Search

Electrical power distribution systems are experiencing a deep penetration of renewable energy integration, which is reversing the power flow during some periods and modifying their conventional operation. In this context, the curtailment of renewable distributed generation is needed to consider the electrical limitations of transformers and lines, mainly voltage deviations and power overloads. Multiport converters with energy storage stand as a potential solution for enhancing power flow operation in the distribution system and minimising renewable generation curtailments. This paper presents an optimisation-based methodology to size multiport power converters and a techno-economic comparison to other conventional approaches. The optimisation includes energy storage components and AC power flow constraints. Moreover, specific days are considered to reduce computational requirements. This methodology is evaluated in a case study based on a medium voltage benchmark network from CIGRE to identify the best scenarios where multiport converters can be considered as a potential solution. Results conclude that the multiport converter provides clear advantages and is economically viable specially in scenarios with unbalanced distributed generation in the feeders, where their rated power is around 25 MVA, or if batteries are initially installed in the feeders. [Display omitted] • Optimisation-based methodology to size a multiport converter. • Multiport converter comparison with other solutions to reduce renewable curtailment. • Multiport converter can reduce curtailment the same or more than battery solutions. • High renewable penetration scenarios are feasible to install a multiport converter. [ABSTRACT FROM AUTHOR]

[Display omitted] • We assess a relaxation of the calculation criteria for nodal hosting capacity. • Dynamic HC (DHC) accounts for RES output stochasticity, enhancing grid flexibility. • Security-aware DHC (SDHC) contemplates the probability N-1 contingencies. • Security-aware DHC minimises risks, ensuring reliable energy integration. • DHC and SDHC allow to inject significantly more energy compared to static HC. Hosting capacity (HC) describes the electricity network's ability to accommodate distributed generation (DG) without deteriorating electrical performance indicators. Distribution system operators typically express their networks' HC as a single threshold, called static hosting capacity (SHC). SHC is determined via conservative regulatory criteria, increasing connection costs and time. This paper explores the potential for additional energy injection into the network via dynamic hosting capacity (DHC). A network node's DHC is derived from the hourly operation of the network, accounting for the time variability of existing distributed generation (DG) output and demand. The methodology considers the network assets' N-1 contingencies and their probabilities, defining the security-aware DHC (SDHC). The SDHC definition is technologically neutral. Through a case study of a radial medium voltage distribution network, the paper highlights the significant limitations of SHC due to conservative calculation criteria mandated by regulators. Annual injectable energy is increased by 62% to 76% when comparing DHC to SHC. Variations between average DHC and SDHC are below 0.01% due to low N-1 probabilities. This finding points out the potential of dynamic hosting capacity definitions, allowing more efficient use of the existing network and facilitating the integration of new DG capacity with reduced connection costs and time. [ABSTRACT FROM AUTHOR]

To mitigate the vulnerability of distribution grids to severe weather events, some electric utilities use preemptive de-energization as the primary line of defense, causing significant power outages. In such instances, networked microgrids could improve resiliency and maximize load delivery, though the modeling of three-phase unbalanced network physics and computational complexity pose challenges. These challenges are further exacerbated by an increased penetration of uncertain loads. In this paper, we present a two-stage mixed-integer robust optimization problem that configures and operates networked microgrids, and is guaranteed to be robust and feasible to all realizations of loads within a specified uncertainty set, while maximizing load delivery. To solve this problem, we propose a cutting-plane algorithm, with convergence guarantees, which approximates a convex recourse function with sub-gradient cuts. Finally, we provide a detailed case study on the IEEE 37-bus test system to demonstrate the economic benefits of networking microgrids to maximize uncertain-load delivery. • Formulated robust reconfiguration and operation of microgrids with load uncertainty. • Derived generalized constraints to ensure islands contain a voltage source. • Developed tractable reformulation and a cutting-plane algorithm to solve it. • Leveraged networked microgrids for distribution grid resilience under contingencies. • Case study ensured robust feasibility of proposed methods across uncertainty levels. [ABSTRACT FROM AUTHOR]

