Flexible Distribution Network Research Articles

Dynamic Management of Flexibility in Distribution Networks through Sensitivity Coefficients

Due to a rising share of renewable energy sources on the production side and electrification of transport and heating on the consumption side, the efficient management of flexibility in distribution networks is crucial for ensuring optimal operation and utilization of resources. Nowadays, the sensitivity-based approach is mainly used in medium-voltage (MV) networks for regulating voltage profiles with reactive power of distributed energy resources (DER). The main disadvantage of the simplified sensitivity-based method is its inaccuracy in case of a high deviation of the network voltage from the nominal values. Furthermore, it was also noted that despite the fact that the method is well described in the literature, there is a lack of systematic approach to its implementation in real-life applications. Thus, the main objective of this paper is to address this disadvantage and to propose an algorithm designed to calculate required consumer flexibility in near real-time to ensure distribution grid operation within operational criteria. In the first part of the paper, network state, including line loading and node voltages, is assessed to determine distribution network node capacity. By analyzing the sensitivity of network busbars to changes in consumption and production, our algorithm effectively identifies the most efficient nodes and facilitates strategic decision-making for resource allocation. We demonstrate the effectiveness of our approach through simulations of real-world distribution network data, highlighting its ability to enhance network flexibility and improve resource utilization. Leveraging sensitivity coefficients, the algorithm enables flexible consumption and production management across various scenarios, supporting the transition to a more dynamic and efficient power system.

Robust Operation of Flexible Distribution Network With Large-Scale EV Charging Loads

The increasing integration of electric vehicles (EVs) significantly challenges the efficient operation of distribution networks. With the rapid development of power electronic devices represented by soft open point (SOP), flexible distribution networks (FDNs) provide a promising opportunity for integrating large-scale EVs. In this paper, a robust operation method is proposed to improve the economy of EVs and ensure the flexible operation of FDN. First, an EVs-aggregator (EVs-AGG) model is formulated to incorporate diverse charging behaviors. The EVs-AGG is efficiently integrated into FDN through the DC link of multi-terminal SOP. Then, optimal operation strategies are obtained by coordinating EVs charging/discharging power with the regulation of multi-terminal SOP. Furthermore, considering the uncertainties of EVs and photovoltaics, a robust operation framework is established to deal with uncertainties. Finally, case studies based on a practical distribution network indicate that through the interactive power regulation between multi-terminal SOP and EVs-AGG, the proposed method alleviates the negative impacts caused by large-scale EVs integration and improves the operational flexibility of FDN.

Coordinated control strategy of reactive power compensation based on a flexible distribution network transformer

In order to solve the problem of the power quality caused by distributed power access to the distribution network, this paper proposes a coordinated control strategy of reactive power compensation based on a flexible distribution transformer. First, the working principle of the flexible distribution transformer is analyzed, and the mathematical model of energy acquisition and the regulation converter of the flexible distribution transformer are studied as well. The device-level control strategies of energy acquisition and regulation converters are proposed, respectively. Then, in order to maintain the stability of the bus voltage and quickly respond to the reactive power changes of the system, a coordinated control strategy for the reactive power compensation of flexible distribution transformers is proposed. The priority herein is to maximize the reactive power compensation capacity of the energy harvesting converter. When the energy harvesting converter reaches the compensation upper limit, the control converter is used for reactive power compensation to further suppress the grid voltage fluctuation. Finally, it is verified through simulations that the flexible distribution transformer can realize the reactive power compensation of the distribution network and effectively improve the power quality of the distribution network.

Two-stage distributionally robust assessment method for distributed PV hosting capacity of flexible distribution networks

The large-scale grid-connection of distributed PV has brought great challenges to distribution network. The traditional stochastic optimization and robust optimization have conservative problems in dealing with the uncertainty of distributed PV. Therefore, based on the historical scenario data of PV output, this paper proposes a two-stage distributionally robust assessment method for distributed PV hosting capacity of distribution networks considering demand response. Firstly, a weighted K-means algorithm is used to cluster the historical data of distributed PV output to obtain typical scenario data and establish the demand response model of electricity price incentive. In order to maximize the grid-connected capacity of distributed PV, multiple operational constraints and adjustment strategies in flexible distribution network were considered. Then, a two-stage distributionally robust assessment model for distributed PV hosting capacity of flexible distribution network is established, and the column-and-constraint generation algorithm is used for iterative solution. The effectiveness of the algorithm is verified on a 70-bus system, and the influence of distributed PV output and different regulation strategies on the hosting capacity of flexible distribution network is analyzed.

