Search

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]

For Arc High-Impedance Faults (AHIFs) in Active Distribution Networks (ADNs) with neutral grounding via Petersen coil, both nonlinear characteristics caused by arc and the influence of zero-input response have been not considered in previous research. Quantitative analysis methods based on the threshold setting comparison is mostly adopted in current field applications, which cannot balance the detection reliability and sensitivity simultaneously. In response to this problem, a higher precision nonlinear AHIF equivalent model is established based on the logarithmic arc model. State equations describing the occurrences of AHIFs are solved by using piecewise linear fitting technique, and analytical expressions of zero-sequence voltage and currents are present in paper. Furthermore, the regular volt-ampere characteristic dynamic trajectory between the zero-sequence voltage and current in characteristic frequency band is analyzed, and a tangent-inverse method is proposed to extract the direction of dynamic trajectory, then an effective fault detection algorithm is designed. The validity of the proposed nonlinear equivalent model and detection algorithm are verified by various cases’ simulations and field test data. [ABSTRACT FROM AUTHOR]

The distributed Volt/Var control solution is a promising one for active distribution networks (ADNs), but a critical issue is how to improve the distributed algorithm's convergence with less communication burden. In this paper, we propose a bi-level consensus alternation direction multiplier method-based fully distributed Volt/Var optimization algorithm (B-FADMM) for ADNs by exploiting inverter-based rapid control devices. In the first level, the Volt/Var optimal solution of subpartitions is obtained in parallel, and the second level completes the variable increment of each partition to correct the search direction of the first level. In the algorithm, firstly, an adaptive matrix $\omega $ initialization strategy is proposed to adapt to the different magnitude of variables, which avoids non-convergence or slow convergence of the algorithm. Secondly, the null-space method is used in the second level of the algorithm to obtain the null-space basis matrix on the active constraint of the subpartition, which reduces the dimensions of the problem and the computational complexity. In addition, the conjugate gradient (CG) algorithm is used to update the dual multiplier of each partition only by exchanging coupling information among partitions, which protects information privacy. Experiments showed the proposed approach attains the same result as the centralized, the computational performance of the proposed method is far superior to some popular other distributed methods in terms of accuracy, computational efficiency, and scalability. [ABSTRACT FROM AUTHOR]

This paper deals with the provision of primary frequency response (PFR) as ancillary service (AS) from active distribution networks (ADNs) to the transmission system (TS). In particular, two methodologies are developed. The first one aims to quantify the PFR capability range of the ADN. This range is defined by determining the range of the aggregated, i.e., equivalent, active power - frequency $P(f)$ droop curves that can be provided at the point of interconnection (POI) with the TS. The second one targets to optimally control $P(f)$ droop curves of individual distributed energy resources (DERs), installed in the premises of the ADN, to guarantee specific frequency regulation characteristic at the POI. This frequency regulation characteristic is expressed by means of a $P(f)$ droop curve. Both methods are tested on two discrete distribution systems. Several test cases are examined to demonstrate their implementation. Additionally, comparisons against conventional approaches and time series simulations are conducted to evaluate the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

The conventional volt-watt control method used in PV inverters to overcome the over-voltage problems can result in significant unfairness in the curtailed active power in PV-rich distribution networks. In this paper, a novel decentralized control strategy is presented to improve fairness in the curtailed active power of inverters. In this strategy, first, each PV inverter calculates the ratio of its node voltage magnitude change to its injected active power. Then, the calculated value by the farthest PV inverter of each phase from the transformer is shared with other PV inverters connected to the same phase. Using this value, PV inverters adjust their control parameters of an enhanced volt-var-watt control method. The performance of this method is evaluated using various simulation studies by making reference to the IEEE European low-voltage test feeder and considering different levels of PV system integration. It is shown that the proposed control strategy can significantly improve the fairness of the active power curtailment among PV inverters. Finally, the result of a comparative analysis between the proposed strategy and two state-of-the-art communication-based methods is reported. [ABSTRACT FROM AUTHOR]