This paper proposes a linear approximation of the alternating current optimal power flow problem for multiphase distribution networks with voltage-dependent loads connected in both wye and delta configurations. We establish a set of linear equations that exactly describes the relationship between power changes at a bus and the corresponding values from a delta-connected device under specific assumptions necessary for a widely accepted linear model. Numerical studies on IEEE test feeders demonstrate that the proposed linear model provides solutions with reasonable error bounds efficiently, as compared with an exact nonconvex formulation and a convex conic relaxation. Our experiments reveal that modeling delta-connected, voltage-dependent loads as if they are wye-connected can lead to significantly different voltage profiles. We also investigate the limitations of the proposed linear approximation stemming from the underlying assumptions, while emphasizing its robust performance in practical situations. [ABSTRACT FROM AUTHOR]

Active distribution grids can experience voltage fluctuations and violations due to the high penetration of variable distributed energy resources (DERs). These problems might occur because of the uncertain and variable generation natures of these resources, especially solar photovoltaic resources, during panel shadowing scenarios. Volt-VAR control (VVC) is an efficient method that controls the reactive power set-points of the inverters to regulate the voltage of distribution grids. Although several VVC approaches have been proposed recently, the performance of these approaches degrades significantly if behind-the-meter solar generation data are unobservable/missing. Therefore, it is necessary to impute missing/unobservable PV data accurately to be utilized in VVC approaches. This paper proposes a model-free, data-driven, centrally trained, and decentrally executed multi-agent deep reinforcement learning-based VVC architecture to regulate the voltage of distribution networks. A generative adversarial network (GAN) is incorporated to impute the unobservable PV data accurately, which improves the performance of the proposed control architecture. The proposed multi-agent-soft-actor–critic algorithm (MASAC)-based VVC technique utilizes the actual PV dataset as well as the imputed dataset from the GAN framework to learn the optimal coordinated control policy for controlling the optimal reactive power set-points of PV inverters. The effectiveness of the proposed approach is analyzed on a modified IEEE 34-bus test case with added PV inverters. The results are compared and analyzed with a base case model with no VVC and VVC with a local droop control approach, genetic algorithm optimization, and a centralized soft actor–critic-based approach. Moreover, the performance of the proposed approach is compared with that of a multi-agent VVC framework without using the PV generation data and load information as the system state. The results illustrate that the proposed method with more state input improves the voltage profile and reduces the power loss of the network across various loading and PV generation scenarios. • A multi-agent GAN-DRL-based Volt-VAR control of distribution systems. • A generative adversarial network (GAN) is incorporated to impute the unobservable PV data. • A MASAC algorithm is used to control the reactive power set-points of PV smart inverters to control the voltage. • Case studies are performed on IEEE 34-bus and IEEE 123 bus systems and performance is compared with different state-of-the-art control approaches under different scenarios. [ABSTRACT FROM AUTHOR]

Low Carbon Technologies (LCTs), such as Photovoltaics (PVs), Electric Vehicles (EVs), and Heat Pumps (HPs), are expected to cause a huge electric load in future distribution grids. This paper investigates the grid impact in terms of over-loading and nodal voltage deviations in different distribution grids due to increasing LCT penetrations. The major objectives are the identification of the most severe LCT, grid impact issue, seasonal effect, and vulnerable distributional area, considering the physical models of the LCTs. It is concluded that Winter is the most hazardous for the future grid impact, characterized by nearly 3 times higher over-loading and 2.5 times higher voltage deviations during high HP penetrations, while suburban areas are the most vulnerable. Moreover, while HPs seem to have, in general, a greater impact compared to EVs, EVs cause more prolonged violations. While this work follows a bottom-up approach, using detailed physical models, aggregated national data has also been acquired, which is often used by top-down approaches. Different grid impact issues have been compared for the two approaches in terms of magnitude and duration. While bottom-up approaches generate more pessimistic results regarding the magnitude of the violations, results about the duration of the violations can be contradictory. • Transformer Overloading: a highly critical grid impact issue in LV distribution grids. • HPs provoke more violations compared to EVs, but EVs cause more prolonged violations. • Violations magnitude increase more than duration from light- to heavier-loaded grids. • EVs consumption is more well combined with PV generation than HP consumption. • Bottom-up approaches: Violations of higher magnitude but not always higher duration. [ABSTRACT FROM AUTHOR]

