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The increasing penetration of renewable generation and distributed energy resources requires new operating practices for power systems, wherein risk is explicitly quantified and managed. However, traditional risk-assessment frameworks are not fast enough for real-time operations, because they require numerous simulations, each of which requires solving multiple economic dispatch problems sequentially. The paper addresses this computational challenge by proposing proxy-based risk assessment, wherein optimization proxies are trained to learn the input-to-output mapping of an economic dispatch optimization solver. Once trained, the proxies make predictions in milliseconds, thereby enabling real-time risk assessment. The paper leverages self-supervised learning and end-to-end-feasible architecture to achieve high-quality sequential predictions. Numerical experiments on large systems demonstrate the scalability and accuracy of the proposed approach. • Rising renewables and distributed resources need new power system risk assessments. • A new risk assessment framework utilizing fast optimization proxies is proposed. • The framework uses an end-to-end feasible proxy for scalable and accurate training. • Proposed risk assessment is highly accurate and 30x faster than optimization methods. [ABSTRACT FROM AUTHOR]

While peer-to-peer energy trading has the potential to harness the capabilities of small-scale energy resources, a peer-matching process often overlooks power grid conditions, yielding increased losses, line congestion, and voltage problems. This imposes a great challenge on the distribution system operator (DSO), which can eventually limit peer-to-peer energy trading. To align the peer-matching process with the physical grid conditions, this paper proposes a cost causality-based network cost allocation method and the grid-aware peer-matching process. Building on the cost causality principle, the proposed model utilizes the network cost (voltage, congestion, and loss) as a signal to encourage peers to adjust their preferences ensuring that matches are more in line with grid conditions, leading to enhanced social welfare. Additionally, this paper presents mathematical proof showing the superiority of the causality-based cost allocation over existing methods. • Proposing a causality-based cost allocation method. • Demonstrating the superiority of causality-based cost allocation. • Designing coordination P2P energy trading and network operation using network costs allocation. [ABSTRACT FROM AUTHOR]

• The optimal cloud entropy algorithm is utilizd to enhance the gray cloud model, and a dynamic weight allocation evaluation method is integrated based on variable weights for comprehensive state perception. • Dynamic indicators such as voltage deviation, voltage fluctuation, and other dynamic metrics as evaluation criteria are introduced, with a primary focus on the influence of dynamic indicators in scenarios with high new energy penetration rates. • A novel multi-scale information fusion method is implemented. This fusion method effectively addresses the issue of incomplete identification frameworks and reduces the impact of sensor errors. • Cases comparison is conducted in actual transformer operating environments with varying levels of new energy penetration rates to validate the superiority and scalability of the proposed model in transformer state assessment. The power transformer is the most important and critical component in the power grid of the electrical system. Its safe and stable operation is of great significance for the reliable transmission of renewable energy generation and the reliable power supply to end users. With the continuous development of new types of power systems, the load of the power system undergoes drastic changes, resulting in increased volatility and instability, which leads to issues such as overload, harmonics, and short circuits in transformers. Therefore, in order to accurately assess the operating status of transformers and promptly identify any existing conditions, a method is proposed in this paper. This method uses an optimal cloud entropy parameter calculation method and a variable weighting method to optimize the gray-cloud evidence model. Through a novel multi-source information fusion method, the results of various test items are fused to obtain the state awareness results. Meanwhile, different evaluation indicators are selected for different levels of renewable energy penetration and compared with other methods. The results are validated through examples, demonstrating that the method proposed in this paper can accurately reflect the operating status of transformers and has good scalability. [ABSTRACT FROM AUTHOR]

Prosumers with flexible distributed energy resources (DERs) can be aggregated as a virtual power plant (VPP) to participate in the electricity market. However, the VPP's energy management requires the prosumers to share individual data, which poses privacy concerns. This paper proposes a distributed differentially private energy management strategy for the VPP to maximize its profit by coordinating the prosumers and at the same time mitigate the privacy risks of local prosumers. Specifically, the coordination of prosumers is first formulated as an optimization problem, which includes the operation constraints of each individual prosumer and the VPP coordination process. On this basis, this paper proposes to solve the optimization problem with a two-level privacy protection strategy. For the individual level, a distributed solution framework is proposed to keep the private information of each prosumer preserved locally. For the communication level, a differential privacy mechanism is integrated into the information exchange process thus reducing the privacy leakage risk. Both theoretical and practical results are provided to verify the performance of the proposed method in terms of optimality and privacy protection levels. • A two-level privacy-preserving framework for the energy management of VPPs. • A distributed differentially private solution algorithm implemented in parallel. • Both theoretical and practical results for the performance of privacy and accuracy. [ABSTRACT FROM AUTHOR]

The virtual power plant (VPP) can aggregate massive distributed resources to participate in power system operation. Different types of resources have been investigated to construct VPPs. Internet data centers (IDCs) are regarded as flexible power consumers that can potentially cooperate with power systems. This paper exhibits the insight for constructing VPPs based on IDCs while comprehensively considering the operational flexibility of the workloads and power supply devices. On the workloads, this paper models the temporal and spatial flexibility of workloads in detail. On the power supply devices, this paper explores their spare capacity with respect to the power supply reliability requirement. Then, a day-ahead scheduling method of the IDC-based VPPs is established based on the operational flexibility modeling. Case studies validate the proposed approach. Moreover, because of the coupling of workload dispatch and power supply device operation, the synergy effect does not equal the sum of its parts. • Exploring the operational flexibility of IDCs to construct VPPs.. • Quantifying the potential of backup power under the reliability requirement. • Realizing the coordination of the backup power and computation workloads in IDCS. • Proposing a day-ahead scheduling framework for IDC-based VPPs. [ABSTRACT FROM AUTHOR]

Topology identification (TI) is critical in electric grids, especially in distribution networks, for detecting unintended changes due to fault events and for some control and optimization functions in normal operation. Topology identification in distribution networks has been widely investigated in the literature. This paper reviews the literature on topology identification in passive and active distribution networks (PDNs, ADNs). The paper discusses model-based and model-free methods of TI, including the methods that use time-series and multi-snapshot data. This paper also reviews special problems such as joint parameters estimation/topology identification and simultaneous multiple switching actions. The problems in TI are due to the presence of distributed energy resources (DERs) in the ADNs, data availability problems including optimal micro phasor measurement units (μ PMUs) placement and data quality, and application of TI including normal operation switching detection, and outage detection have been addressed in this paper. • Frequent topology changes due to the intermittency of distributed energy resources. • Strong correlation of adjacent buses in the presence of distributed generations. • Significant effect of meter places and data quality on topology identification result. • Renewable inverters provide intentional probing topology identification methods. • Cyberattack detection comes from data availability of customers and power utilities. [ABSTRACT FROM AUTHOR]

