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Voltage rise caused by high levels of distributed generation is manifesting as voltage regulation challenges for many electricity network service providers. In this environment it would be ideal to reduce supply voltage magnitudes, however, many network operators are hesitant to do so due to concerns related to consumer appliance performance at reduced supply voltage magnitudes. Voltage regulation requirements are defined by network standards and network service providers must ensure voltages remain within specified limits. Through an evaluation of domestic appliance performance when supplied at various voltage magnitudes, this paper examines the impact of varying voltage levels on residential appliances. Equipment energy demand, operation and actuation were monitored for each applied voltage magnitude. While no equipment failures were recorded, appliance behaviour varied significantly with applied voltage magnitude. Individual appliance conservation voltage reduction (CVR) factors have also been established. The results highlight the importance of good voltage regulation and provide substantiated appliance performance figures for future studies. The outcomes of this paper allow electricity network service providers to understand the implications of supply voltage magnitude on domestic appliance performance, whether it be understating of the impact of higher voltage magnitudes caused by distributed generation or implications of reducing voltage magnitudes to provide headroom for distributed generation integration. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty of single-phase grounding fault line selection in a small current grounding system, a distribution network fault line selection method based on modified CEEMDAN and convolutional neural network is proposed. Firstly, the random forest and multiscale permutation entropy are used to modify the Complete Ensemble Empirical Mode Decomposition Adaptive Noise algorithm (CEEMDAN), and the zero-sequence current of each line is decomposed into a series of intrinsic mode functions through the modified CEEMDAN (MCEEMDAN) algorithm. Secondly, the intrinsic mode function is transformed into the image formation by using the signal-image conversion method, and the generated image is upgraded to a three-dimensional color image by combining pseudo-color coding technology. Finally, the color images converted from the signals of each line are fused as the input of the GoogLeNet network, and the fault line selection of the distribution network is realized in the form of probability output by the Softmax function. The experimental results show that the proposed method has not only strong feature extraction ability and high recognition accuracy but also has good anti-noise and robustness. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems have been gaining popularity among distribution network engineers. The integration of these small-scale sources has a variety of benefits for the distribution network. One of the most important benefits is the power loss reduction. However, a high penetration level of PV systems may disrupt the protection coordination of conventional overcurrent relays of the distribution network, reducing the profit of the electricity distribution company (EDC) due to healthy feeder de-energization. This paper determines the optimal penetration level of PV systems in the distribution network considering both power loss reduction profit and protection miscoordination cost to maximize the profit of the EDC. The merits of the proposed methodology are verified through several case studies on a simulation model of the IEEE 33-bus test system in the Electrical Transient Analyzer Program (ETAP) environment considering the reliability data of the IEEE Roy Billinton Test System. [ABSTRACT FROM AUTHOR]

The future grid is evolving into a smart distribution network that integrates multiple distributed energy resources ensuring at the same time reliable operation and increased power quality. In recent years, many research papers have addressed the voltage violation problems that arise from the high penetration of distributed generation. In view of the transition to active network management and the increase in the quantity of collected data, distributed control schemes have been proposed that use pervasive communications to deal with the complexity of smart grid. This paper reviews the recent publications on distributed and decentralized voltage control of smart distribution networks, summarizes their control models, and classifies the solution methodologies. Moreover, it comments on issues that should be addressed in the future and the perspectives of industry applications. [ABSTRACT FROM PUBLISHER]

This paper presents a peer-to-peer (P2P) local energy trading strategy in distribution networks with a novel decentralized voltage management scheme based on voltage sensitivity factors. A social welfare maximizing optimization problem is formulated for P2P energy trading, which is solved by an alternating direction method of multipliers (ADMM) algorithm. The proposed approach is especially designed for asynchronous communication between energy producers and consumers, which may be more suitable for short-term market clearings. [ABSTRACT FROM AUTHOR]

An uninterruptible power supply (UPS) can reduce power failure loss by responding to power demand during distribution network failures. For reliability-sensitive power consumers, deploying UPSs is a solution for improving power supply reliability. Nevertheless, because of UPS devices’ energy limits and potential failure probability, power consumers with UPSs cannot guarantee 100% adequacy. In future power systems with frequent reconfiguration and massive storage facilities, the power supply adequacy of a consumer with respect to the network configuration and the storage capacity will need to be frequently evaluated. Furthermore, the analytical formulation of a consumer with a UPS should be investigated since the simulation-based method might witness low efficiency and poor convergency. Therefore, this paper proposes a novel analytical availability index calculation framework for adequacy evaluation. The framework models the uncertainties of distribution networks and UPS devices as stochastic processes. Then, the availability index calculation formula is acquired based on the stochastic modeling. Compared with existing methods, this paper incorporates UPS devices’ capacity and characteristics. Moreover, the distribution network configuration and protection device placement are considered. Case studies on an illustrative distribution network, the modified RBTS system, and a real distribution system in China validate the proposed method. [ABSTRACT FROM AUTHOR]

Flexible DC distribution networks have been widely developed because of their advantages of high efficiency, low cost, and easy access to renewable energy. However, as distribution networks have more ends than the two-terminal transmission line, it is more challenging to protect them and locate their faults. Therefore, this paper proposes a modified T-source circuit breaker to protect against DC faults. Based on the proposed circuit breaker, an integral fault location method using double-terminal asynchronous data is proposed. The accuracy of the algorithm is not affected by the fault resistance. There are no differential terms in the algorithm, which greatly reduces the sensitivity to noise. The integral algorithm can remain steady when the voltage and current are zero in oscillation and attenuation periods. This constitutes the main contribution of this paper. Finally, it is verified by simulation and experiment that the proposed circuit breaker can isolate the power source within a short time in the typical DC distribution network. Under a noise of 30 dB for simulation, the location error of the proposed algorithm is <0.4% when the fault resistance is 0.5 Ω–500 Ω. [ABSTRACT FROM AUTHOR]