The allocation of electric power transmission lines in tunnels is a common practice in several applications, like underground lines in urban areas, connections of cables to substations, mining and railway systems. Construction of tunnels requires difficult and costly works. Besides, limitations of external and internal magnetic fields generated by the currents in the cables of the tunnel are needed. The present paper proposes and analyzes different statistical methods for calculation of optimal dispositions of cables and tunnel dimensions for reducing magnetic field and costs, for time varying currents in the cables. The implementation of these methods to a four-circuit real case probes their effectiveness. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed renewable energy resources brings a great challenge for real-time voltage security of distribution grids. The paper proposes a safe multi-agent deep reinforcement learning (MADRL) algorithm for real-time control of inverter-based Volt-Var control (VVC) in distribution grids considering communication delay to minimize the network power loss, while maintaining the nodal voltages in a safe range. The multi-agent VVC is modeled as a constrained Markov game, which is solved by the MADRL algorithm. In the training stage, the safety projection is added to the combined policy to analytically solve an action correction formulation to promote more efficient and safe exploration. In the real-time decision-making stage, a state synchronization block is designed to impute the data under the latest timestamp as the input of the agents deployed in a distributed manner, to avoid instability caused by communication delay. The simulation results show that the proposed algorithm performs well in safe exploration, and also achieves better performance under communication delay. • A MADRL based decentralized control strategy is proposed to achieve real-time voltage regulation under fast fluctuations. • A safe MADRL algorithm is proposed to achieve efficient exploration with guaranteed safety. • A data synchronization strategy is proposed to achieve guaranteed performance under communication delay. [ABSTRACT FROM AUTHOR]

Underground cables are intensively used, especially for transporting energy in urban areas. In general, the currents flowing in the cables change, following the loads profile. Moreover, the optimal installation of these cables must consider geometry, magnetic fields and arrangement costs. This paper proposes and analyses different statistical calculations of these optimal locations. The application of the methods to real data demonstrates the effectiveness of the statistical approaches in terms of costs and magnetic fields. [ABSTRACT FROM AUTHOR]

Advanced distribution management systems (ADMSs) are considered tools for deploying control and management strategies to address challenges in future distribution grids populated with distributed energy resources (DERs). This work presents a framework to integrate a degradation forecasting (DF) layer into ADMSs. Based on observations of considered DERs' degradation behaviors, a load-dependent degradation model is constructed and integrated into a dynamical Markov chain-based degradation prediction model. Multiple sources of data can be used to train the Markov prediction model, and then Evidence Theory (ET) is used to fuse predicted results from the prediction model to enhance the reliability of the prediction model. Then, the predicted results are used to adjust energy management (EM), which is a component of the ADMS concept, to abate the degradation of components with higher degradation costs. The proposed strategy is verified on the IEEE 33 bus system by numerical simulations. In addition, controller-hardware-in-the-loop (CHIL) experimentation for the proposed scheme is implemented. Both the numerical simulations and CHIL experimentation show operation cost savings for the studied system. • Propose a degradation forecasting and abatement framework. • Synthesize with real-time control and management system of distribution grids. • Provide CHIL implementation to support the proposed strategy. [ABSTRACT FROM AUTHOR]

Evolving power systems with increasing levels of stochasticity call for a need to solve optimal power flow problems with large quantities of random variables. Weather forecasts, electricity prices, and shifting load patterns introduce higher levels of uncertainty and can yield optimization problems that are difficult to solve in an efficient manner. Solution methods for single chance constraints in optimal power flow problems have been considered in the literature, ensuring single constraints are satisfied with a prescribed probability; however, joint chance constraints, ensuring multiple constraints are simultaneously satisfied, have predominantly been solved via scenario-based approaches or by utilizing Boole's inequality as an upper bound. In this paper, joint chance constraints are used to solve an AC optimal power flow problem while preventing overvoltages in distribution grids under high penetrations of photovoltaic systems. A tighter version of Boole's inequality is derived and used to provide a new upper bound on the joint chance constraint, and simulation results are shown demonstrating the benefit of the proposed upper bound. The new framework allows for a less conservative and more computationally efficient solution to considering joint chance constraints, specifically regarding preventing overvoltages. [ABSTRACT FROM AUTHOR]