• The proposed single-ended fault location framework fully considers the effect of partial coupling, without requiring measurements from the remote ends. • Within the proposed framework, two methods based on impedance line model and fully distributed line model are presented. • The effectiveness of the proposed methods is validated through many simulation analyses. The proposed methods demonstrate some robustness towards measurement noises and parameter errors. Accurate fault location technology can not only speed up fault search but also enhance the power supply reliability. This paper introduces a single-ended fault location framework for single line to ground faults, taking into account the effects of partially coupled lines. The proposed framework relies only on local measurements. Within the proposed framework, the two methods proposed in this paper are based on the impedance line model and the fully distributed parameter line model, respectively. The impedance model based fault location method does not require iteration and has no risk of divergence, while the method based on the fully distributed parameter model has higher accuracy. The estimated results of the impedance-based method can be used as the initial values of the solution of the distributed parameter model based method. The proposed methods are validated through a number of testing cases, encompassing various fault locations, resistances, and coupling ratios. [ABSTRACT FROM AUTHOR]

• Analysis of traction power supply system is conducted considering cross-bonding cable. • Model simplification method based on multiport network hybrid parameters is designed. • The calculation difference of loss before and after consideration is more than 10 %. • Cross-bonding cables should be set near the position with large train power. The urban rail transit traction power supply system adopts DC power supply. The upstream and downstream rails are generally connected by cross-bonding cables at intervals, so as to decrease the rail equivalent resistance and reduce the rail potential and loss. The existing modelling methods for power flow calculation do not consider the influence of cross-bonding cables due to the complexity of modelling, which causes some deviations in the calculation results. Hence, a modelling method for power flow calculation of urban rail transit traction power supply system considering cross-bonding cables is proposed in this paper. Firstly, the influence of cross-bonding cables on power flow model is analyzed. Subsequently, a model simplification method based on multiport network hybrid parameters is designed. Finally, power flow calculations considering cross-bonding cables are carried out with a metro line. The calculation results show that the calculation difference of traction network loss before and after consideration is more than 10 %. Moreover, this paper analyzes the influence law of the setting of cross-bonding cables on the power flow state, and gives some suggestions on the setting in actual engineering application. [ABSTRACT FROM AUTHOR]

• A new control method for DER is suggested. • The proposed control method balances the load between feeders. • DER connects to feeder through SST. • DER-controlled circuit shared the load between two feeders like ring connection. • Voltage profiles are improved. The multi-purpose use of distributed energy resources (DER) can bring a lot of income to its owners. For example, the load imbalance of the radial feeders in the distribution network is an old problem that occurs due to the simple structure of the radial topology and the unidirectional power flow in traditional networks. With emerging DERs and the development of power electronic technology, previous concepts are changing continuously. This paper aims to balance the load between two feeders whilst, it yields better voltage profiles and more appropriate utilization of distribution feeders. This paper proposes a new control method for DERs (including PV panels plus battery storage) that makes the radial system pretends a ring topology properties. It is done with the help of a solid-state transformer (SST). The relationships of the DER current controller are formulated based on current passes through the ring connection. Case studies are carried out on improved IEEE 33-node and 13-node systems with DERs and SSTs. The results show that the suggested control method can simulate the real ring topology and operating objective namely, load balancing is reached. [ABSTRACT FROM AUTHOR]

• Develop the uniform design method to comprehensively quantifies the self-healing performance of the system. • Establish three indicators for evaluating the self-healing performance of distribution network covering self-healing range, speed, and complexity. • The combination of network reconstruction and island partition method is proposed to design the self-healing strategy. With the increase in power demand and system complexity, accurately evaluate the self-healing performance of distribution network has become one of the key factors in ensuring the reliability and stability of power supply. To address the issues of incomplete evaluation scenarios and low evaluation efficiency in current self-healing evaluation, this paper proposes the uniform design-based method for evaluating the self-healing performance of the distribution network. It enables comprehensive and fast evaluation of self-healing under multiple fault scenarios. Firstly, three indicators are proposed to evaluate the self-healing performance: self-healing rate, self-healing speed, and self-healing operation complexity. Then, the calculation of three self-healing indicators is carried out under the self-healing strategy that coordinates both network reconstruction and island partition. By uniformly sampling the enumerated k -order system states, the computational time cost is greatly decreased, avoiding the problem of high computational complexity under high-order states and improving the efficiency of evaluation process. Finally, case study shows that the uniform design method greatly decreases the computational time (nearly 20 times faster than the state enumeration) on the IEEE 33-node distribution network. [ABSTRACT FROM AUTHOR]

This paper studies the impact of distributed energy resources (DER) on economic displacements or delays of power system investments. We investigate how the operational flexibility from DER controllability can influence the integrated expansion of transmission and energy storage. Given the interplay of flexibility provision from different technologies, accurately representing uncertainties is essential to avoid over- or under-estimating the flexible operational capabilities of DER. To address this challenge, we propose a multi-stage stochastic expansion planning model that can optimise transmission and storage investments, as well as DER services against long-term uncertainties and detailed operational constraints. We employ a four-stage scenario tree to represent uncertainties and a Dantzig–Wolfe decomposition within a column generation approach to tackle computational challenges. Case studies performed on real Australian National Electricity Market (NEM) scenarios demonstrate that a deterministic model overestimates the capabilities of controllable DER to displace transmission investments, particularly in early stages. Conversely, the proposed stochastic model provides a more measured assessment, maintaining a steadier estimate of transmission displacement potential by controllable DER throughout various stages. • Multi-stage model optimising DER with investments in transmission and storage. • Analysis of DER controllability's impact on investments under uncertainty. • Comparison of deterministic and stochastic approaches in real scenarios. • Stochastic model enhances investment robustness by incorporating flexible DER. • Deterministic models show increased uncertainty in resulting portfolios. [ABSTRACT FROM AUTHOR]

Modern distribution systems with high penetration of distributed energy resources face multiple sources of uncertainty. This transforms the traditional Optimal Power Flow problem into a problem of sequential decision-making under uncertainty. In this framework, the solution concept takes the form of a policy , i.e., a method of making dispatch decisions when presented with a real-time system state. Reasoning over the future uncertainty realization and the optimal online dispatch decisions is especially challenging when the number of resources increases and only a small dataset is available for the system's random variables. In this paper, we present a data-driven distributed policy for making dispatch decisions online and under uncertainty. The policy is assisted by a Graph Neural Network but is constructed in such a way that the resulting dispatch is guaranteed to satisfy the system's constraints. The proposed policy is experimentally shown to achieve a performance close to the optimal-in-hindsight solution, significantly outperforming state-of-the-art policies based on stochastic programming and plain machine-learning approaches. • We present a data-driven distributed policy for decisions in a distribution system. • The decisions are made online and under uncertainty. • Decisions are assisted by a Neural Network but satisfy system constraints. • The policy performs close to the optimal, outperforming state-of-the-art policies. [ABSTRACT FROM AUTHOR]