State estimation (SE) of distribution networks heavily relies on pseudo measurements that introduce significant errors, since real-time measurements are insufficient. Interval SE models are regularly used, where true values of system states are supposed to be within the estimated intervals. However, conventional interval SE algorithms cannot consider the correlations of same interval variables in different terms of constraints, which results in overly conservative estimation results. In this paper, we propose a new interval SE model that is based on the relative distance measure (RDM) arithmetic. In the proposed model, measurement errors are assumed to be bounded in given sets and the state variables are described as RDM variables. Since the SE model is a non-convex, the solution's credibility cannot be guaranteed. Therefore, each nonlinear measurement equation in the model is transformed into dual inequality linear equations using the mean value theorem. The SE model is finally reformulated as a linear programming contractor that iteratively narrows the upper and lower bounds of the estimated state variables. Numerical tests on IEEE three-phase distribution networks show that the proposed method outperforms the conventional interval-constrained propagation, modified Krawczyk-operator and optimization based interval SE methods. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed renewable energy sources drastically alters the dynamic characteristics of distribution networks (DNs). Therefore, several equivalent models have been recently proposed, to analyze more accurately the complex behavior of modern DNs. However, relatively simple models are still commonly used in practice for dynamic power system studies. In addition, dynamic equivalent models for DNs are sensitive to different operating conditions and there is lack of systematic understanding of their performance. Scope of this paper is to propose a methodology for identifying the applicability range in terms of accuracy and generalization capability of several conventional and newly developed equivalent models for the dynamic analysis of modern DNs. A set of metrics is used for the modelling accuracy assessment and a sensitivity analysis framework is introduced to fully quantify the generalization capabilities of DN equivalent models. Based on the above, guidelines and recommendations for the development of robust equivalent models for DN analysis are proposed. [ABSTRACT FROM AUTHOR]

Negative-sequence current is required to be intentionally injected to support the power grid or compensate the imbalanced load in asymmetrical faults utilizing STATCOM. However, the extreme compensation range of negative-sequence current injection becomes complicated when grid voltage becomes asymmetrical. This article presents a novel off-line strategy for negative-sequence current compensation under two typical kinds of asymmetrical grid faults — single-phase voltage drop and double-phase voltage drop, with the following two benefits simultaneously: maximizing negative current compensation capacity; avoiding iterations and reducing computation burdens. In this paper, the mechanism of DC voltage balancing among three phases in STATCOM is analyzed and a new identification method of negative-sequence current compensation range is explored out. To simplify the calculation, this paper takes the ratio of negative-sequence reactive (Iq-) and active (Id-) current as a variable (s). In this way, the relationship between the extreme compensation range, asymmetrical voltage sag depth of power grid h is drawn with MATLAB. Different from the previous iterative method, the off-line identification method is applied to avoid huge iterations. By fitting the curve, the precise ranges of negative-sequence current compensation under different asymmetrical grid faults are approximated as simple polynomials, thus significantly reducing the computation burdens. Simulation and experimental results verify the practicability of the proposed compensation method. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed photovoltaics (PVs) has brought great challenges to the security and stability of distribution networks (DNs), especially voltage violation. Current voltage optimization methods rarely consider the mutual voltage support between high voltage (HV) and medium voltage (MV) DNs, which may cause PV generation losses in extreme scenarios. Limited by the convergence conditions of common distributed optimization algorithms, mixed integer optimization problem can hardly be calculated in a distributed manner. This paper tries to exert the mutual voltage support capability of HV and MV DNs and realize optimal operation of continuous and discrete voltage regulation devices. Thus, a hierarchical distributed voltage optimization method for HV and MV DNs is proposed in this paper. It is assumed that HV and MV DNs are operated by different control systems and real-time communication between them is available. First, the voltage optimization models of HV and MV DNs are developed to minimize the total cost of voltage adjustment and network losses, which optimize the dispatch of on-load tap changers, reactive power compensators, feeder switches, and distributed PVs. Then the generalized Benders decomposition is applied to solve the mixed integer optimization model in a master-slave distributed manner and the accuracy of LinDistFlow equation is improved through the compensation of peak voltage and boundary power flow in MV DNs. Finally, the effectiveness of proposed method is demonstrated via simulation tests in Jinzhai DNs of China. The results of proposed optimization method are very close to those of global centralized optimization method. [ABSTRACT FROM AUTHOR]

The ever-growing higher penetration of distributed energy resources (DERs) in low-voltage (LV) distribution systems brings both opportunities and challenges to voltage support and regulation. This paper proposes a deep reinforcement learning (DRL)-based scheduling scheme of energy storage systems (ESSs) to mitigate system voltage deviations in unbalanced LV distribution networks. The ESS-based voltage regulation problem is formulated as a multi-stage quadratic stochastic program, with the objective of minimizing the expected total daily voltage regulation cost while satisfying operational constraints. While existing voltage regulation methods are mostly focused on one-time-step control, this paper explores a day-horizon system-wide voltage regulation problem. In other words, the size of action and state spaces are extremely high-dimensional and need to be delicately handled. Furthermore, in order to overcome the difficulty of modeling uncertainties and develop a real-time solution, a learn-to-schedule feedback control framework is proposed by adapting the problem to a model-free DRL setting. The proposed algorithm is tested on a customized 6-bus system and a modified IEEE 34-bus system. Simulation results validate the effectiveness and near-optimality of voltage regulation by ESS in comparison with a deterministic quadratic program solution. [ABSTRACT FROM AUTHOR]