Distribution systems are evolving from traditional passive networks into, what is known as, Active Distribution Networks (ADNs). Unlike traditional distribution networks, ADNs are characterized by bi-directional power flow, the high penetration of DERs, storage capabilities and sophisticated control strategies. Multiple layers of communications, sensing and computation are being integrated into ADNs for monitoring, control and protection of a variety of components and critical operations. This enhanced dependency on information and communication technologies, however, increases the exposure of ADNs to cyber-attacks. Several papers have been published in recent years with a focus on cyber-physical security (CPS) of smart grids. However, the published survey papers primarily emphasize the transmission level of smart grid threats and challenges, with little focus on the ADNs. Given the rapid deployment of ADNs and the increasing cyber threats against power grids and critical infrastructures, we are motivated, in this article, to present a review and survey focused, instead, on the latest research advancements in the area of CPS for ADNs. This paper represents the first survey of timely research in the area of CPS of ADNs with a focus on ADN critical operations and components. The cyber-physical aspects of each critical operation/component are analyzed. In addition, the challenges and requirements of associated communication protocols and standards are presented. Cybersecurity of ADN devices and sensors including Phasor Measurement Units (PMUs), smart meters, advanced metering infrastructure and protection relays are discussed in detail. Moreover, a thorough study of ADNs application drivers and enablers including microgrids, Electric Vehicles (EVs), Internet-of-Things (IoT) and smart homes is conducted. Potential and existing solutions by industry are highlighted. Finally, survey outcomes and directions for future work are presented to highlight emerging avenues of research.

Utility-scale and small scale wind and solar power installations along with electric vehicle charging stations, and other active sources of energy are increasing at the medium and lower voltage levels in the distribution grid. This situation requires a better understanding of the impact of high penetration of weather-dependent renewable energy sources on the operating conditions of the distribution network at both medium and low voltage levels. Despite the need, a multi-voltage level distribution network model, based on real network data and weather-dependent renewable generation data, has not been presented for distribution grid studies. This paper presents a comprehensive multi-voltage level active distribution network model based on real network data along with load and generation time-series for about a year. The network topology is modelled based on geographical data for various rural, semi-urban, and urban locations. The distribution network is embodied with a large share of renewable generation sources, with generation time-series simulated from meteorological data. The network is also flexible to incorporate other assets such as electric vehicle charging stations, storage, etc. The presented active distribution network model can be used to study, optimize, and control the effects of weather-dependent generation and other network assets in the distribution grid. [ABSTRACT FROM AUTHOR]

In traditional power systems, the load consumption inside the distribution networks has a relatively passive behavior. Consequently, transmission system operators have commonly aggregated the load centers using static loads such as exponential load models. This is a practical approach for bulk power system stability studies. In modern systems, the impact of distributed generation units on the system dynamics can no longer be neglected. Static load models fall short to represent the highly dynamic behavior of active distribution networks. This paper proposes a methodology for equivalencing such networks while conserving the dynamics of interest for long-term stability studies. The methodology is based on Hammerstein-Wiener models that use only boundary variables measured at the distribution network point of common coupling. Therefore, unlike gray-box models, no previous knowledge about the system is required. Furthermore, the proposed methodology has a transparent relationship to linear systems and a convenient block representation that can be implemented in commercial power system analysis software using only standard elements. This allows for easy implementation, overcoming a barrier for a rather conservative sector such as power systems planning and operation. The conclusions are derived from time-domain simulations on the IEEE test system for voltage stability assessment. [ABSTRACT FROM AUTHOR]