The increasing amount of Distributed Energy Resources (DERs) in Distribution Networks (DNs) has awakened the interest of system operators to represent how DERs will react to large disturbances. These units will respond according to the ride-through capability curves, the tripping logic settings, the local voltage magnitude, and frequency measurements. In this paper, we show the limitations of the well-known DER_A model and propose changes to represent better the ride-through capabilities and the tripping of a population of DER units. We also show the need for more than one aggregation in the same bus to represent the response of the old and modern DER technologies. To validate the new model, we run dynamic simulations of a Transmission Network (TN) and DNs with hundreds of DER units modeled in detail. The simulation results show that the proposed changes improve the representation of a population of DERs during low-voltage and low-frequency events. • Review of discrepancies between DER_A ride-through/trip logic and IEEE Std. 1547-2018. • New aggregate model for distributed energy resources with ride-through capabilities. • Validation of the new ride-through logic by comparing against disaggregated models. [ABSTRACT FROM AUTHOR]

This paper first designs a holistic coordination framework between transmission system operator (TSO) and distribution system operators (DSOs) to modernize traditional top-to-down (from transmission system to distribution systems) power flexibility provision mechanism to a bi-directional power flexibility provision mechanism between TSO and DSOs. More specifically, it empowers TSO and DSOs to exchange both active and reactive power flexibility without having to reveal their confidential grids data. Above all, it allows TSO to take advantage of the potential active and reactive power flexibility of the proliferating number of distributed energy resources (DERs) installed in distribution systems. Secondly, it develops a linearized power flow model for transmission networks. Leveraging the designed framework along with the developed linearized power flow model, it finally offers a two-stage linear stochastic optimization method to help TSO optimally book its required active and reactive power flexibility from both power plants and distribution systems. In particular, it considers constraints and active/reactive power losses of the transmission network. The performance of the proposed framework is evaluated considering a real-world transmission network, i.e. the Swiss transmission network. • It designs a holistic TSO–DSO coordination framework where TSO and DSOs establish their collaboration on the basis of the FPC areas. This framework facilitates the TSO and DSO collaboration, thereby, boosting unlocking the potential active/reactive power flexibility of DERs. • It develops a decision making tool for TSOs to help them optimally book their required active and reactive power flexibility via FPC areas. Leveraging a linearized AC power flow model, it is able to account for grid's active and reactive power losses and, most notably, the nodal voltage magnitude constraints. Therefore, it can ensure that power plants accomplish their voltage regulation task against all uncertainties and contingencies without reaching their maximum/minimum reactive power limits. Accordingly, it helps TSOs steer their grids far from risks associated with under-voltage/over-voltage issues triggering voltage collapse. • It allows TSO to take advantage of all existing power flexibility resources including dispatchable power plants and DERs located in distribution networks. • It follows the sequential energy and flexibility market structure, thus, it is a tailored tool for most of European TSOs. • It evaluates the efficiency of the developed framework and decision making tool on a real-world transmission network, i.e. the Swiss transmission network operated by Swissgrid. [ABSTRACT FROM AUTHOR]

• Linear OPFs are requested for the most recent challenges of distribution systems. • Traditional linear OPFs for distribution grids do not manage closed-loops and OLTCs. • Linear OPF can be improved to manage modern distribution system challenges. • Improved OPF preserves accuracy and numerical performance of the traditional one. • Stability and scalability properties of improved linear OPF are experimented. Modern distribution systems face new challenges due to the rise of small-scale renewable energy resources and the electrification of energy demand. Additionally, the complexity of network management increases with the exchange of flexibility services between distribution and higher voltage levels. The system operator's role involves optimizing the planning and operation of the distribution network. In current scenarios, this includes managing flexibility resources (such as generators, demand response, and on-load tap changers) and implementing new operating strategies (such as closed-loop topologies and dynamic reconfiguration). This paper proposes a reformulation of one of the most used linear approximations of the Optimal Power Flow model for distribution networks. The novelty of this reformulation lies in integrating equations to handle tap-changing transformers with closed-loop/meshed network operation, while preserving the original linear formulation and computational tractability. [ABSTRACT FROM AUTHOR]

• A quasi-static time series simulation approach for transmission system expansion planning to capture off-peak issues and opportunities. • A quantitative study of the operation of a Hydro-Québec transmission system planning with a massive integration of distributed energy resources. • The level of penetration of distributed photovoltaics can create counterintuitive operational risk in the long term or lead to unoptimized system planning. • The use of resources to maintain a stable and secure transmission system is very sensitive to the penetration rate of electric vehicles and charging times. Due to advancements in solar cell fabrication and inverter-based technology, as well as decreasing acquisition costs, solar distributed generation systems have emerged as a promising renewable source. Furthermore, electrification of the transportation system is an alternative promoted by many governments to mitigate the dependency on fossil fuels and achieve the goals of decarbonizing the economy. However, interconnecting a large quantity of distributed energy resources (DERs) to the power system reveals technical problems affecting the entire system. Planning coordinators and grid operators need to understand the full-spectrum impact of DERs on the power system to ensure secure and reliable grid planning and operations. The operation of a Hydro-Québec transmission system planned for 2030 is simulated in quasi-static time series mode with a massive integration of 1 million of electric vehicles and 1000 – 3000 MW of distributed photovoltaics. This paper provided findings on assessment of high DER penetrations impacts on the transmission system in terms of voltage control, mitigation means used, MVAR availability and consumption margin, generating unit optimal operation, and switching actions of several equipment. [ABSTRACT FROM AUTHOR]

This paper uses convex inner approximations (CIA) of the AC power flow to tackle the optimization problem of quantifying a 3-phase distribution feeder's capacity to host distributed energy resources (DERs). This is often connoted hosting capacity (HC), but herein we consider separative bounds for each node on positive and negative DER injections, which ensures that injections within these nodal limits satisfy feeder voltage and current limits and across nodes sum up to the feeder HC. The methodology decomposes a 3-phase feeder into separate phases and applies CIA-based techniques to each phase. An analysis is developed to determine the technical condition under which this per-phase approach can still satisfy network constraints. New approaches are then presented that modify the per-phase optimization problems to overcome conservativeness inherent to CIA methods and increase overall HC, including selectively modifying the per-phase impedances and iteratively relaxing per-phase voltage bounds. Discussion is included on trade-offs and feasibility. To validate the methodology, simulation-based analysis is conducted with the IEEE 37-node test feeder and a real 534-node unbalanced radial distribution feeder. [ABSTRACT FROM AUTHOR]