With the rapid growth of electric vehicles (EVs) in distribution systems, a new player, called EV parking lot operator (EV PLO), is emerging around the world. Furthermore, the integration of distributed generation in the distribution level, in particular, renewable energy sources, is leading to the establishment of various markets in distribution systems. On one hand, such PLOs aim at managing their EVs within their parking lots to participate in the distribution markets and to maximize their profits. On the other hand, a distribution system operator seeks to minimize the system-wide cost while minimizing renewable power spillage and the side-effects of its intermittency. This interaction inspires the innovative two-level model proposed in this paper. In the first level, a new model is proposed for EV PLOs which models the EVs’ characteristics, including EV owners’ uncertainties, in a reasonably accurate manner. These PLOs are allowed to participate in energy, reserve and regulation distribution markets by optimally managing their EVs. In the second level, a new model is developed to ensure that the technical constraints in the distribution networks are met while minimizing the overall system cost. In addition, this paper evaluates the effects of the penetration level and the placement of wind and solar PV on the offering strategies of EV parking lots, as well as on the overall performance of the distribution systems. [ABSTRACT FROM PUBLISHER]

The increasing penetration of photovoltaic (PV) systems promotes utilization of PV inverters for volt/var control (VVC) in distribution networks. However, PV inverters are vulnerable and their reliability is one of the most critical concerns for sustainable PV energy utilization. To enhance PV inverter reliability, this paper proposes a PV inverter reliability-constrained VVC method of distribution networks under uncertainties. Firstly, a full reliability analysis procedure for PV inverters is developed to estimate the lifetimes of core devices inside inverters. Secondly, considering the impacts of VVC on PV inverter reliability, reliability constraints are proposed based on regulation of inverter apparent power and application of a PV curtailment scheme. Thirdly, a multi-objective PV inverter reliability-constrained VVC optimization model is proposed, which aims to minimize network power loss and PV curtailed power, simultaneously. Accordingly, the nonconvex model is relaxed by a second-order cone programming method, and the uncertainties such as PV power generation and loads are addressed by a scenario-based stochastic optimization method. The proposed VVC method is tested on the 33-bus and 69-bus distribution networks, and the simulation results verify high efficiency of the proposed method in energy loss reduction and inverter reliability enhancement. [ABSTRACT FROM AUTHOR]

In recent years, the rapid growth of active consumers in the distribution networks transforms the modern power markets’ structure more independent, flexible, and distributed. Specifically, in the recent trend of peer-to-peer (P2P) transactive energy systems, the traditional consumers became prosumers (producer+consumer) who can maximize their energy utilization by sharing it with neighbors without any conventional arbitrator in the transactions. Although a distributed energy pricing scheme is inevitable in such systems to make optimal decisions, it is challenging to establish under the influence of non-linear physical network constraints with limited information. Therefore, this paper presents a distributed pricing strategy for P2P transactive energy systems considering voltage and line congestion management, which can be utilized in various power network topologies. This paper also introduces a new mutual reputation index as a product differentiation between the prosumers to consider their bilateral trading willingness. In this paper, a Fast Alternating Direction Method of Multipliers (F-ADMM) algorithm is realized instead of the standard ADMM algorithm to improve the convergence rate. The effectiveness of the proposed approach is validated through software simulations. The result shows that the algorithm is scalable, converges faster, facilitates easy implementation, and ensures maximum social welfare/profit. [ABSTRACT FROM AUTHOR]

As the penetration of distributed energy resources (DERs) increases significantly in the distribution networks, their integration reshapes distribution system power flows and leads to serious voltage quality problems in distribution networks. In this paper, a novel distributed cooperation voltage regulation method is proposed for future distribution networks. The proposed method can minimize the number of agents involved in the voltage regulation and it minimizes the change of required power for voltage regulation, which together minimizes the need for re-dispatching, i.e., the impact of voltage regulation on exchange of energy. Moreover, the proposed method performs online optimization, i.e., the value of the decision variable is physically implemented as a controller set-point at each iteration which reduces the response time. The presented algorithm is benchmarked against ADMM and centralized optimization. The results show the voltage regulation effectiveness. Compared to the centralized method, the proposed method performs better in the case of a single agent failure, and compared to the ADMM method, the proposed method greatly reduces the action time and does not require retuning if network topology or agent participation changes. [ABSTRACT FROM AUTHOR]

Resilience becomes vital for power grids facing the increasingly frequent extreme weather events. Microgrid formation is a promising way to achieve resilient distribution networks (RDN) when the utility power is unavailable. This paper proposes a RDN-oriented microgrid formation (RoMF) method based on the deep reinforcement learning (DRL) technique, which integrates the OpenDSS as an interaction object and searches for optimal control policies in a model-free fashion. Specifically, we formulate the microgrid formation problem as a Markov decision process, taking into account complex factors such as unbalanced three-phase power flow and microgrid operation constraints. Next, a simulator-based RoMF environment is constructed and integrated into the OpenAI Gym, providing a standard agent-environment interface for applying DRL algorithms. Then, the deep Q-network is used to search for optimal microgrid formation strategies, and an offline-training and online-application framework of the DRL-based RoMF is given. Finally, extensive numerical results validate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