Total supply capability of electricity distribution networks considering flexible interconnection of low‐voltage service transformers

AbstractUnder the target of ‘emission peak and carbon neutrality’, electricity distribution networks will massively access low‐carbon technologies, which will result in problems such as insufficient hosting capacity, unbalanced electricity loads, degraded power quality etc. The low‐voltage flexible distribution network (LVFDN), which interconnects its low‐voltage service transformers using flexible power electronic devices (flexible interconnected devices [FIDs]) is considered an effective means to deal with the challenges above. The total supply capability (TSC) of LVFDN is proposed. Firstly, the typical structures of LVFDN and their operation modes are proposed. Then, the TSC model of LVFDN, which formulates flexible power flow control and multi‐level (medium‐voltage feeder and low‐voltage flexible interconnection) load transfer is proposed. Due to the non‐linear non‐convex characteristics of the proposed TSC model, a new algorithm based on the ‘branch and bound algorithm’ is also provided. In the case study, the TSC of an actual electricity distribution network is calculated and tested by the N‐1 verification method. Finally, the variations of TSC with different capacities of the low‐voltage FID are analysed. Suggestions for the planning and operation of LVFDN are also given. A theoretical basis for the application of flexible interconnection technology in low‐voltage electricity distribution networks is provided.

SparGD: A Sparse GEMM Accelerator with Dynamic Dataflow

Deep learning has become a highly popular research field, and previously deep learning algorithms ran primarily on CPUs and GPUs. However, with the rapid development of deep learning, it was discovered that existing processors could not meet the specific large-scale computing requirements of deep learning, and custom deep learning accelerators have become popular. The majority of the primary workloads in deep learning are general matrix-matrix multiplications (GEMMs), and emerging GEMMs are highly sparse and irregular. The TPU and SIGMA are typical GEMM accelerators in recent years, but the TPU does not support sparsity, and both the TPU and SIGMA have insufficient utilization rates of the Processing Element (PE). We design and implement SparGD, a sparse GEMM accelerator with dynamic dataflow. SparGD has specific PE structures, flexible distribution networks and reduction networks, and a simple dataflow switching module. When running sparse and irregular GEMMs, SparGD can maintain high PE utilization while utilizing sparsity, and can switch to the optimal dataflow according to the computing environment. For sparse, irregular GEMMs, our experimental results show that SparGD outperforms systolic arrays by 30 times and SIGMA by 3.6 times.

Supply Restoration of Data Centers in Flexible Distribution Networks With Spatial-Temporal Regulation

The reliable operation of Internet data centers (IDCs) is crucial to digitization transformation of society. However, the extreme fault on the IDC-located distribution network is fatal to IDC operation. The evolution of flexible distribution networks (FDNs) can satisfy the deep de-pendence of IDC on high-quality and reliable power supply. In FDNs, AC/DC feeders with multiple voltage levels can be flexibly interconnected and adjusted by flexible stations (FSs). It is promising to flexibly enhance operation per-formance of crucial IDCs after fault isolation in FDNs. In this paper, a coordinated supply restoration method of IDCs in FDNs with spatial-temporal regulation is proposed to minimize IDC data-dropping loss under complex faults. First, a spatial-temporal transfer strategy of IDC work-loads is precisely formulated and an FS-based network reconfiguration strategy is proposed. Then, a mixed-integer second-order conic programming model is established for efficient solving. Spatial-temporal coordination among the source, network, storage, and demand sides can be realized without violating physical feasibility. Finally, the proposed method is validated on the modified IEEE 33-node and modified IEEE 123-node test cases. Case studies show that total outage loss is effectively reduced with sufficient utili-zation of available power. Operational performance of FDN is also enhanced under extreme faults.

Dynamic directed graph convolution network based ultra‐short‐term forecasting method of distributed photovoltaic power to enhance the resilience and flexibility of distribution network

AbstractAccurately forecasting regional distributed photovoltaic (DPV) power is crucial in mitigating the negative impact of high DPV penetration on the reliability and resilience of the distribution network. However, most of the current photovoltaic power forecasting methods suffer from two key problems: (1) ignoring the asymmetric influence relationship among DPV sites; (2) lack of consideration of dynamic spatiotemporal correlation among DPV sites. As a result, these methods are unable to fully adapt to the characteristics of DPV, making it challenging to directly apply the existing forecasting methods to improve the accuracy of DPV power forecasting. To conquer this limitation, a dynamic directed Graph Convolution Neural Network (DDGCN) is applied to regional DPV ultra‐short‐term power forecasting. Unlike the conventional Graph Convolution Neural Network (GCN) based forecasting methods, the proposed method improves GCN to process the directed graph. On this basis, to capture the dynamic and directed adjacency relationship among graph nodes, a temporal attention mechanism is proposed and combined with the directed GCN model. In this way, the dynamic and asymmetric/directed relationships among DPV sites can be taken into account. It is worth noting that the DPVs’ adjacency relationship can be constructed without any prior knowledge by end‐to‐end model training. The simulation experiment proves that the prediction accuracy can be further improved by taking into account the dynamic directed relationship among the sites via a real DPV power dataset.