In the context of a real electrical distribution network in Puerto Carreño, Colombia, this paper addresses the challenge of enhancing power supply reliability through the strategic integration of Renewable Energy Sources with Hydrogen Energy Systems. The planning problem is reformulated as a Mixed-Integer Quadratically Constrained Programming problem, enabling the use of efficient solvers with global optimal solution. Considering the extensive curtailment options and numerous allocation components, the optimization problem naturally scales up. To address this computational intensity, representative scenarios are derived from the original high-dimensional probability distribution employing the Gaussian Mixture Model. These scenarios are used in conjunction with the proposed optimization framework to achieve cost-effective power grids while reducing fossil fuel dependency. • MIQCP optimizes renewable and hydrogen energy components. • Reduces optimization samples using Gaussian Mixture Model. • Enhances grid stability and cost-effectiveness in remote areas. [ABSTRACT FROM AUTHOR]

Recently, online feedback optimization (OFO) emerges as a promising approach for real-time distribution grid management. OFO offers several advantages, including not requiring precise grid models or real-time load metering and demonstrating robustness against inaccurate problem data. However, one important limitation is that OFO does not consider the intertemporal relationships and short-term planning capabilities of assets, thus not harnessing the full potential of a variety of distributed energy resources (DER) such as batteries and electric vehicles. To address this limitation, this paper proposes a multi-timescale coordinated control framework. In the slower timescale, local optimization problems are solved to provide real-time OFO controllers with reference setpoints. The overall approach thereby maintains minimal model, computation, and communication requirements while enforcing grid limits. Case studies based on a 96-bus unbalanced low-voltage grid with a high DER penetration level and second-scale data demonstrate its effectiveness and solution quality benchmarked with a centralized optimal power flow approach. • Combining local optimization with online feedback optimization. • Real-time control of distributed energy resources with intertemporal relationships. • Offline optimization provides online optimization reference setpoints. • Reduced PV curtailment compared to benchmark ideal optimal power flow. • Efficient utilization of existing distribution grid capacities. [ABSTRACT FROM AUTHOR]

This paper discusses the modeling of inverters used in distributed energy resources in steady state. Modeling the interaction between distribution grids and inverter-based resources is crucial to understand the consequences for the network's operational and planning processes. This work highlights the limitations of existing models and emphasizes the need for better representations of inverters and their control laws in decision-making contexts. Improved steady-state grid-following and grid-forming inverter models are presented, including both three-leg and four-leg converter variants. The advantages of these improved models in mathematical optimization contexts are showcased by investigating the power quality improvement capabilities of the inverters. Numerical studies integrating the proposed inverter models in a four-wire unbalanced optimal power flow engine are presented, and trade-offs between modeling detail and computational intensity are illustrated. • Review of algebraic inverter models and formulation improvements. • Derive (algebraic) nonlinear programming models for both the inverters and control laws, and perform numerical experiments through IPOPT [10]. • Demonstrate the integration of these models into unbalanced optimal power flow engines, up to four-wire [11]. • Illustrate how inverter model approximations can lead to sub-par decisions. [ABSTRACT FROM AUTHOR]

With the increasing amount of Distributed Energy Resources (DERs), coordination of Distribution Grid Operators (DSOs) and Transmission Grid Operators (TSOs) is of paramount importance. Managing a large number of DERs at the TSO level is, however, challenging. To address this problem, flexibility aggregation is a topic of frequent research activities. Aggregation means to describe the combined flexibility of the DERs at the vertical grid coupling between DSO and TSO. Existing works are often limited with respect to guaranteeing feasibility, with respect to efficient numerical implementation, and in terms of quantification of the cost of DER usage. In the present paper, we investigate aggregation based on Approximate Dynamic Programming (ADP). We propose efficient numerical aggregation schemes using tools from computational geometry thus avoiding the need to solve multiple OPF problems. We rely on different variants of the DistFlow model for radial grids, which are computationally efficient. This allows to model of current and voltage limits and enables the consideration of voltage dependencies in the aggregation. Furthermore, we propose a method for cost aggregation and identify open problems of flexibility aggregation. • An Approximate Dynamic Programming based framework for flexibility aggregation. • Efficient numerical methods based on set projection. • A comparison of specialized grid models for an efficient projection. • Cost aggregation. • Consideration of voltage dependence. [ABSTRACT FROM AUTHOR]

Utilizing distribution grid flexibility for ancillary services requires the coordination and dispatch of requested active and reactive power to a large number of distributed energy resources in underlying grid layers. This paper presents an approach to hierarchically dispatch flexibility requests based on Online Feedback Optimization (OFO). We implement a framework of individual controllers coordinating actors, contributing to flexibility provision, to track a requested operating point at the interface between grid layers. The framework is evaluated in terms of performance during coordination and possible interaction between individual controllers, both central and distributed. Results show high reliability and robustness of the OFO controllers as well as an efficient dispatch of active and reactive power. Its computational efficiency and capabilities in set point tracking during online grid operation are making OFO a promising approach to the flexibility dispatch problem. • Hierarchical control of distributed flexibility with Online Feedback Optimization. • Provision of ancillary services across multiple grid layers. • Accurate set point tracking by the proposed control structure. • Robustness against model mismatch and disturbances through closed loop control. • Simulative and experimental case studies. [ABSTRACT FROM AUTHOR]

This paper proposes a cooperative approach aimed at distributed energy resources sizing in a renewable energy community, with considerations of the community's optimal operation, impact on the electrical grid and an allocation of the benefits to its members. To this purpose, multiple investment modes are evaluated via a two-step procedure. In the first step, the size of renewable energy sources is determined by solving an optimization problem that maximizes community welfare, considering network and investments. In the second step, an optimization problem maximizing additional community member profit with price regularization is solved. This step shares benefits among community members. The potential of the proposed procedure is illustrated using a benchmark Dickert-LV network. This is a full y cooperative framework where the community operator is ensuring adequate grid operation, operational planning and sizing of new investments. • Energy communities are a good enabler for energy transition and prosumer empowerment. • The incentive for DER investment that they provide needs quantification. • Intra-community exchanges and co-investments have effects on the grid. • What are the benefits of an energy communities with and without co-investments? • How could these benefits and return on investments be computed? [ABSTRACT FROM AUTHOR]

Distribution utilities need to ensure fast fault location to improve the energy supply service, given that, in the event of an outage scenario, the service restoration process starts after the fault has been localized. Minimizing the time required for fault location leads to a reduction in the system average interruption duration index (SAIDI), a crucial metric that all utilities strive to meet. The use of fault indicators can facilitate the fault location process in distribution systems. The challenge lies in determining the optimal placement of these fault indicators to facilitate the fault location process effectively. This paper presents a metaheuristic-based framework that takes into account both commercial and operational criteria to optimize the placement of fault indicators in distribution systems. This tool considers essential practical factors to assist utilities in achieving minimal SAIDI by optimizing the allocation of fault indicators. The method is tested for a 135-bus distribution feeder to verify its practicability. The numerical results demonstrate that the proposed methodology improves the expected SAIDI, leading to better energy supply service for customers. • Allocation of FIs considering load density, reliability index and priority customers factors. • The allocation of fault indicators enhances reliability indexes. • The consideration of priority customers may worsen systemic reliability. [ABSTRACT FROM AUTHOR]

