any node of the selected distribution network. Further, the response of the distribution network is also evaluated under an electrical fault condition in the presence of the wind-wave hybrid system.

MANA Formulation Based Load Flow Solution for DC Distribution Networks

This paper presents an efficient load-flow (LF) solution method for direct current distribution networks (DC-DNs). The principles of this method are based on the modified-augmented-nodal-analysis (MANA) formulation and Newton-Raphson (NR) algorithm. The guaranteed convergence and quadratic convergence conditions are also formulated using Kantorovich’s theorem. The modified IEEE 33 bus test feeder is simulated to test the proposed method and compare its performance with Laplacian Matrix based LF methods, Holomorphic Embedding LF method (HE-LFM) and classical Gauss-Seidel (GS) solution. Apart from the comparisons with these numerical methods the accuracy of the proposed method is also validated with Electromagnetic Transients (EMT) results. The proposed method offers much better convergence properties with a faster simulation speed.

A method to control distributed energy resources in distribution networks using smart meter data

Voltage regulation has been one of the major challenges for distribution network operators (DNOs) due to the integration of distributed energy resources in the recent years. Control approaches mitigating over-voltage (OV) generally require full network model, which for low-voltage distribution networks (LVDNs), can be inaccessible to DNOs. This paper presents a novel data-driven model-free control framework for improving voltage regulation in 4-wire 3-phase LVDNs. In the proposed approach, first, using smart meter data, the aggregated resistance and reactance matrices of the network are estimated. Then, these matrices serve as an input for the proposed optimal power flow (OPF) method to control the DERs’ active and reactive power outputs. This method also lets the DNO adjust the fairness in prosumers’ generation curtailment. The performance of the proposed approach is evaluated in various case studies conducted on a real-world low-voltage test feeder. The simulation results exhibit significant effectiveness in solving the OV problem associated with DERs. For example, in the studied network, the proposed approach limits the maximum voltage of the network to 1.1 pu, while without having control over the DERs output power, it can even overpass 1.17 pu during the peak generation period. In addition, utilizing DERs’ reactive control capability reduces the amount of active power curtailment, and prosumers are able to collectively inject 5.66% more active power.

Evaluating the planning and operational resilience of electrical distribution systems with distributed energy resources using complex network theory

Electrical Distribution Systems (EDS) are extensively penetrated with Distributed Energy Resources (DERs) to cater the energy demands with the general perception that it enhances the system’s resilience. However, integration of DERs may adversely affect the grid operation and affect the system resilience due to various factors like their intermittent availability, dynamics of weather conditions, non-linearity, complexity, number of malicious threats, and improved reliability requirements of consumers. This paper proposes a methodology to evaluate the planning and operational resilience of power distribution systems under extreme events and determines the withstand capability of the electrical network. The proposed framework is developed by effectively employing the complex network theory. Correlated networks for undesirable configurations are developed from the time-series data of active power monitored at nodes of the electrical network. For these correlated networks, compute the network parameters such as clustering coefficient, assortative coefficient, average degree, and power law exponent for the anticipation; and percolation threshold for the determination of the network’s withstand capability under extreme conditions. The proposed methodology is also suitable for identifying the hosting capacity of solar panels in the system while maintaining resilience under different unfavorable conditions and identifying the most critical nodes of the system that could drive the system into non-resilience. This framework is demonstrated on IEEE 123 node test feeder by generating active power time-series data for a variety of electrical conditions using the simulation software, GridLAB-D. The percolation threshold resulted as an effective metric for the determination of the planning and operational resilience of the power distribution system.

Electric Water Heater Modeling for Large-Scale Distribution Power Systems Studies with Energy Storage CTA-2045 Based VPP and CVR

As the smart grid involves more new technologies such as electric vehicles (EVs) and distributed energy resources (DERs), more attention is needed in research to general energy storage (GES) based energy management systems (EMS) that account for all possible load shifting and control strategies, specifically with major appliances that are projected to continue electrification such as the electric water heater (EWH). In this work, a methodology for a modified single-node model of a resistive EWH is proposed with improved internal tank temperature for user comfort modeling and capabilities for conservation voltage reduction (CVR) simulations as well as Energy Star and Consumer Technology Association communications protocol (CTA-2045) compliant controls, including energy storage calculations for “energy take”. Daily and weekly simulations are performed on a representative IEEE test feeder distribution system with experimental load and hot water draw (HWD) profiles to consider user comfort. Sequential controls are developed to reduce power spikes from controls and lead to peak shavings. It is found that EWHs are suitable for virtual power plant (VPP) operation with sustainable tank temperatures, i.e., average water temperature is maintained at set-point or above at the end of the control period while shifting up to 78% of EWH energy out of shed windows per day and 75% over a week, which amounts to up to 23% of the total load shifted on the example power system. While CVR simulations reduced the peak power of individual EWHs, the aggregation effect at the distribution level negates this reduction in power for the community. The EWH is shown as an energy constant load without consistent benefit from CVR across the example community with low energy reductions of less than 0.1% and, in some cases, increased daily energy by 0.18%.

DC grid load flow solution incorporating generic DC/DC converter topologies

This research proposes a load flow (LF) solver with DC/DC converter models for DC distribution. The solver is based on modified-augmented-nodal-analysis (MANA) formulation and is implemented with the Newton–Raphson (NR) algorithm. The conditions to guarantee the convergence range of the MANA are demonstrated with Kantorovich’s theorem. The proposed formulation is generic and flexible, allowing it to accommodate any arbitrary DC grid configuration. In addition, the formulation is extendable due to MANA’s modular structure, which enables the incorporation of any device without changing the prior formulation. Instead of bus power injections, the summation of line flows and their associated derivatives are used when considering connections with various kinds of buses equipped with various converters. The proposed method is implemented in MATLAB and tested on the modified IEEE 33 bus distribution test feeder. Electromagnetic Transient (EMT) simulations confirmed the accuracy of the proposed method. Moreover, the results are also compared with the recently proposed efficient Laplacian Matrix (eLM) based method. Results suggest that the proposed method is robust and offers superior convergence characteristics with a faster simulation speed.