Threats against power grids continue to increase, as extreme weather conditions and natural disasters (extreme events) become more frequent. Hence, there is a need for the simulation and modeling of power grids to reflect realistic conditions during extreme events conditions, especially distribution systems. This paper presents a modeling and simulation platform for electric distribution grids which can estimate overall power demand during extreme weather conditions. The presented platform's efficacy is shown by demonstrating estimation of electrical demand for (1) Electricity Reliability Council of Texas (ERCOT) during winter storm Uri in 2021, and (2) alternative hypothetical scenarios of integrating Distributed Energy Resources (DERs), weatherization, and load electrification. In comparing to the actual demand served by ERCOT during the winter storm Uri of 2021, the proposed platform estimates approximately 34 GW of peak capacity deficit. 1 1 These numbers are consistent with state-of-the-art prediction results published in the literature (Gruber et al., 2022). For the case of the future electrification of heating loads, peak capacity of 78 GW (124% increase) is estimated, which would be reduced to 47 GW (38% increase) with the adoption of efficient heating appliances and improved thermal insulation. Integrating distributed solar PV and storage into the grid causes improvement in the local energy utilization and hence reduces the potential unmet energy by 31% and 40%, respectively. • Modeling and simulation platform to estimate power demand during extreme weather conditions. • Demonstration of the platform on 2021 winter storm Uri conditions. • 34 GW of peak capacity deficit estimated for the current Electricity Reliability Council of Texas (ERCOT) grid. • 78 GW peak capacity deficit for load electrification, reduced to 47 GW with technology efficiency • Integrating distributed solar PV and storage reduces the potential unmet energy by 31% and 40%, respectively. [ABSTRACT FROM AUTHOR]

While electric vehicles (EVs) are expected to support decarbonizing transport, EVs can challenge the electricity system. Investigating the EV charging load and its flexibility, e.g., by shifting load, is therefore crucial to ensure a secure and sustainable energy system. We develop an agent-based model to investigate how different plug-in behaviors can affect (future) EV charging load profiles and their spatial–temporal flexibility. We contribute to extant literature by (1) revealing the effect of diverse plug-in behaviors on EV load profiles, particularly the flexibility potential resulting from different plug-in behaviors; (2) presenting the (future) charging load in different spatial structures, i.e. urban, rural, or suburban, and home, work, or public charging locations; and (3) demonstrating the effect of detailed driving profiles in high spatial and temporal resolution. We implement three future scenarios regarding EV and charging infrastructure diffusion and technology developments. We find that the impact of potential changes in plug-in behavior on EV charging load would be highest for urban areas and increases as charging infrastructure becomes more spatially diversified. Decision-makers in policy and industry can use these insights to evaluate the impact of EV charging on distribution grids and design incentives to leverage the flexibility potential of EVs. • Agent-based model with detailed mobility data to investigate EV charging loads. • Focus on plug-in behavior, spatial structures, and driving profiles. • Future scenarios to account for EV mass adoption. • Spatially diversified charging increases flexibility potential of plug-in behavior. • Greater impact of driving profiles than changes in plug-in behavior. [ABSTRACT FROM AUTHOR]

Phasor measurement units (PMUs) are time-synchronized measurement devices that have been proliferated in transmission networks during the last two decades. Recently, there have been efforts to bring this technology to distribution grids for different applications such as three-phase state estimation, fault and event analyses, and phase identification. Streamed time-synchronized voltage and current phasor data can be used for events classification and region identification along distribution feeders to determine the type and location of events, which are important features of any fault and event detection, location, and isolation software. In this paper, the spectral theory-based graph convolution is used for event classification and region identification. The proposed model uses modified graph convolution filters to aggregate the regional multi-rate samples of PMU data, i.e., voltage magnitude and angles from several nodes. Besides these temporal data of the measured nodes, the physical configuration of the network containing edge features are given to the graph convolution network (GCN) to not only classify the event type, but also identify the affected region and location. The proposed graph-based method is tested on a standard test system with capacitor and distributed energy resources-related events, malfunction of voltage regulator, sudden load changes, and different types of faults. The results are compared with baseline methods, Chebyshev graph neural network (GNN), decision tree, logistic regression and K-nearest neighbor using the accuracy, recall, precision and F-1 score metrics. Furthermore, performance sensitivity analysis is carried out with respect to the number of installed PMUs, measurement noise level, size of available historical data, availability of network edge features, and different designs of GNN. • A physics-informed Graph Neural Network (GNN) model is proposed for event classification and region identification. • The proposed study is developed to process multi-rate sampling PMU data stored in PV-penetrated distribution grids. • An efficient Graph Convolution filter is used, demonstrating a better performance compared to the conventional approaches. [ABSTRACT FROM AUTHOR]