Distributed Energy Resources (DERs) integration into distribution systems becomes problematic when the penetration level exceeds the system DER hosting capacity. Although several studies propose to quantify DER hosting capacity, methodologies to maximize it in distribution systems are scarce. Some approaches propose installing var compensators, incurring in costly solutions; others propose reduced cost solutions like network reconfiguration or optimized Volt–Var curve. Nevertheless, the combination of these approaches has not been proposed. Therefore, this paper proposes the simultaneous network reconfiguration and optimized setting of grid-tie inverter Volt–Var curve to maximize DER hosting capacity and minimize power losses. The uncertainties are addressed using Monte Carlo simulation, whereas Non-Dominated Sorting Genetic Algorithm II handles the multi-objective problem. The simulations are performed using Matlab and OpenDSS software, considering the IEEE 33-bus test system. Finally, the results demonstrate the superiority of the proposed approach against previous studies that performed network reconfiguration and Volt–Var optimization separately. • Network reconfiguration and Volt–Var optimization for hosting capacity maximization. • Multi-objective approach to increase DER hosting capacity and minimize power losses. • Inclusion of uncertainties related to DERs and loads in a 24-hour cycle. • Cost-effective solution by avoiding the need for additional var compensator equipment. [ABSTRACT FROM AUTHOR]

Using spatiotemporal data can increase forecasting accuracy for distributed energy resources such as wind farms, solar power plants, and electric vehicle charging stations. The underlying forecasting models assume that data are shared by the different data owners, which is not always feasible because of privacy and confidentiality constraints. Existing methods for privacy-preserving data-sharing are based mainly on linear or non-linear univariate models. This is quite limiting concerning the potential of non-linear multivariate forecasting approaches, since these models can be computationally more efficient than univariate approaches and can learn non-linear relationships. This paper introduces a new federated forecasting approach implementing a multivariate non-linear learning scheme based on decision trees while enabling fully decentralized computation. Results show higher predictive performance than existing linear or univariate energy forecasting models while preserving privacy. • New encryption for lossless and efficient federated regression trees. • We address the need for multivariate and non-linear models simultaneously. • We analyze the resilience to missing data. • The proposed method is computationally feasible for spatiotemporal applications. • Application to DER-Forecasting is more accurate than State of the Art methods. [ABSTRACT FROM AUTHOR]

For network-safe coordination of distributed energy resources (DERs), many approaches impose some form of constraint set to guarantee the safe operation of a distribution network. This paper presents a comparative analysis of two distinct approaches that leverage a constraint set for network-safe coordination: nodal operating envelopes versus network-wide constraints on the action of a DER aggregator. We investigate their respective strengths and limitations by considering information and communication requirements and trade-offs in terms of flexibility and fairness. The availability of potentially private information to the aggregator or to the distribution system operator determines which of the approaches is feasible. The results of the case studies suggest that if the goal is to maximize DER flexibility, a nodal constraint approach should be used. However, if the aggregator does not have information on how the constraints map to its DERs and/or does not have the ability to send out node-specific control inputs, the nodal constraint approach is not feasible and a network-wide constraint approach is needed. We show that a network-wide constraint approach constraining the aggregator's control input provides a good balance between flexibility and fairness. • Distributed energy resources (DERs) can cause electric grid constraint violations. • DER aggregators and distribution system operators must coordinate. • Comparison of nodal and network-wide constraints for network-safe operation. • Regulatory and communication frameworks dictate which approach is feasible. • Operational outcomes differ depending on the consideration of equity. [ABSTRACT FROM AUTHOR]

During the last few decades, extensive research has been conducted to mitigate potential voltage-related problems that high penetration of Distributed Energy Resources (DERs) may cause. One of these problems is the voltage variation caused by the disconnection of the DERs due to their anti-islanding protection action after a temporary fault. This issue has not been deeply studied so far and it may pose severe voltage variations to distribution systems' consumers. In this context, this paper proposes a new formulation aiming at the preventive voltage regulation strategy for step voltage regulators considering the impacts triggered by a sudden disconnection of DERs. The voltage control is accomplished by a specialized Particle Swarm Optimization that selects control actions predicting the possibility of the DERs being disconnected. Results show that voltage violations were minimized after the disconnection of DERs, keeping the nodal voltages within statutory limits. • Centralized tap control minimizes violations after generators are disconnected. • Specialized Particle Swarm Optimization for Step Voltage Regulators was proposed. • Coordination of Voltage Regulators with generators improves voltage regulation. • Mitigation of voltage sags caused by anti-islanding protection was achieved. • A proactive approach enhances grid resilience by disruptive events. [ABSTRACT FROM AUTHOR]

Given the increasing penetration of converter-interfaced resources in power systems, properly estimating the short-circuit current (SCC) contribution in large networks has become a growing challenge and necessity to ensure the security and stability of the systems and assets. This paper presents two novel methods to estimate the SCC contribution of type IV wind turbines at both the transient and steady-state stages of unbalanced and balanced faults: (1) a machine learning-based method trained with electromagnetic transient (EMT) simulations and capable of estimating some of the initial peak and transient current magnitudes; (2) an analytical approach to estimate the steady-state SCC based on the voltage and grid code dependency of the converter during the fault. The methods are coupled into a single framework and compared to field-validated EMT models of a real turbine. The results show that the majority of the estimated currents in the transient stages present errors below 5%. In steady-state, the errors are not greater than 1.21%. Given the complexity of the problem, these margins may be deemed acceptable for short-circuit studies. [Display omitted] • Short-Circuit Current Contribution from IBRs is becoming increasingly important. • The novel framework has been verified against field-validated EMT Models. • For the steady-state stage, the power-flow-based algorithm shows high accuracy. • Due to the complexity of the transient stage, a Machine-Learning model is used. [ABSTRACT FROM AUTHOR]

The transition from centralised, fossil fuel-powered generating units to distributed energy resources (DER) represents a significant step forward, offering numerous benefits. However, this shift also presents operational challenges for distribution network service providers (DNSPs) and the electricity markets. In this paper, we introduce our innovative solution for allocating network capacity in the form of operating envelopes, shaped to enhance customer participation in energy and reserve markets. Our approach also provides DNSPs with network support flexibility of DER which can be leveraged to either increase network throughput for market services or postpone network augmentation. This study outlines our initial findings from Project Converge 1 1 https://arena.gov.au/projects/project-converge-act-distributed-energy-resources-demonstration-pilot/. —a comprehensive real-world trial involving 1000 active customers (the largest participant cohort in Australia) located in the Australian Capital Territory. Within, we discuss the challenges we have encountered, the opportunities that have arisen, potential avenues for future expansion, and the invaluable insights gained during the pre-trial phase of the project. • Proposing shaped operating envelopes – a novel extension of dynamic operating envelopes to allocate network capacity amongst aggregators. • Enabling DER to participate in energy, FCAS and network support market while empowering DNSPs to postpone network augmentation. • Real-world implementation of our approach in a live trial in Australia with more that active 1000 customers. [ABSTRACT FROM AUTHOR]