A power electronic transformer (PET) can flexibly regulate the power flow of a network and facilitate the integration of distributed energy resources via AC/DC networks. With the continuous expansion of the scale of multiregional interconnection networks, the traditional centralized optimization method faces great challenges. This paper proposes a distributed convex optimal power flow (OPF) model by leveraging the hierarchical structural characteristics of PET enabled AC/DC distribution networks. An analytical target cascading method is introduced to divide the centralized OPF problem into several subproblems that can make the subnetworks operate individually and coordinate with each other. The PET flexibility is also harnessed to reduce network power loss and voltage deviation. To improve the computational efficiency and algorithm convergence, the convex relaxation and linear approximation methods are proposed. This allows us to transform the original nonconvex nonlinear problem into a successive convex programming problem, which can be easily solved by an iterative solution algorithm. Simulation results verify the feasibility and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Due to the complex structures and fault conditions, accurate fault sensing of the underground distribution cables is a difficult task. The distribution automation system (DAS) and fault indicator system (FIS) are only applicable to the severe faults. In addition, the current DAS and FIS are all based on the centralized data processing system (CDS), increasing the communication pressure and data processing time of the main station. Considering the advantages of edge computing, this paper investigates using the grounding line currents to sense the fault in distribution cable networks. First, an edge computing based fault sensing system (ECS) is designed. The equivalent time-domain distribution cable models considering multi-conductor mutual couplings are then established in both normal and fault cases. The time-domain features of the cable grounding line currents prior to and after the fault are analyzed to create the fault detection criterion. Combining the network topology, locations of the measurement units (MUs), and direction factor of the ground line current, a space matrix based algorithm is created to achieve fault cable section location at edge. Finally, a distribution cable network is created by PSCAD/EMTDC. Various simulations are conducted to validate the effectiveness and robustness of the algorithm. [ABSTRACT FROM AUTHOR]

The line parameters of power distribution systems stored in the dispatching center may have large errors due to the line aging and system operational dynamics. Such line parameter errors may directly affect the state estimation accuracy, and the advanced management functionalities rely on these parameters. This paper proposes an augmented state estimation method of erroneous distribution line parameters considering the presence of phasor measurement units (PMUs). The augmented state-space models under single and multiple measurement snapshots are established separately by combining the parameter state equation with the state-space model of the distribution network. The augmented state estimation based on the improved adaptive unscented Kalman filter (IAUKF) is developed. The proposed method can effectively perceive the process noise statistical parameter of the erroneous parameters during the estimation to accurately estimate line parameters. The proposed solution is validated through simulation experiments using the IEEE 33-bus system and the numerical results demonstrated its accurate parameter estimation performance.Also, the scalability of the proposed algorithm is demonstrated based on the simulation of the 118-bus system with distributed generators. [ABSTRACT FROM AUTHOR]

In DC distribution networks, the uncertainties of renewable energies and loads cause frequent voltage fluctuations. In this paper, a voltage regulation strategy based on the combined coordination of centralized optimal control (COC) and adaptive droop control (ADC) is proposed for droop-controlled DC distribution networks. In the COC strategy, the operation reference power of voltage source converters (VSCs) is optimized using the bus injection power measurements to improve the voltage profile and balance the power loading rates of different VSCs. The VSC capacity margin constraint is established to ensure that the capacity margin of each VSC is sufficient to cope with the unexpected fluctuation of loads and renewable energies. Then, an ADC strategy is adopted to handle voltage fluctuation between adjacent two COCs. In the ADC strategy, the operation reference power of the VSCs is further adjusted to recover the voltages. The voltage regulation is achieved based on the relationship among the VSC voltages and the minimum/maximum voltages of the whole network, which does not depend on the information of each nodal voltage and thus real-time network communication is avoided. The simulation results show that the proposed voltage regulation strategies can effectively improve the voltage profile under various uncertain environments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper applies the principles of distribution locational marginal pricing (DLMP) to unbalanced three-phase distribution networks. We first propose a linear model for AC optimal power flow derived through a series of approximation and reformulation techniques. Then a scenario-generation algorithm is proposed to properly model the uncertain parameters in the linear model. Through a proposed No U-Turn sampler (NUTS) based algorithm, probability density functions (PDFs) of DLMPs are calculated. These PDFs provide statistical information about the locational and temporal price risks. By means of applications to two IEEE unbalanced test networks, the numerical results show promising performance for the proposed linear model and the NUTS-based algorithm in creating PDFs of DLMPs. DLMP price densities will be increasingly useful as distribution system operators seek flexible, low risk solutions from embedded generators and aggregators of distributed energy resources. [ABSTRACT FROM AUTHOR]

In recent decades, the evolution of loads and distributed energy resources has added great complexity and uncertainty to distribution networks. In such a context, a reliable characterization of the uncertainty associated with prediction is fundamental to the wide range of the newly emerging applications in the low-voltage level grid. In this regard, this paper proposes a novel method to solve the short-term probabilistic load forecasting (STPLF) problem in distribution networks in which the loads are usually too volatile to be forecasted accurately. The approach developed employs a Dirichlet process mixture model (DPMM) to handle the uncertainty of load patterns, which is inferred by a Markov chain Monte Carlo (MCMC)-based method. Thereafter, the DPMM representation of the load patterns is combined with a tree-based ensemble learning method to address the STPLF by solving a classification problem. The final result is averaged over all MCMC samples. The proposed technique is compared to selected benchmark methods at different aggregation levels using smart meter datasets collected from customers located in Ireland as well as the City of Saskatoon, SK, Canada. The results obtained demonstrate that the proposed approach outperforms the benchmark methods in STPLF at the given aggregation levels. [ABSTRACT FROM AUTHOR]