The three-phase state estimation algorithms developed for distribution systems (DS) are based on traditional approaches, requiring components modeling and the complete knowledge of grid parameters. These algorithms are capable of dealing with the particular characteristics of DS but cannot be used in cases where grid topology and parameters are unknown, which is the most common situation in existing low voltage grids. This paper presents a novel three-phase state estimator for DS that enables the explicit estimation of voltage magnitudes and phase angles in all phases, neutral, and ground wires even when grid topology and parameters are unknown. The proposed approach is based on the use of auto-associative neural networks, the autoencoders (AE), which only require an historical database and few quasi-real-time measurements to perform an effective state estimation. Two test cases were used to evaluate the algorithm's performance: a low and a medium voltage grid. Results show that the algorithm provides accurate results even without information about grid topology and parameters. Several tests were performed to evaluate the best AE configuration. It was found that training an AE for each network feeder leads generally to better results than having a single AE for the entire system. The same happened when different AE were trained for each network phase in comparison with a single AE for the three phases. [ABSTRACT FROM AUTHOR]

• Review of fault location and classification methods in distribution grids. • The review includes methods developed for low-voltage grids. • The consideration of distributed generation in the available methods is studied. • The research trends of the fault diagnosis methods are presented. • Identification of research gaps and future challenges in the field of fault isolation. The evolution of the conventional power systems to smart grids has changed the way to conceive and operate them. The part of the grid evolving the most is the distribution grid where the installation of additional sensors and actuators has increased its observability and controllability. These have enabled the development of more accurate and automated processes including some critical ones such as the fault detection, isolation and restoration techniques. In this direction, unconventional methods, e.g. artificial intelligence, have been increasing in popularity over the last years. In this paper, fault location and fault classification methods are reviewed for both medium–voltage and the until recently unexplored case of low–voltage distribution grids. Different methods applied for both fault location and fault classification are being classified by the implemented technique. Such methods are explained and analyzed providing the main advantages and disadvantages of each category. Additionally, the research trends in both fields are analyzed and state–of–the–art methods from each category are thoroughly compared. Finally, the research gaps are identified. [ABSTRACT FROM AUTHOR]

The transformation process for a more cost-effective electricity sector has also brought radical changes to distribution networks. In Europe, fundamental differences between distribution system operators (DSOs) might jeopardise the process by increasing present technological gaps. In this work, we identify those technological features having an higher impact on the quality of the supplied service. By focusing on the duration of electricity interruptions indicator, we provide statistical models that identify feasible technological options to improve DSOs performances. Our analysis is based on a subset of the non-confidential DSO Observatory project dataset counting almost 100 unbundled DSOs. • Reliability measured through SAIDI is a key target for distribution grids. • Six multi-regression models have been built based on a European DSOs dataset. • The impact on SAIDI of several grid features has been analysed. • The need of an updated and validated DSOs dataset is discussed. [ABSTRACT FROM AUTHOR]