This paper proposes an uncertainty modeling method for the aggregated power flexibility of DERs. Basically, both outer and inner approximated power-energy boundary models are utilized to describe the aggregated flexibility of controllable DERs. These power and energy boundary parameters are uncertain because the availability of controllable devices, such as electric vehicles and thermostatically controlled loads, cannot be precisely predicted. The optimal operation problem of the aggregator is thus formulated as chance-constrained programming (CCP). Then, a flexibility envelope searching algorithm based on the ALSO-X+ method is proposed to solve the CCP, the result of which is a conservative approximation of the original CCP but not as conservative as the Conditional Value-at-Risk approximation. After optimizing the aggregated power of the group of DERs, the decision at the aggregator level is disaggregated into the flexibility regions of individual DERs. Finally, the numerical test demonstrates the effectiveness and robustness of the proposed method. • Uncertainty modeling of the aggregated flexibility of DERs. • Chance constraint approximation based on the ALSO-X+ approach. • Comparative analysis of costs, risk levels, and feasibility with Conditional Value at Risk. • Comparison between the outer and inner approximated flexibility aggregation models. [ABSTRACT FROM AUTHOR]

• Exploring the benefits of the joint optimization of water and electrical energy distribution systems. • Combined optimization of small-scale water-energy nexus. • Consideration of current separate ownership and management of water and energy systems. • Decentralization of water-energy co-optimization via ADMM. • Improved robustness of ADMM for micro water-energy nexus via objective-based approach. The water-energy nexus encompasses the interdependencies between water and energy resources identifying the existing links between the production and distribution of these resources. Therefore, understanding the water-energy nexus is crucial for developing sustainable and integrated resource management approaches. This paper proposes a decentralized co-optimization model for a micro water-energy nexus system (MWEN), aiming to optimize the combined supply of both resources to end consumers. The approach respects the separate ownership and management of the water and energy sectors while bridging the gap between their optimized operations. An enhanced version of the alternating direction method of multipliers (ADMM) is proposed, the objective-based ADMM (OB-ADMM), which is able to robustly optimize each system independently towards a common objective, only sharing information about the power consumption of water management, providing privacy for each resource provider. [ABSTRACT FROM AUTHOR]

Accurate transformer-customer relationship is critical for better operation and management of low-voltage distribution system. It is of high cost to establish and check the profiles of transformer-customer relationship by manual maintenance, which is often mistakenly archived too. This paper proposes a novel automatic identification method based on a deep Gaussian mixture model with voltage data. The improved model is built with feature extraction network, clustering network and splitting and merging network, which can adjust the number of transformers adaptively and dynamically. It can also identify outliers of the data and effectively solve the uncertainty and imbalance of samples. Finally, the proposed method is tested in a practical power supply district which has 2752 customers with 9 transformers. Five different test cases are set to verify its effectiveness. The method can dynamically adjust to the actual number of transformer under different initial clustering numbers. The experimental results demonstrate that our model achieves an accuracy of up to 98.8% in profiling unlabeled users, while achieving an error correction accuracy of 99% in the case of imbalanced users in the transformer area. To further validate the superiority of our model, we conducted robustness experiments under different user scales and data loss scenarios. The results consistently outperform current state-of-the-art algorithms. • An innovative deep Gaussian mixture model is proposed. • The model consists of feature extraction, clustering, splitting and merging network. • The unlabeled substations can be automatically profiled and verified by our model. • The high accuracy transformer-customer relationship identification is achieved. • The problem of sample imbalance can be solved by our model. [ABSTRACT FROM AUTHOR]

Resilience evaluation is a crucial aspect of operation and planning in electrical distribution systems (EDS). Resilience refers to the system's ability to effectively respond to and recover from highly impactful, low-probability (HILP) events. Recent advancements in renewable energy technologies have paved the way to build and significantly enhance the resilience of EDS under extreme events. This paper introduces the percolation threshold (PT) as a quantifiable measure for analyzing the operational and planning resilience of the system. PT helps in measuring the resilience of EDS in an instantaneous time-based environment with consideration of different fault conditions. This study compares PT values at various nodes of the distribution system with and without extreme events. The fault time and restoration time of the system are analyzed for various events. To evaluate the performance of PT, it is compared with other electrical-based resilience measures. The analysis is extended to a larger system considering longer time frames and integration of distributed energy resources (DER). Additionally, we employ a machine learning-based feature importance algorithm to identify the important nodes of the EDS that significantly characterize the system's resilience. Furthermore, we use Gaussian Process Regression (GPR) for the prediction of PT values, offering a proactive approach to understanding and managing system resilience. The developed framework provides a valuable method for analyzing and enhancing the resilience of EDS that enables utilities and system operators to make informed decisions and implement robust strategies to ensure resilient power system operation. • A quantifiable measure for resilience in an instantaneous time-based environment. • The proposed resilience approach is compared with electrical resilience measures. • The resilience evaluation is extended to a larger system for a longer time frame. • Identification of important key nodes responsible for overall system resilience. • Gaussian process regression is used to predict the percolation threshold values. [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]

Although several approaches have been proposed in the literature to utilize inverter-based distributed energy resources (DERs) in the secondary frequency control (SFC), challenges related to practically implementing SFC with inverter-based DERs have not been considered. These challenges include response time of DERs, the communication environment, and emulation of the actual Automatic Generation Control (AGC). This paper develops and implements a coordinated operation and control for below the substation resources such as inverter-based DERs and a utility-scale battery energy storage system (USBESS) to participate in the secondary frequency control. A linearized optimization problem is formulated to simultaneously determine optimal setpoints of participating resources and satisfy AGC requests in real time for the secondary frequency control. To mimic the actual process of AGC, a hardware- and software-in-the-loop-based experimental setup is developed with the integration of real-time digital simulators (RTDS), a centralized controller at the substation, and gateways as IoT devices, which are commonly used in practice to interface DERs to control and monitoring systems. The controller remains in listening mode until it receives an AGC signal. When it receives an AGC signal, it optimally dispatches the USBESS and DERs and monitors the change in power flow at the substation. The controller iteratively dispatches the participating resources considering their response time until the AGC request is satisfied. The proposed control is implemented on an actual feeder configuration located in Las Vegas, Nevada, in collaboration with NV Energy—the largest public utility in Nevada. The results illustrate that USBESS and DERs can be coordinated and dispatched to satisfy AGC requests for SFC. • Novel linearized optimization-based secondary frequency controller • High fidelity prototype for practical implementation of AGC on real system • Combined hardware-and software-in-the-loop-based real-time laboratory setup for AGC [ABSTRACT FROM AUTHOR]