Legacy distribution systems need to adapt quickly to upcoming operating scenarios. The expected growth of electromobility and distributed generation will bring forth many challenges regarding efficient operation. Additionally, the limited communication infrastructure of these networks hinders the applicability of heavily centralized control strategies. In this context, this paper proposes a distributed control scheme for photovoltaic generators. The scheme comprises a local real–time control layer for the synthesis of active and reactive power references, using traditional voltage droop control, along with affine policies to handle the uncertainty associated to solar irradiance and demand levels. The characterization and periodic adaptation of such control scheme is achieved via a global, computationally tractable robust model able to co–optimize both droop and affine policy parameters. Performance is benchmarked via extensive computational experiments against other state–of–the–art distributed and local control strategies. Results suggest the proposed scheme is able to outperform all competitive frameworks. Its performance is also evaluated as a strictly local control scheme obtaining superior voltage regulation as compared to the recommendations in the Standard IEEE 1547.8. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid distribution transformer integrating photovoltaics (HDT-PV) with local and remote cooperative control in an active distribution network (ADN). HDT-PVs, regarded as promising devices with high reliability and easy engineering in ADNs, can not only realize the on-site maximum utilization of distributed solar energy but also solve the various power quality problems that follow. Moreover, the reactive power flow in the ADN can be optimized through remote cooperative control of multiple HDT-PVs. In this situation, the two objectives of voltage regulation and network power loss decrease are achieved in the ADN. If the power electronic device in the HDT-PV fails, the HDT-PV is able to work as a conventional distribution transformer by cutting off all power electronic devices and PV systems. Even when a small number of HDT-PVs malfunction, the system can still run stably. Finally, the simulation and experimental results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

The increase in distributed energy resources and flexible electricity consumers has turned TSO-DSO coordination strategies into a challenging problem. Existing decomposition/decentralized methods apply divide-and-conquer strategies to trim down the computational burden of this complex problem, but rely on access to proprietary information or fail-safe real-time communication infrastructures. To overcome these drawbacks, we propose in this paper a TSO-DSO coordination strategy that only needs a series of observations of the nodal price and the power intake at the substations connecting the transmission and distribution networks. Using this information, we learn the price response of active distribution networks (DN) using a decreasing step-wise function that can also adapt to some contextual information. The learning task can be carried out in a computationally efficient manner and the curve it produces can be interpreted as a market bid, thus averting the need to revise the current operational procedures for the transmission network. Inaccuracies derived from the learning task may lead to suboptimal decisions. However, results from a realistic case study show that the proposed methodology yields operating decisions very close to those obtained by a fully centralized coordination of transmission and distribution. [ABSTRACT FROM AUTHOR]

Relying on the power flexibility of distributed energy resources (DERs) located in an active distribution network (ADN), this ADN will be able to provide power flexibility to the upper-layer grid at their point of common coupling (PCC). The power flexibility is defined as additional bi-directional active/reactive powers a resource can provide to the grid by adjusting its operating point. In this context, this paper presents a two-stage ADN management method to deliver, at the PCC, the power flexibility that the upper-layer grid operator would request minutes-ahead real-time operation. The first stage updates the power set-points of DERs considering their offer curves as well as the uncertainties stem from the short-term forecast errors of demand and renewable generation profiles. The inter-temporal constraints and losses of the grid are accounted for by exploiting a linearized dynamic optimal power flow model, whereby the first stage is implemented as a linear scenario-based optimization problem. Then, in real-time operation, relying on a linear optimization problem, the second stage adjusts the power flexibility injection of a utility-scale battery energy storage system (ESS) to mitigate the imbalance at the PCC inherent in the above-mentioned uncertainties. The performance of the proposed method is tested in the case of a real ADN located in the city of Aigle in southwest of Switzerland. [ABSTRACT FROM AUTHOR]

Non-negligible amounts of Distributed Generating Units (DGUs) are already connected to power systems, predominantly to medium- and low-voltage networks. The interaction between distribution and transmission systems is gaining the attention of system operators. Given the high controlability of DGUs, the active distribution networks can potentially support the transmission system during emergency situations. Previously proposed voltage emergency controls involving distribution network assets focused on preserving the transmission system integrity, even if it implies affecting the distribution system operation (intrusive schemes). More attention needs to be devoted to supporting transmission voltages, while preserving the distribution network operation (non-intrusive schemes). This paper introduces a new adaptive and non-intrusive voltage emergency control scheme based on the synchronization of DGUs and the load tap changer of the corresponding distribution transformer. The conclusions are derived from time-domain simulations using an extended version of the IEEE Nordic test system for voltage stability assessment. [ABSTRACT FROM AUTHOR]