The exploitation of renewable energy resources poses great challenges regarding the manner in which they can be integrated into the modern electrical distribution infrastructure. To understand the difficulties of integration of Distributed Generation (DG) in electricity distribution network, the analyses and result of the effects on aspects of power quality are provided, as can be the problem of failure defects in networks and how DG grid connection affects voltage control at both medium-voltage (MV) and low-voltage (LV) levels. Results demonstrated that there was a communication system between all generators protective systems, a selective protective system, a tracking of perturbations system to isolate failure defects, and a phase control system between the generators and the network. Also synchronizing voltage regulation is crucial for guaranteeing the quality of the power supply. Currently, recent and modern technologies allow different services to share a single communications infrastructure while guaranteeing the required levels of security, reliability, and efficiency. This paper aims to provide a Smart City (SC) project, implemented in the city of Malaga, Spain, where the integration of the applications of Smart Grid (SG) and the Use Cases (UCs) for the different functionalities of SG has been developed. The SC architecture proposed envisions a hierarchical, distributed, and autonomous structure to address the challenge of smart distribution grids. As a result of this project, new levels of standardization of languages and protocols and new interconnection functions will be required. These functions will allow smart devices to recognize each other so that they can reconnect in case of failure regardless of the state of the network topology. In conclusion, renewable energy sources (RES) can be optimally integrated into the distribution grid. Generation can approach consumption through the installation of photovoltaic (PV) panels and mini-wind generators, with the reuse of electrical infrastructure. [ABSTRACT FROM AUTHOR]

[Display omitted] • Extended electric grids with fine time resolution require high computational effort. • Cellular approaches model real grids with decreased complexity for fast calculations. • Aggregated models require network reduction to preserve electrical behavior. • The presented network reduction method enables high modeling accuracy for cell models. • This method offers additional advantages for certain application purposes. Sustainable electricity supply can be achieved by expanding renewable energy sources (RES), which due to their volatile nature present challenges for the power grids. Modern electricity grids must be able to coordinate and balance these unpredictable generation patterns. One option for grid-friendly RES integration is multi-energy systems (MES) which enable energy use across energy carriers and, thus, relieve electrical grids. Therefore, long-term simulations of MES are indispensable and require appropriate models. Since MES have high systemic complexity due to high temporal resolution, spatial coverage, and hierarchical depth, modeling requires massive computational effort, impeding the investigations. Models with reduced complexity (spatial resolution reduction) and, thus, reduced computational effort can be created using a cellular approach. In particular, for electrical grids, reduction of complexity requires network reduction methods that create equal grid models regarding electrical behavior. Since most (numerical) network reduction methods (e.g., REI, WARD method) fail to replicate all required parameters, this work presents a novel network reduction method enhancing modeling accuracy, primarily regarding reactive power. The introduced method is based on a detailed parameter analysis to identify parameters responsible for deviations between original grid and reduced cell model. The validation of this method uses test networks at different voltage levels to reveal influencing variables that enhance modeling accuracy. This allows to derive trends for modelling accuracy of individual electrical parameters. The introduced method facilitates developing cell models for time-series-based calculations with maximum modeling accuracy and reasonable calculation effort. Additionally, this paper presents advantageous application purposes of this method. [ABSTRACT FROM AUTHOR]

Abstract: This work discusses the implications of interconnection of decentralized wind power generation on a real-life medium voltage distribution grid. Using measured wind generation data and simulated load profiles of the households, which are connected to the LV side at each MV substation, the impacts on voltage ranges, voltage profiles, line loading, and energy losses are analyzed. The availability to integrate a new wind mill into the grid which already contains three wind mills is examined with respect to the technical conditions for generation connection to the MV grids introduced by German Association of the Energy and Water Industries. [Copyright &y& Elsevier]

Abstract: The continuously increasing application of distributed photovoltaics (PV-DG) in residential areas around the world calls for detailed assessment of distribution grid impacts. Both photovoltaic generation and domestic electricity demand exhibit characteristic variations on short and long time scales and are to a large extent negatively correlated, especially at high latitudes. This paper presents a stochastic methodology for simulation of PV-DG impacts on low-voltage (LV) distribution grids, using detailed generation and demand models. The methodology is applied to case studies of power flow in three existing Swedish LV grids to determine load matching, voltage levels and network losses at different PV-DG penetration levels. All studied LV grids can handle significant amounts of PV-DG, up to the highest studied level of 5kWp PV per household. However, the benefits of PV-DG in terms of relative improvement of on-site reduction of demand, mitigated voltage drops and reduced losses were most significant at a penetration level of 1kWp PV per household. [ABSTRACT FROM AUTHOR]