To mitigate the voltage violation and overloading caused by the integration of Distributed Generation (DG) and Electric Vehicle Charging (EVC) loads, a feasible solution is to convert some AC medium voltage distribution lines to DC ones and formulate an AC/DC hybrid distribution network, based on which flexible power shifting between lines can be achieved. However, the complementarity of power flows amongst AC/DC lines is hardly described and embedded in existing configuration optimization methods in which only the average loading or the variations of loading for a certain period is considered. Solution space will be geometrically increased if operation scenarios are fully simulated for configuration caused by the strong uncertainties of DGs and EVC loads. This paper proposes an analytic quantifiable method to describe the power complementarity between AC/DC lines. The Gaussian mixture model is used to depict the temporal correlation and uncertainties. The complementarity is calculated by the conditional probability of multi-dimensional joint probability density function of AC/DC hybrid lines. Accordingly, an optimal configuration method of hybrid AC/DC distribution network is proposed considering the complementarity of power flows on AC/DC lines, in which the size and location of Voltage Source Converters (VSCs) are optimized. Simulations studies are performed to verify the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel contribution factor (CF) approach to predict diversified daily peak load of low voltage (LV) substations. The CF for each LV template developed in part I of the paper is determined by a novel method—clusterwise weighted constrained regression (CWCR). It takes into account the contribution from different customer classes to substation peaks, respecting the natural difference in time and magnitude between LV substation peaks and the variance within the templates. In CWCR, intercept and coefficients are constrained to ensure that the resultant coefficients do not lead to reverse load flow and can respect zero-load substations. Cross validation is developed to validate the stability of the proposed method and prevent over fitting. The proposed method shows significant improvement in the accuracy of peak estimation over the current status quo across 800 substations of different mixes of domestic, industrial and commercial (I&C) customers. The work in the two parts of the paper is particularly useful for understanding the capabilities of LV networks to accommodate the increasing penetration of low carbon technologies without large-scale monitoring. [ABSTRACT FROM PUBLISHER]

With the increasing number of controllable devices in the grid, devices owned by different entities are supposed to cooperatively support the grid operation. To balance the various interests of the customers and the utility, this paper proposes a two-level scheduling algorithm to address voltage regulation by coordinating the customer-owned battery energy storage system (BESS) and utility-owned load tap changers (LTCs) in power distribution systems. The objective is to find the optimal operation scheduling for both BESSs and LTCs that simultaneously maximize the economic payback and satisfy system operation rules. For BESSs, a local optimization problem is formulated in a chance-constrained manner in major consideration of the economic performance. For the utility interest, a Markov decision process (MDP) and a computationally efficient reinforcement learning (RL) algorithm are utilized to solve the optimal tap setting problem. To coordinate BESSs and LTCs, a global voltage adjustment is applied. The effectiveness of the proposed algorithm is validated via simulations on the IEEE 13-node and 34-node distribution test feeders. [ABSTRACT FROM AUTHOR]

This paper proposes a novel reactive power-based control strategy for single-phase PV inverters (PVIs) to simultaneously improve voltage unbalance (VU) and voltage regulation (VR) in low-voltage distribution networks. The proposed strategy relies on communication links between neighboring PVIs to exchange limited data. In this strategy, each PVI finds communication paths between itself and the closest neighboring ones connected to other phases. Then, using the obtained paths and the maximum and the minimum voltage magnitude of the grid, PVIs improve both VU and VR at the same time. The performance of the proposed control strategy is evaluated by various simulation studies using the IEEE European low-voltage test feeder and considering different operational conditions. In addition, the impacts of moving clouds and a failure in the communication links have been assessed. The simulation results exhibit that using the proposed control strategy, the voltage magnitude of all the nodes will remain within the allowed limits and at the same time, the phase voltage unbalance factor will be also significantly improved. [ABSTRACT FROM AUTHOR]

Point cloud based retrieval for place recognition is still a challenging problem since the drastic appearance changes of scenes due to seasonal or artificial changes in the environments. Existing deep learning based global descriptors for the retrieval task usually consume a large amount of computational resources ($e.g$., memory), which may not be suitable for the cases of limited hardware resources. In this paper, we develop an efficient point cloud learning network (EPC-Net) to generate global descriptors of point clouds for place recognition. While obtaining good performance, it can greatly reduce computational memory and inference time. First, we propose a lightweight but effective neural network module, called ProxyConv, to aggregate the local geometric features of point clouds. We leverage the adjacency matrix and proxy points to simplify the original edge convolution for lower memory consumption. Then, we design a lightweight grouped VLAD network to form global descriptors for retrieval. Compared with the original VLAD network, we propose a grouped fully connected layer to decompose the high-dimensional vectors into a group of low-dimensional vectors, which can reduce the number of parameters of the network and maintain the discrimination of the feature vector. Finally, we further develop a simple version of EPC-Net, called EPC-Net-L, which consists of two ProxyConv modules and one max pooling layer to aggregate global descriptors. By distilling the knowledge from EPC-Net, EPC-Net-L can obtain discriminative global descriptors for retrieval. Extensive experiments on the Oxford dataset and three in-house datasets demonstrate that our method achieves good results with lower parameters, FLOPs, GPU memory, and shorter inference time. Our code is available at https://github.com/fpthink/EPC-Net. [ABSTRACT FROM AUTHOR]

This paper proposes a novel data driven framework for detecting topology transitions in a distribution network. The framework analyzes data from phasor measurement units (PMUs) and relies on the fact that changes in network topology results in changes in the structure and admittance of the network. Using voltage and current phasors recorded by PMUs, the proposed method approximates network parameters using an ensemble-based deep learning model and thus, it does not require any knowledge of network parameters and load models. Using the prediction error of the proposed model, a connectivity matrix which shows the status of switches is constructed. In contrast to other methods, this proposed framework does not require a library of voltage and current transients associated with possible network transitions. It can also detect simultaneous switching actions and is robust to noise and load variations. The model yields a lower error detection rate, and its performance is validated using a modified version of the IEEE 33 bus network and a real feeder located in Queensland, Australia, under full and partial observability conditions. The proposed model has also been compared with another data driven method in terms of inference time and error detection rates. [ABSTRACT FROM AUTHOR]