Highlights • Control of parallel-connected voltage source converters. • Voltage control loop affects synchronous reference frame inverter output impedance. • Mathematical modeling of error in power sharing between parallel inverters. • Designing dq virtual resistive, inductive, and capacitive impedances. • Experiments validate resistive, inductive, and capacitive droop control in dq-RF. Abstract This paper investigates dq -reference-frame (dq -RF) self and cross-coupling output impedances of the islanded voltage source converter (VSC), by considering the controller impacts. The dq -RF impedances for the closed-loop system are designed to exhibit the desired resistive, inductive, or capacitive characteristics, and are employed in the power control of VSC. The effects of these impedances and connecting lines impedances on the power control of islanded parallel connected VSCs, for different impedance characteristics, are also studied. It is shown in this paper that the unbalanced impedance significantly affects the performance and power control of these parallel converters. Besides, virtual impedance based active and reactive power control of the parallel converters in the dq -RF, for resistive, inductive, and capacitive cases, are designed and implemented. The numerical and experimental results verify the analysis presented in this paper about the dq -output impedance and the virtual impedance based power sharing control. [ABSTRACT FROM AUTHOR]

• This paper shows how artificial neural networks can be used to determine overvoltages in power system. • The primary contribution of this paper is a novel method based on artificial neural networks for classification of overvoltages in power systems. • This approach consists of a few steps, as follows: • Modelling of power system components for transient studies in EMTP. This means developing two different models, one for switching overvoltages, and the other one for fast front overvoltages. • Creating and deploying JavaScript to automate the simulation process and parametric analysis. • Overvoltage feature extraction from generated overvoltage waveshape using harmonic analysis in MATLAB. • Overvoltage classification using neural network in MATLAB. Numerous factors, including sudden load reductions, switching transient loads, lightning strikes, and malfunctions of control devices, can result in overvoltage. Overvoltage can harm associated power supply components and result in insulation failure, electronic component damage, flashovers, etc. A machine learning technique called a "neural network" estimates computation results that depend on a lot of inputs. For a variety of reasons, neural networks have recently been used to manage and optimize the power system. This paper presents an artificial neural network (ANN)-based approach to determining overvoltages in power systems. To simulate overvoltages, many simulations were performed in Electromagnetic Transient Program (EMTP). Variations of parameters of interest that have an influence on overvoltages were made using JavaScript that was connected to EMTP models. The extraction of characteristic parameters from overvoltage waveshape is a demanding task, and it was conducted in MATLAB, as was a overvoltage classification methodology based on neural networks. Results were presented and discussed. [ABSTRACT FROM AUTHOR]

Passive distribution networks are rapidly transforming to active networks due to ever growing penetration of mainly renewable distributed energy resources. The integration of these resources has positive impact from environmental perspective but their penetration has raised serious issues on secure and reliable operation of a network, owing to their intermittent and not dispatchable output. To maintain an optimal operation of a network, extensive research studies have been carried out under the theme of network management. This paper discusses the concept of losses management, which falls under the same theme, in active distribution networks. The methods related to modelling of such systems and end-users, numerical tools required for system analysis, active network management schemes for losses minimization and fair losses allocation to passive and active consumers are reported and investigated. Therefore, the aim of this paper is to provide a concise yet comprehensive comparison of the most recently proposed losses management approaches and strategies. [ABSTRACT FROM AUTHOR]

This paper proposes a frequency-adaptable method for computing the phase associated with the positive sequence components from the measured grid voltages. It relies on a simple procedure in which the positive sequence is separated from the negative one through a suitable decomposition of the synchronous voltage vector onto the stationary αβ frame. The procedure performance is enhanced by means of an one-cycle Fourier filter. The positive sequence voltages are then handled by a synchronous-reference frame phase-locked loop (SRF-PLL) algorithm. The structure composed of the positive-sequence estimator and the SRF-PLL is called a one cycle Fourier PLL (OCF-PLL). The OCF-PLL is robust against voltage harmonic distortions due to the OCF frequency-response characteristic. The frequency adaptability is obtained through the series connection of two OCF-PLL blocks. In the first block the fundamental frequency is computed by the SRF-PLL. This frequency is supplied to the second OCF-PLL block which computes the positive phase of the original measured voltages. Also, in this paper, the frequency adaptability is mathematically demonstrated. The method is initially evaluated by means of simulated voltages. Then, an experimental setup has been designed, in which the proposed method has been implemented in a digital signal processor (DSP) and tested on voltages produced by a programmable power supply able to emulate distorted and unbalanced voltages. The results prove the technique efficacy. [ABSTRACT FROM AUTHOR]

From the perspective of power balancing, we consider in this paper the timescale of real-time dispatch in power systems with high penetration of intermittent power sources. Due to the integration of intermittent power sources, real-time dispatch must be employed in the process of power balancing. The hierarchy of power system operation is typically comprised of the short-term scheduling, the real-time dispatch, and the automatic generation control (AGC). Therein, the AGC is the last-level defense of the systemwide power balancing, which has limited power adjustment capability. Consequently, the real-time dispatch must eliminate as much power imbalance as possible, and its capability of doing so depends on its timescale. Based on the statistical forecast uncertainty functions, a quantified relation between the uncertainty level of the power system and the critical timescale for real-time dispatch is established in this paper. Simulations demonstrate the effectiveness of the proposed formula. [ABSTRACT FROM AUTHOR]

Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermittent characteristics of the distributed energy resources (DERs) generation and load variations. Moreover, the unknown behavior of cyber attacks, especially false data injection attacks (FDIAs) in the distribution grids with complex temporal correlations and the limited amount of labeled data increases the vulnerability of the grids and imposes a high risk in the secure and reliable operation of the grids. To address these challenges, this paper proposes an unsupervised adversarial autoencoder (AAE) model to detect FDIAs in unbalanced power distribution grids integrated with DERs, i.e., PV systems and wind generation. The proposed method utilizes long short-term memory (LSTM) in the structure of the autoencoder to capture the temporal dependencies in the time-series measurements and leverages the power of generative adversarial networks (GANs) for better reconstruction of the input data. The advantage of the proposed data-driven model is that it can detect anomalous points for the system operation without reliance on abstract models or mathematical representations and data labels. To evaluate the efficacy of the approach, it is tested on IEEE 13-bus and 123-bus systems with historical meteorological data (wind speed, ambient temperature, and solar irradiance) as well as historical real-world load data under three types of data falsification functions. The comparison of the detection results of the proposed model with other data-driven methods verifies its superior performance in detecting cyber attacks in unbalanced power distribution grids. • An unsupervised neural network model combines the GANs and autoencoder for FDI attacks. • The proposed model utilizes the outputs of autoencoder and critic score for attack detection. • The proposed model is tested on unbalanced power distribution grids integrated with DERs. • Simulation results verify the model's superior performance compared to other data-driven models. [ABSTRACT FROM AUTHOR]