Analyzing the blockades to electric vehicle mobility in an emerging economy: Toward a triple bottom line sustainable development

AbstractThe emerging economies are moving toward electric vehicles (EVs), which are much sustainable means of transportation. However, many social, economic, and technological blockades of EV mobility remain to be addressed. The study investigates social, economic, and technological blockades for e‐mobility in an emerging economy and simultaneously concentrating on their existing and future business implications. Identification of e‐mobility blockades is obtained through literature review and consensus of the experts. Kappa analysis is used to strain the priority map of the mentioned blockades on the basis of developing consensus among experts. Thereafter, a multi criteria decision making approach that is, best‐worst method is adopted to rank the identified potential blockades. The study models the social, economic, and technological blockades of EV mobility in context of emerging economy. To cater blockades in a desired sequence, the study further suggests the ranking sequence of blockades to enhance e‐mobility toward sustainable development. The findings from the study reveals that “costs associated with EVs and “the lack of government policies, standards, and regulations” are the most important blockades to sustainable EV adoption. Thus, the organizations and the Government need to find ways to reduce the cost of EVs and simultaneously should plan and develop policies to enhance EV adoption and to reduce the usage of gasoline vehicles. The third most important blockade that is, “low demand due to undetermined sustainable benefits” indicates that there is an urgent need to spread awareness among the consumers about the sustainable benefits for the adoption of EVs. Further, the sensitivity analysis reveals that sensitivity range for weightage of the group of academicians lies between 0.3 and 0.7 on a scale of 1. If more weightage is provided to the practical scenario, it is discovered that “location of charging station (BEV4)” is more important than “lack of flexible distribution network (BEV2)” to enhance the amount of EV adoption in India. The study provides insights to practitioners dealing with e‐mobility in emerging economies, showing the appropriate sequence of the blockades to help them build up strategies to mitigate these blockades.

Robust optimization model of flexible distribution network considering source-load uncertainty

The increase in renewable energy permeability and the electrical load uncertainty aggravates the fluctuation of power flow and bus voltage magnitudes of the distribution network (DN). Then, the fluctuation threatens system security and makes increasing difficulties in DN regulation. However, the widespread utilities of power electronic technology promote rapid developments in flexible distribution networks, thereby providing certain opportunities to treat the open question. Therefore, this study proposes a robust optimization model of flexible distribution network allowing for system security and economy. Firstly, a optimal power flow (OPF) model of flexible distribution network with various active management strategies is derived and constructed by taking the minimum loss and voltage offset as the objective function. The model is linearized and relaxed to convex problem by a second-order cone. Then, the robust OPF model of flexible distribution network is established considering the uncertainty of load and renewable energy. Finally, a relaxation-based co-evolution algorithm is proposed to solve the model. The modified IEEE 33 bus system and PG&E 69 bus system are involved for case studies, in which the calculation results under various scenarios are compared. Consequently, the effectiveness and practicability of proposed model to locate and cope with the worst situation is demonstrated.

Dynamic Optimal Power Dispatch in Unbalanced Distribution Networks with Single-Phase Solar PV Units and BESS

Battery energy storage systems (BESSs) are a promising solution for increasing efficiency and flexibility of distribution networks (DNs) with a significant penetration level of photovoltaic (PV) systems. There are various issues related to the optimal operation of DNs with integrated PV systems and BESS that need to be addressed to maximize DN performance. This paper deals with day-ahead optimal active–reactive power dispatching in unbalanced DNs with integrated single-phase PV generation and BESS. The objectives are the minimization of cost for electricity, energy losses in the DN, and voltage unbalance at three-phase load buses by optimal management of active and reactive power flows. To solve this highly constrained non-linear optimization problem, a hybrid particle swarm optimization with sigmoid-based acceleration coefficients (PSOS) and a chaotic gravitational search algorithm (CGSA)called the PSOS-CGSA algorithm is proposed. A scenario-based approach encompassing the Monte Carlo simulation (MCS) method with a simultaneous backward reduction algorithm is used for the probabilistic assessment of the uncertainty of PV generation and power of loads. The effectiveness of the proposed procedure is evaluated through aseries test cases in a modified IEEE 13-bus feeder. The simulation results show that the proposed approach enables a large reduction in daily costs for electricity, as well as a reduction in expected daily energy losses in the DN by 22% compared to the base case without BESS while ensuring that the phase voltage unbalance rate (PVUR) is below the maximum limit of 2% for all three-phase buses in the DN.