In distribution networks with a high penetration of photovoltaic (PV), a coordination between reactive power compensation (RPC) of PV inverter and active power compensation (APC) of battery energy storage system (BESS) is always used in voltage regulation (VR). Since using a periodical VR inevitably increases communication utilization, how to design a new event-triggered one to reduce communication burden while achieving comprehensive voltage governance with the required minimum BESS sizing? Therefore, an event-triggered hybrid VR method is proposed in this paper. Firstly, an event-triggered communication is designed based on the changes in bus voltage and sensitivity, thereby defining all operating instants into transmission and ordinary sampling types. For the voltage violation issue at the transmission instants, a network-wide coordinated proportional RPC-APC is then designed, which keeps constant during one transmission interval. For the additional voltage fluctuation at the ordinary sampling instants in the interval, a local RPC-APC is additionally designed to yield a fast suppression without using any communication. In addition, the required minimum BESS sizing is obtained by calculating the total APC for VR requirement, which gives a reference in configuring BESS. Finally, the case study is conducted to demonstrate the effectiveness of this method. [ABSTRACT FROM AUTHOR]

The neutral grounding method is one of the crucial technologies used to ensure the safe and stable operation of distribution networks. With the development of power electronics-based devices, the application of highly controllable inverters to grounding systems has become a new research direction. In this review paper, based on the summary and analysis for the research status, the flexible grounding system is systematically classified according to its control strategy and structure. And then, the following core technologies are summarized: faulted line selection, current component detection, high-resistance ground identification, ground parameter measurement, neutral point voltage control, and fault location. Subsequently, deficiencies of flexible grounding systems are summarized from the following four perspectives: reliability, accuracy, speed, and stability. Finally, some discussions and recommendations for future development and research direction are presented. Especially, the reliability-enhanced hybrid grounding system that combines active inverters and low resistance for high-resistance ground faults is recommended. This paper provides a systematic literature review of the crucial technologies for flexible grounding systems and can be used as a technical reference for future research. [ABSTRACT FROM AUTHOR]

Accurate faulty phase detection under single-line-to-ground (SLG) fault is the basis of the emerging voltage-based fault arc suppression technology. Most conventional faulty phase recognition criteria assume the distributed line-to-ground parameters are symmetrical, therefore suffering invalidity in actual distribution networks especially under high-resistance ground-fault conditions. This paper firstly analyzes the magnitude and phase angle variation models of the phase-to-ground voltages before and after the SLG fault. Considering the asymmetrical distributed parameters and the line impedance, the specific phase-to-ground voltage variation rules corresponding to SLG fault on each phase are then discussed. Based on the voltage variation rules, a robust and practical method for faulty phase identification is proposed, which does not need the distributed parameters. Finally, the correctness of the variation rules and the availability of the proposed method is verified by simulation. Comparative study shows the proposed method has better accuracy and applicability with the conventional methods when the asymmetry and ground-fault resistance are high. [ABSTRACT FROM AUTHOR]

The work presented in this paper describes a multi-issue negotiation protocol between active consumers and a management platform in order to establish load coordination in a highly congested network. The multi-issue negotiation protocol considers simultaneously the consumption interval, the price, and the size of the energy packages, which is the main contribution of this work. Regarding the implementation methodology, the proposed algorithms have been implemented using MATLAB classes that allow emulating the behaviour of a multi-agent system in which each vehicle is an agent that interacts with the platform, which is another agent. In the present work, and without loss of generality, the algorithm is applied to coordinate the charging of electric vehicles (EVs) in a distribution network in which building loads represent critical loads. The algorithm is tested in a realistic environment, and its stability and performance are evaluated. Furthermore, the description of the algorithm is provided in a generic form, and it could be applied to any other scenario. [ABSTRACT FROM AUTHOR]

Volt/VAr Optimization (VVO) is becoming increasingly crucial in distribution management systems with renewable sources that require setting their reactive power in coordination with legacy voltage and reactive power control devices. This paper presents a VVO method that operates in complex variables based on the Wirtinger calculus; it employs a penalty method that keeps voltage magnitudes and controller parameters within limits together with a probabilistic rounding technique for handling switched device variables. The complex variable implementation permits using the compensation technique when solving the Karush-Kuhn Tucker (KKT) system, thus achieving a significant speedup due to limiting the number of linear system factorizations. The proposed method is contrasted with a classical VVO implementation employing discrete coordinate search from the current operating point and an enhanced version that uses sensitivity information. Numerical results on distribution networks having up to 3147 nodes show that the proposed method is significantly faster than classical methods and gives operating points free of voltage magnitude violations and lower power loss. [ABSTRACT FROM AUTHOR]

Cryptocurrency mining device (CMD) is a plug-and-play device installed by any low-voltage customer to convert electrical energy to digital money. Recently, the significant increase in the price of digital currencies has persuaded residential customers in many countries to employ CMDs to obtain quick and easy profit. However, these rapidly evolving loads can cause a substantial increase in the coincident peak load of distribution networks. Therefore, it can threaten the safe and reliable operation of the network. A straightforward way to restrict the penetration rate of these home-based miners is to increase the electricity price such that this business becomes unprofitable; however, this solution affects the national regulations and is not practical in many countries. This paper proposes a practical electricity pricing strategy to manage the operation of cryptocurrency mining loads appropriately. This pricing strategy can change the miner’ threat to an opportunity to sell more electrical energy during the off-peak hours to these loads and to maximize the profit of the utility. The proposed strategy is studied using the load profile dataset obtained from the measurements of Tehran Electricity Distribution Company, and its effectiveness is demonstrated. [ABSTRACT FROM AUTHOR]

In this paper, we study the optimal sizing planning of renewable distributed generation (RDG) in distribution networks to minimize the long-term cost, including the investment cost, maintenance cost, and operating cost. In particular, the operating cost itself is optimized by solving an optimal power flow (OPF) problem at each time $t$ based on uncertain time-varying RDG output and load demand. As a result, the sizing planning problem is a bilevel stochastic programming problem, which is hard to solve. Instead of resorting to conventional meta-heuristic algorithms, this paper first proposes a novel data-driven approach based on the philosophy of online convex optimization to solve the problem with drastically lower complexity. As a key step to facilitate the algorithm, we derive a closed-form expression to iteratively update the sizing solution upon drawing each data sample. With sufficient data samples, the proposed algorithm guarantees to converge to the global optimal solution regardless of the underlying probabilistic distribution of RDG output and load demand. Numerical results on the IEEE 13-bus test feeder, the IEEE 33-bus test feeder, and the Southern California Edison (SCE) 56-bus feeder show that our data-driven method drastically outperforms the other methods in terms of both the solution optimality and computational complexity. [ABSTRACT FROM AUTHOR]