Protection schemes are essential in active distribution networks and microgrids’ reliable, efficient, and flexible operation. However, the protection of these networks presents significant challenges due to operational changes, such as variations in topology, distributed energy resources connection/disconnection, and microgrid operating modes, among others. This paper proposes an adaptive protection scheme based on overcurrent devices with several setting groups based on artificial intelligence algorithms. The developed strategy is composed of two stages. In the off-line stage, a clustering technique is employed to group the active distribution network operating scenarios exhibiting similarities. The optimal settings for the protection devices are determined for each set of scenarios. On the other hand, in the on-line stage, the protection strategy’s implementation and operation, considering the active distribution network’s existing communication system, are defined. Furthermore, the approach formulates the overcurrent relay coordination as a mixed-integer non-linear optimization problem, and as a result, the optimal setting of the overcurrent protection devices is obtained. It aims to minimize the operating time, considering the transformers’ thermal limits, fuse operating curves, and overcurrent relay settings. The solution is determined by using an Augmented Lagrangian genetic algorithm. The presented protection scheme is validated on the modified IEEE 34 node test feeder, considering the main operating scenarios of the active distribution networks, such as topology changes, distributed energy resource connection/disconnection, and microgrid operating modes (on-grid and off-grid). The results obtained and its easy implementation indicates the high potential for real-life applications.

Dispatching active distribution networks (ADNs) is an energy-intensive application that, if implemented via battery-energy storage systems (BESSs), can require a large capacity of these assets in order to fully balance the uncertainties caused by the stochastic demand and generation. The insufficient capacity of the BESSs often leads to their state-of-charge (SOC) saturation; this results in unreliable dispatch tracking. In this work, we propose and experimentally validate a real-time control scheme that achieves a highly-reliable dispatching of ADNs and ensures that the BESSs’ SOC is not saturated during the daily operation. Our proposed scheme uses a two-layer model predictive control (MPC). The upper-layer MPC, running every 5 minutes, optimizes the BESSs’ SOC trajectories while minimizing the tracking error, considering the prosumption forecast of the whole day. Then, the lower layer MPC, running every 30 seconds, takes the BESSs’ SOC trajectories as constraints while achieving a high-resolution tracking of the dispatch plan over the current 5-minute time horizon. Both layers account for the grid constraints by using the augmented relaxed optimal power-flow (AR-OPF) model; an exact convex relaxation of the original AC-OPF and used in this paper (for the first time in the literature) to solve a real-time constrained control problem for ADNs. Our proposed framework was experimentally validated using a 1.5 MVA/2.5 MWh BESS connected to an actual 24-node medium-voltage (MV) ADN that, in Aigle, Switzerland, hosted an uncontrollable 3.2 MWp distributed photovoltaic generation, 3.4 MVA hydro-power generations, and a 2.8 MW base demand.

The frequent switching operations and triggering events of automation devices are poorly documented in distribution management systems, leading to challenging work regarding the timely detection of system state changes. Additionally, the limited measurements in distribution networks and the randomness of plug-and-play devices make obtaining real-time observations of system states difficult. Furthermore, distributed generation (DG) output uncertainties exacerbate the situation. Thus, we propose a dynamic topology awareness (DTA) approach to make judgments regarding operational states and identify all possible topology changes (including normal operations, islanding and outages) in the absence of real-time DG measurements. The method consists of two steps. The first step applies parallel Gaussian mixture model-based probabilistic state estimators (GMM-PSEs) of subareas and extended subareas to calculate the probability distributions of border bus voltages with full consideration of DG uncertainties. The second step utilizes the Kullback-Leibler (KL) divergence measure to evaluate the difference between the two probability distributions estimated by subareas and extended subareas, thereby obtaining an indicator (average KL divergence) for identifying possible topologies. The effectiveness of the proposed method is examined on the IEEE 33-bus and IEEE 123-bus test cases. It is computationally efficient and accurate when identifying all possible topologies under limited measurements and DG uncertainties. [ABSTRACT FROM AUTHOR]