• PV hosting capacity determination of distribution systems is important for planning. • Existing methods suffer from lack of theoretical guarantees, time inefficiency, and non-convex problem model. • We provide a bottom-up analysis, generalizing the geometric intuition to find optimal hosting capacity. • We adapt the analytical solution with respect to different operational constraints. • We use parallel computation to speed up hosting capacity calculation for large system. Power systems face increasing challenges on reliable operations due to the widespread distributed generators (DGs), e.g., rooftop PV systems in distribution grids. Characterizing the hosting capacity (HC) is vital for utilities to assess the total amount of distributed generations that a grid can deploy efficiently before upgrading. Some methods conduct extensive simulations for analyzing HC, which lack theoretical guarantees and can be time-consuming. Therefore, there are also methods employing optimization over all necessary operation constraints. However, the complexity and inherent non-convexity lead to non-optimal solutions. To solve the problems, this paper provides a constructive model for HC determination. Based on geometrical understanding, HC solutions are constructed sequentially according to realistic constraints so that the optimal solution can be reserved even with a non-convex model. For practical adaption, the three-phase unbalance condition is considered, and parallel computation is developed for speed-up. The method is validated by extensive numerical results on IEEE standard systems, such as 8-bus and 123-bus radial distribution grids. In comparison, the proposed approach obtains analytical hosting capacity solutions of larger values and uses less computation time. The results of HC verify that the proposed method reserves optimality and thus has good performance in complex scenarios, including more operational constraints, different PV deployment, and larger system size. [ABSTRACT FROM AUTHOR]

The integration of distributed energy resources (DER) complicates the operation of the power distribution grids, and the nodal voltage may violate frequently. Making accurate predictions of the nodal voltage is fundamental for voltage regulation of the distribution grid. Even though energy forecasting has been widely studied, voltage is still a rarely touched area. This paper enriches the research by proposing an ensemble approach for both deterministic and probabilistic short-term nodal voltage forecasting. Specifically, a new joint model- and data-driven feature selection is first performed to select the most relevant features for distribution grid voltage forecasting. Then, different individual forecasting models are trained using the selected features. On this basis, simple weighted averaging and quantile regression averaging approaches are applied to combine the individual models for deterministic and probabilistic forecasting, respectively. Finally, case studies are conducted on a real-world distribution grid to verify the effectiveness and superiority of the proposed method. • Joint model- and data-driven feature selection for voltage forecasting. • Ensemble learning approaches for deterministic and probabilistic voltage forecasting. • Real-world case study for a Swiss distribution grid. [ABSTRACT FROM AUTHOR]

This article presents an efficient voltage regulation method using capacitive reactive power. Simultaneous operation of photovoltaic power systems with the local grids induces voltage instabilities in the distribution lines. These voltage fluctuations cross the allowable limits on several occasions and cause economic losses. In the proposed method, the reactive power is applied at the load and generated using a capacitor bank. The capacitors are arranged in a binary order of capacitances to enable the 2n equally dispersed combinations. Initially, a strict analytical solution is developed to study the outcome of capacitance connection at load. The capacitance is selected based on the analytical solution of non-linear equations describing voltage deviations in the distribution line connected with photovoltaic systems. The control system is developed for fast and appropriate capacitance selection and a smooth transient process. The experimental setup is developed to verify the theoretical analysis. The measured data shows good agreement with the calculated one, verifying the correctness and accuracy of the proposed method. It is recommended that the reactive power compensation can be applied for a shorter time because the source current enhances substantially as the capacitance is connected to the load. The proposed method can be applied together with the tap-changers functionality, and the capacitance should be disconnected as the tap-changer corrects the transformation ratio. [ABSTRACT FROM AUTHOR]