Data-Driven Operation of Flexible Distribution Networks with Charging Loads

The high penetration of distributed generators (DGs) and the large-scale charging loads deteriorate the operational status of flexible distribution networks (FDNs). A soft open point (SOP) can deal with operational issues, such as voltage violations and the high electricity purchasing cost of charging stations. However, the absence of accurate parameters poses challenges to model-based methods. This paper proposes a data-driven operation method of FDNs with charging loads. First, a data-driven model-free adaptive predictive control (MFAPC) approach is proposed to fully involve charging loads in the control of FDN without accurate network parameters. Then, a multi-timescale coordination control model of an SOP with charging loads is established to satisfy the demand of charging loads and improve the control performance. The effectiveness of the proposed method is numerically demonstrated on the modified IEEE 33-node distribution network. The results indicate that the proposed method can effectively reduce the electricity purchasing cost of charging stations and improve the operational performance of FDNs.

Two-Stage Robust Optimal Scheduling of Flexible Distribution Networks Based on Pairwise Convex Hull

With distributed generation (DG) being continuously connected into distribution networks, the stochastic and fluctuating nature of its power generation brings ever more problems than before, such as increasing operating costs and frequent voltage violations. However, existing robust scheduling methods of flexible resources tend to make rather conservative decisions, resulting in high operation costs. In view of this, a two-stage robust optimal scheduling method for flexible distribution networks is proposed in this paper, based on the pairwise convex hull (PWCH) uncertainty set. A two-stage robust scheduling model is first formulated considering coordination among on-load tap changers, energy storage systems and flexible distribution switches. In the first stage, the temporal correlated OLTCs and energy storage systems are globally scheduled using day-ahead forecasted DG outputs. In the second stage, FDSs are scheduled in real time in each time period based on the first-stage decisions and accurate short-term forecasted DG outputs. The spatial correlation and uncertainties of the outputs of multiple DGs are modeled based on the PWCH, such that the decision conservativeness can be reduced by cutting regions in the box with low probability of occurrence. The improved column-and-constraint generation algorithm is then used to solve the robust optimization model. Through alternating iterations of auxiliary variables and dual variables, the nonconvex bilinear terms induced by the PWCH are eliminated, and the subproblem is significantly accelerated. Test results on the 33-bus distribution system and a realistic 104-bus distribution system validate that the proposed PWCH-based method can obtain much less conservative scheduling schemes than using the box uncertainty set.

Analysis of Pre- and during COVID-19 Mixed Load Models on Unbalanced Radial Distribution System Using a New Metaphor-Less Rao Optimization

An unbalanced electrical distribution system (DS) with radial construction and passive nature suffers from significant power loss. The unstable load demand and poor voltage profile resulted from insufficient reactive power in the DS. This research implements a unique Rao algorithm without metaphors for the optimal allocation of multiple distributed generation (DG) and distribution static compensators (DSTATCOM). For the appropriate sizing and placement of the device, the active power loss, reactive power loss, minimum value of voltage, and voltage stability index are evaluated as a multiobjective optimization to assess the device’s impact on the 25-bus unbalanced radial distribution system. Various load models, including residential, commercial, industrial, battery charging, and other dispersed loads, were integrated to develop a mixed load model for examining electrical distribution systems. The impact of unpredictable loading conditions resulting from the COVID-19 pandemic lockdown on DS is examined. The investigation studied the role of DG and DSTATCOM (DGDST) penetration in the electrical distribution system for variations in different load types and demand oscillations under the critical emergency conditions of COVID-19. The simulation results produced for the mixed load model during the COVID-19 scenario demonstrate the proposed method’s efficacy with distinct cases of DG and DSTATCOM allocation by lowering power loss with an enhanced voltage profile to create a robust and flexible distribution network.