This paper proposes a new local energy market design for distribution systems, which integrates peer-to-peer (P2P) energy trading and probabilistic locational marginal pricing. Distribution locational marginal pricing and P2P energy trading have each been proposed as potential alternatives to traditional retail pricing, to improve coordination between prosumers with distributed energy resources. Unidirectional locational pricing provides a scalable approach for coordinating demand, considering constraints and losses; while P2P energy trading allows prosumers to negotiate mutually beneficial bilateral energy transactions that increase the utilisation of their flexible energy resources. This paper proposes a market design combining the benefits of these two strategies. First, a new strategy for day-ahead locational marginal pricing is developed, which manages the uncertainty associated with local generation, demand and upstream prices by introducing a spread between the prices charged for energy imports and paid for energy exports. Then, local P2P energy trading platforms are integrated to additionally enable direct prosumer-to-prosumer trading, with transaction fees penalising energy transfers according to probabilistic differential locational marginal prices. Case studies are presented for a multi-phase low voltage distribution network, showing how the design can create value for prosumers, and the system as a whole, by reducing the curtailment of renewable generation. [ABSTRACT FROM AUTHOR]

The online monitoring data in distribution networks contain rich information on the running states of the networks. By leveraging the data, this paper proposes a spatio-temporal correlation analysis approach for anomaly detection and location in distribution networks. First, spatio-temporal matrix for each feeder line in a distribution network is formulated and the spectrum of its covariance matrix is analyzed. The spectrum is complex and exhibits two aspects: 1) bulk, which arises from random noise or fluctuations and 2) spikes, which represents factors caused by anomaly signals or fault disturbances. Then, by connecting the estimation of the number of factors to the limiting empirical spectral density of covariance matrices of residuals, the spatio-temporal parameters are accurately estimated, during which free random variable techniques are used. Based on the estimators, anomaly indicators are designed to detect and locate the anomalies by exploring the variations of spatio-temporal correlations in the data. The proposed approach is sensitive to the anomalies and robust to random fluctuations, which makes it possible for detecting early anomalies and reducing false alarming rate. Case studies on both synthetic data and real-world online monitoring data verify the effectiveness and advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed coordinated voltage control scheme for distribution networks with distributed generation (DG) and on-load tap changer (OLTC). In this scheme, static synchronous compensators (STATCOMs), DG units and OLTC are coordinated to regulate voltages of all buses to be close to the nominal value in the distribution network, mitigate voltage fluctuations, and minimize the number of operations of OLTC while considering different temporal characteristics of voltage regulation devices. The optimization problem of coordinating DG units and STATCOMs is decomposed by the gradient projection (GP) method. The local controller optimizes the reactive power outputs of DGs and STATCOMs according to local voltage and reactive power measurements, and still achieves the optimal coordination of DG units and STATCOMS in a decentralized manner without a central controller or communication between local controllers. The OLTC control scheme is designed to correct the long-term voltage deviations based on model predictive control (MPC) while minimizing the number of operations. The local controllers send the calculated reactive power references of DG and STATCOMs to the OLTC controller, which achieves distributed coordinated voltage control and mitigates the computation burden. A distribution network with two 20 kV feeders and 8 DG units was used to validate the control performance of the proposed coordinated voltage control scheme. [ABSTRACT FROM AUTHOR]

Coordination of networked microgrids (MGs) offers a promising solution to utilize distributed resources flexibilities and accommodate renewable energy. This paper studies real-time coordination of distribution system operation (DSO) and MGs considering multivariate uncertainty. Current researches suffer from inadaptability to dynamic system uncertainty, extensive iterations, and dependence on prediction. To fill these gaps, A novel multi-agent learning based stochastic dynamic programming (MASDP) is proposed to obtain the optimal policy for entities. Specifically, transactive energy control (TEC) mechanism, which requires only market-based information interactions, is employed to facilitate coordination between MG and DSO. A data-driven offline self-learning is proposed for entities to learn how to manage resources in response to system uncertainty. After sufficient offline learning, online operation of MASDP can be run in both non-iterative and iterative manners, by which near-optimal/optimal real-time solutions of DSO and MGs can be given sequentially and distributedly. Numerical comparisons with state-of-art policies and TEC algorithms verify the optimality, efficiency, adaptability, and scalability of MASDP. [ABSTRACT FROM AUTHOR]

The uncertainty in the direction and magnitude of fault current is a thorny issue for active distribution network (ADN) protection. Current differential protection (CDP) is suitable for ADN with complex operating modes and multiple types of DGs. However, the data synchronization conditions in transmission lines are usually not available in distribution systems with relatively low investment levels. Fault data self-synchronization (FDSS) algorithm is a low-cost data synchronization method specially used for the protection of distribution networks, but there will be large time synchronization errors under some extreme fault conditions. In this paper, the inherent defect of the conventional FDSS algorithm is first analyzed. Then an improved FDSS algorithm is proposed, which uses the current zero-crossing time and current slope polarity to estimate the starting delay difference at both terminals. On this basis, a corresponding CDP scheme is proposed. Compared with traditional CDP, the proposed scheme does not require additional synchronization equipment and high communication bandwidth. Finally, an ADN simulation model was built using PSCAD, and the improved FDSS algorithm program was implemented in MATLAB. Test results prove that the improved FDSS algorithm can effectively reduce the time synchronization error, and the corresponding CDP scheme is effective under various fault conditions. [ABSTRACT FROM AUTHOR]