This paper analyses optimal electricity investments (PV and battery storage) to decarbonise heat supply in residential buildings under different heat pump and energy retrofitting scenarios in a detailed representation of the Swiss power and heating system. The sensitivity of PV and storage deployment, including lithium-ion (LiB) and vanadium redox flow batteries (VRFB), with respect to distribution network capacity is also investigated. We propose an open-source dispatch sector coupling model (GRIMSEL-AH) to minimise energy system costs (social planner perspective) for heating and electricity supply in Switzerland with hourly and daily time resolution for electricity and heating respectively. Moreover, our representation of the Swiss energy system includes various types of consumers and urban settings which are represented with monitored electricity demand data for each sector and simulated heat demand data at the building level for the residential sector. We find that under a "business as usual" heat pump deployment and retrofitting rate, the optimal electricity investments correspond to 27.8 GW p of PV combined with 16.9 GW (33.8 GWh) for LiB and 1.9 GW (7.6 GWh) for VRFB. For this case, 57% (13.3 TW th /year) of the residential heat demand is covered by heat pumps with a total installed capacity of 19.7 GW th by 2050 (capacity exogenously set with its operation optimised). With increasing heat pump deployment, retrofitting rates are found to have a large impact on the investment in storage and a 100% heat pump scenario for the residential sector appears to be feasible. Our results show that heat pumps do not only decarbonise heat but also provide extra flexibility to the power system, since they increase local PV self-consumption, resulting in higher PV deployment. The model and the methodology presented in this study can be applied to other countries. • Open-source sector coupling modelling framework with flexible heat pump operation. • Impact of heat pump and energy retrofitting scenarios on electricity investments. • Retrofitting rates have a significant impact on the storage investment. • Heat pumps provide extra flexibility by increasing local PV self-consumption. • Trade-offs between storage and distribution grid upgrade are discussed. [ABSTRACT FROM AUTHOR]

• A global assessment approach for the European power system development up to 2050. • European long-term energy system outlook combine with grid investment and dispatch. • Grids evolution under a 2 °C climate change policy relevant for policy makers. • Assessing impacts on the CO 2 emissions and the development of the grid infrastructure. • Local flexibility solutions influence the evolution of the European power system. Previous works proposed a tool coupling models of a prospective outlook on long-term energy systems and a transmission grid investment and dispatch, focusing on the representation of the European transmission grid and its development on the horizon 2050–2100. In this paper, this prospective tool is further improved with the capacity to compute voltage as well as active and reactive power flows at the level of the distribution grid. This added capacity allows analyzing various issues related to the integration of variable energy resources in three representative real medium voltage distribution grids (urban, rural and semi-urban). Technical flexibility solutions such as on-load tap changers, variable energy resources curtailment and storage technologies are modeled and compared to reinforcement. A cost comparison between these flexibility solutions is also carried out. Finally, the new version of the tool is used to evaluate the CO 2−eq emissions linked to the development of the European power system infrastructure, with flexibility solutions, up to the year 2050 (both high voltage alternative and direct current lines reinforcement being considered) under a 2 °C climate energy policy scenario. Results show that it exists various options for the development of the European grid infrastructure, which are clearly sensitive to the level of accuracy in the representation of the physical infrastructures and their technical limitations. Being able to represent the distribution grid, in addition to the transmission one, has a noticeable impact on the prospective outlook of the European power systems both in terms of infrastructure reinforcement and estimation of the needs of flexibility solutions. [ABSTRACT FROM AUTHOR]

• Power injection variations due to Distributed Generators can cause voltage fluctuation. • Grids with resistive lines and unbalanced loads needs of voltage magnitude and phase compensation. • The Dynamic Direct Voltage Compensator (D2VC) can regulate voltage profile. • D2VC control is based on the estimation of the instantaneous symmetrical components of the voltage. This work proposes a voltage regulator to solve quality issues at the point of common coupling (PCC) of distribution grids affected by the intermittency of renewable energy sources. The proposed solution is a voltage compensator based on single-phase voltage source converter (VSC) units, called Dynamic Direct Voltage Controller (D2VC). The three-phase D2VC topology comprises three single-phase VSC back-to-back units. For each unit, one converter is connected in series with the grid at the PCC and the other converter is connected in shunt similar to an UPFC (Unified Power Flow Controller). To avoid coupling transformer, the series converter is directly connected in series to the existing distribution transformer thus no extra transformer is necessary. The D2VC can regulate the three-phase voltage profile at the PCC and balance all phases independently. The control strategy for each series converter is based on the estimation of the instantaneous symmetrical components of the voltage. Simulation results using PSCAD/EMTDC and an experimental platform of the D2VC are shown to confirm all the D2VC advantages in grids with resistive lines (R >> X). The proposed solution is much lighter and effective than the conventional indirect voltage control (reactive power control) and more robust than on load tap changer transformer. [ABSTRACT FROM AUTHOR]