Feasibility analysis of PV and energy storage system integration for flexible distribution networks: A moment-based distributionally robust approach

With the requirements of carbon neutrality and scientific and technological progress, massive distributed renewable energy resources have been integrated in distribution networks. The flexible interconnected distribution networks (FDNs) provide an effective way to avoid the negative effects of power quality, network loss and relay protection. This paper proposes a feasibility analysis model of distributed photovoltaic power generations (PVs) integration for FDNs based on distributionally robust. It uses moment-based ambiguity sets to model the uncertainty of PV outputs. Based on the circular constraint linearization method and duality theory, the solution method of the above model is designed. In this paper, two typical FDN structures are considered, namely, the closed-loop flexible interconnected structure between feeders and the flexible interconnected structure between feeder loops. The hosting capacity of the PV and energy storage system of the two structures is analyzed in case study, and the effectiveness of the proposed model and method is verified.

Risk-based joint flexible distribution network expansion planning and allocation of EV parking lots

Risk-based joint flexible distribution network expansion planning and allocation of EV parking lots

Optimizing DSO Requests Management Flexibility for Home Appliances Using CBCC-RDG3

This article covers a case study with homes equipped with multiple appliances for energy consumption. The central goal is to provide for aggregators’ flexibility in distribution networks by building an optimal schedule that takes advantage of load flexibility resources. This, in turn, allows for the re-scheduling of shifting/real-time home appliances to provision a request from a distribution system operator (DSO). The paper concludes with the consideration of the CBCC-RDG3, HyDE-DF, and genetic algorithms, which were used to find the best schedule that would be highly efficient and meet all the constraints associated with the problem that successfully demonstrate the effectiveness of this particular approach.

DLMP-Based Quantification and Analysis Method of Operational Flexibility in Flexible Distribution Networks

With high penetration of flexible resources, operational flexibility of flexible distribution networks (FDNs) is important to ensure high-quality electricity services. This paper proposes a value quantification and analysis method for operational flexibility in FDNs based on distribution locational marginal pricing (DLMP). First, flexibility constraints are formulated from the perspective of node integration, branch transfer, and network aggregation of flexibility. Then, a unified analytical framework for quantifying the operational flexibility is established, in which spatial-temporal transfer of flexibility is modeled with the derivation of flexibility sensitivity factors. Further, a DLMP-based flexibility value quantification model is proposed with nodal net power as the unit for flexibility pricing. The obtained flexibility price can not only quantify flexibility value but also be used to guide the flexible resources. Finally, the effectiveness of the proposed method is validated on a modified IEEE 33-node distribution system and a modified IEEE 123-node distribution system. Results verify that the proposed method can quantify the impact of nodal net power on operational flexibility and effectively improve the overall flexibility performance through the guide of flexibility price.

Peer-to-Peer electricity trading of interconnected flexible distribution networks based on Non-Cooperative games

With the integration of power electronic devices represented by soft open points (SOPs), distribution networks have gradually evolved into interconnected flexible distribution networks (FDNs). Considering the deregulation of electricity market and user privacy, multiple stakeholders have participated in the operation of FDNs. Peer-to-peer (P2P) electricity trading is promising to alleviate operational problems of interconnected FDNs. As multiple regions pursue the maximum profits individually, non-cooperative game methods can be utilized to realize fair profit allocation in P2P trading. In this paper, a non-cooperative game-based P2P trading method is proposed to meet the electricity trading needs of multi-region interconnected FDNs. First, based on non-cooperative games, a two-layer P2P electricity trading framework is established to realize cost reduction and voltage profile improvement of multi-region interconnected FDNs. Then, a P2P trading adjustment mechanism is designed to improve the operational profits of SOP, in which spatial active power trading adjustment, temporal dispatching of energy storage (ES) link and reactive power support are incorporated. Finally, the effectiveness of the proposed method is verified based on a practical distribution network with four-terminal SOP in Tianjin. The results show that the proposed P2P electricity trading method can promote the economic operation performance of interconnected FDNs and improve the operational profit of SOP.

Peer-to-Peer Electricity Trading of Interconnected Flexible Distribution Networks Based on Distributed Ledger

The condition of power imbalance in flexible distribution networks (FDNs) is deteriorated due to the high penetration of distributed generators. Through peer-to-peer (P2P) electricity trading, multiple regions interconnected by soft open points (SOPs) can flexibly exchange power to alleviate power imbalance. As the physical foundation of P2P transactions, SOP regulation guarantees accurate instruction execution. Besides, smart contracts based on distributed ledger technology (DLT) facilitate highly credible P2P transactions. Oriented for the economic operation of interconnected FDNs, this article proposes a DLT-based P2P electricity trading method based on intelligent SOP regulation. First, a smart contract is designed for P2P transactions of FDNs, in which trading solutions are automatically settled with a modified highest combined offer principle. Then, a trading platform is built for the P2P electricity trading of interconnected FDNs. Finally, the effectiveness of the proposed method is verified on a practical FDN with four-terminal SOPs in Tianjin.