The rapid growth of renewables and dynamic loads has highlighted the short-term voltage stability (STVS) issue in distribution network operation. With the advances in metering, communication and control technologies, deep learning-based distribution network reconfiguration (DNR) becomes available to maintain secure and economic system operation. However, STVS is traditionally evaluated for a specific network topology using time-domain simulations, which cannot be expressed explicitly as a function for optimization, making it intractable to consider STVS in DNR. In this paper, an efficient deep learning-based method is proposed to address this issue. An STVS evaluation network is customized from deep convolution neural networks (CNNs) and trained to learn the relationship between network topology and STVS performance from historical data. To find the optimal topology, a well-trained evaluation network is applied in DNR, where STVS with various network topologies is evaluated without resorting to time-domain simulations. Then, the number of candidate topologies is significantly reduced by a threshold of STVS performance, which enables the direct solution of the DNR for STVS enhancement. The application of the proposed method in large-scale systems is also discussed via integration with heuristic algorithms. Case studies of a modified 69-bus distribution system and a real large-scale distribution system validate the necessity of considering STVS enhancement in DNR and the effectiveness of the proposed solution method. [ABSTRACT FROM AUTHOR]

Distributed energy resources, together with information and communication technologies, have transformed the traditional electricity consumers into proactive consumers, namely prosumers. Prosumers can exchange their surplus energy with consumers through peer-to-peer (P2P) energy sharing. In this paper, the framework of P2P energy exchange in active distribution networks is developed using a multi-round double auction (MRDA) with average pricing mechanism (APM) integrated with distributional locational marginal price. The advantages of the proposed P2P framework include, 1) modeling and integration of the costs of voltage regulation, congestion, and power loss into the payments of agents for each transaction; 2) the entire distribution network clustered into multiple zones with transactions cleared at different levels, which decreases the additional costs for successful transactions, reduces the computational time, and increases the number of successful transactions; and 3) the matching algorithm encourages more prosumers and consumers to participate in P2P energy sharing and increases the efficiency and benefit from P2P market. The proposed MRDA-APM framework is validated by testing on the 33-node and 141-node distribution test systems. Simulation results demonstrate the effectiveness of the proposed mechanism for P2P energy exchange from both technical and computational viewpoints. [ABSTRACT FROM AUTHOR]

Cellular base stations (BSs) are equipped with backup batteries to obtain the uninterruptible power supply (UPS) and maintain the power supply reliability. While maintaining the reliability, the backup batteries of 5G BSs have some spare capacity over time due to the traffic-sensitive characteristic of 5G BS electricity load. Therefore, the spare capacity is dispatchable and can be used as flexibility resources for power systems. This paper evaluates the dispatchable capacity of the BS backup batteries in distribution networks and illustrates how it can be utilized in power systems. The BS reliability model is first established considering potential distribution network interruptions and the effects of backup batteries. Then, the analytical formula of the BS availability index is derived with respect to batteries’ backup duration. The dispatchable capacity of BS backup batteries is evaluated in different distribution networks and with differing communication load levels. Furthermore, a potential application, daily operation optimization, is illustrated. Case studies show that the proposed methodology can effectively evaluate the dispatchable capacity and that dispatching the backup batteries can reduce 5G BS electricity bills while satisfying the reliability requirement. [ABSTRACT FROM AUTHOR]

This paper presents a distributed voltage regulation method based on multi-agent system control and network self-organization for a large distribution network. The network autonomously organizes itself into small subnetworks through the epsilon decomposition of the sensitivity matrix, and agents group themselves into these subnetworks with the communication links being autonomously determined. Each subnetwork controls its voltage by locating the closest local distributed generation and optimizing their outputs. This simplifies and reduces the size of the optimization problem and the interaction requirements. This approach also facilitates adaptive grouping of the network by self-reorganizing to maintain a stable state in response to time-varying network requirements and changes. The effectiveness of the proposed approach is validated through simulations on a model of a real heavily-meshed secondary distribution network. Simulation results and comparisons with other methods demonstrate the ability of the subnetworks to autonomously and independently regulate the voltage and to adapt to unpredictable network conditions over time, thereby enabling autonomous and flexible distribution networks. [ABSTRACT FROM AUTHOR]

The knowledge of the topology of a wired network is often of fundamental importance. For instance, in the context of power line communications networks it is helpful to implement data routing strategies, while in power distribution networks and smart microgrids it is required for grid monitoring and for power flow management. In this paper, we use the transmission line theory to shed new light and to show how the topological properties of a wired network can be found exploiting admittance measurements at the nodes. An analytic proof is reported to show that the derivation of the topology can be done in complex networks under certain assumptions. We also analyze the effect of the network background noise on admittance measurements. In this respect, we propose a topology derivation algorithm that works in the presence of noise. We finally analyze the performance of the algorithm in a setup that is typical of power line distribution networks. [ABSTRACT FROM AUTHOR]

The conservation voltage reduction (CVR) and load modelling have acquired a renewed interest in reassessing the CVR capabilities and load model parameters for renewable-rich distribution networks. The underlying theoretical views behind the changes in load model parameters and subsequent CVR capabilities in the distribution networks caused by the high penetration of renewable generation have yet to be explored. This research has presented a mathematical analysis of equivalent network impedance with integrated distributed generations (DG) and further established a relationship of the equivalent network impedance with the static load model parameters and CVR to accurately assess the impact of DGs on the CVR. Then, this paper has demonstrated various case studies with IEEE 33 bus network to analyze and validate the changes in static load model parameters due to the integration of renewable DGs and batteries into the balanced and unbalanced power systems. The obtained results verify the increased CVR capabilities of a distribution network, caused by the higher active power exponent in the load model parameters, as affected by the integration of renewable DGs. [ABSTRACT FROM AUTHOR]