Stochastic fluctuations in power injections from distributed energy resources (DERs) combined with load variability can cause constraint violations (e.g., exceeded voltage limits) in electric distribution systems. To monitor grid operations, sensors are placed to measure important quantities such as the voltage magnitudes. In this paper, we consider a sensor placement problem which seeks to identify locations for installing sensors that can capture all possible violations of voltage magnitude limits. We formulate a bilevel optimization problem that minimizes the number of sensors and avoids false sensor alarms in the upper level while ensuring detection of any voltage violations in the lower level. This problem is challenging due to the nonlinearity of the power flow equations and the presence of binary variables. Accordingly, we employ recently developed conservative linear approximations of the power flow equations that overestimate or underestimate the voltage magnitudes. By replacing the nonlinear power flow equations with conservative linear approximations, we can ensure that the resulting sensor locations and thresholds are sufficient to identify any constraint violations. Additionally, we apply various problem reformulations to significantly improve computational tractability while simultaneously ensuring an appropriate placement of sensors. Lastly, we improve the quality of the results via an approximate gradient descent method that adjusts the sensor thresholds. We demonstrate the effectiveness of our proposed method for several test cases, including a system with multiple switching configurations. • Bilevel optimization minimizes number of sensors capturing all voltage violations. • Conservative linear approximations tackle power flow nonlinearities. • MILP reformulation proves tractable across various test cases. • Post-processing minimizes false alarms, validated through testing. [ABSTRACT FROM AUTHOR]

• The output power volatility of wind-photovoltaic and the operating characteristics of the electrolyzer is considered. • A capacity optimization method for off-grid RES-H2 production systems considering the load-follow-source mechanism is proposed. • A multi-objective optimization model with minimum cost, minimum energy storage capacity and optimal power supply efficiency was constructed. • An improved optimization algorithm based on two common algorithms is designed. Based on the uncertainty of the output of renewable energy such as wind and photovoltaic (PV), a capacity optimization allocation method considering the load-follow-source mechanism is proposed to solve the shortages of traditional methods, which is low in utilization rate of energy and low in economy. Firstly, considering the fluctuations of renewable energy sources (RES) and the operating characteristics of the electrolyzer, this paper proposes a capacity optimization method for off-grid hydrogen(H2) production system of renewable energy sources considering the load-follow-source operation mechanism. Then, combined with this mechanism, a capacity optimization allocation model is established, the objectives contain minimizing the levelized cost of hydrogen (LCOH), minimizing the capacity of energy storage, and optimizing the efficiency of power supply. Finally, an adaptive non-dominated sorting differential evolution algorithm (ANSDE) with external archiving is proposed to solve the established model. The calculation results show that the load-follow-source mechanism is effective for improving the economy of the system, and the proposed algorithm is high in efficiency and good in convergence comparing with traditional algorithms. [ABSTRACT FROM AUTHOR]

• The proposed data-driven control method eliminates the need for an accurate offline system model. • The presented control method incorporates a regularization term and introduces slack variables to effectively handle uncertainties and disturbances. • The FADMM method is utilized to enhance privacy and scalability by decomposing the centralized optimization problem into several sub-problems. The networked microgrid system (NMG) has become an effective alternative for integrating various types of distributed energy resources (DERs). However, because of the increasing share of DERs and high R/X ratio in the system, the uncertainty and variability of DERs' power generation may cause significant voltage violations in the system. To address these issues, this paper introduces a novel distributed data-driven voltage control strategy based on data-enabled predictive control (DeePC) to address voltage violations problems in the NMG. The DeePC algorithm uses only historical system trajectories, eliminating the need for parameter identification procedures of power system. Based on the system data model, the proposed controller coordinates MGs in the NMG in a distributed manner by employing a consensus version of the fast alternating direction method of the multiplier algorithm (FADMM), which achieves a better privacy and scalability. Simulation studies in a modified two-MG NMG demonstrate the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Utilizing Distributed Generators (DGs) and Energy Storage Systems (ESSs) enhances power system reliability, drawing significant research attention. However, these systems pose challenges, compelling scientists to explore optimization methods. Our paper presents an innovative solution, Parallel Multi-Objective Multi-Verse Optimization (PMOMVO), aimed at optimizing DGs and Battery Energy Storage Systems (BESSs) allocation. This optimization addresses voltage violations and operation costs, crucial concerns for system operators and consumers. By leveraging a parallel approach, PMOMVO significantly accelerates the optimization process. We compared its results with a base case scenario, demonstrating the superior efficiency of our parallel method. It not only enhances the optimization performance but also proves its efficacy by generating optimal solutions from the Pareto front set. This research showcases the benefits of PMOMVO, offering a faster, more efficient, and reliable way to optimize power systems, benefiting both operators and consumers. • Introduces a novel bi-level optimization approach for DG and BESS allocation and operation. • Proposing a multi-objective formulation to identify Pareto optimal solutions, offering a balance between voltage stability and cost-effectiveness. • Optimizes asset operation strategies, considering both peak and off-peak load times, enhancing resource utilization. • Proposes a parallel processing method based on modified MOMVO, significantly accelerating computations for large distribution systems problems. • Achieves a substantial improvement in the speed-up index, ensuring swift and effective DG and BESS allocation and operation in complex systems. [ABSTRACT FROM AUTHOR]

• Novel Methodology : Introduces a new double-ended terminal method for locating vegetation high impedance faults (VHIFs) in power distribution systems using the third harmonic component. • Efficiency and Cost-Effectiveness : This technique requires only two measurement terminals, significantly reducing the need and expense of extensive sensor deployment. • Overcomes Traditional Limitations : Unlike traditional methods, it eliminates the need for complex mathematical formulation models, improving accuracy in fault localization. • Demonstrated Accuracy : Simulation tests validate the method's effectiveness, achieving error rates below 1.6 %, highlighting its potential for real-world applications. • Practical Implications : Offers a proactive approach to power system protection, contributing to improved operation and maintenance of power distribution networks. Vegetation-related high-impedance faults (VHIFs) on overhead distribution systems can cause damage, maintenance issues, and even wildfires, impacting both utilities and ecosystems. Existing fault location techniques struggle to accurately locate VHIFs. In this paper, we propose a novel double-ended terminal location method based on the third harmonic component of the fault system. The proposed technique determines the distance of the VHIF by approximating the third harmonic voltage at the fault local, utilizing measurements from the remote terminal located at the far-end of the feeder. This method efficiently locates VHIFs with just two measurement terminals, cutting the need and cost of extensive sensor deployment. Moreover, the proposed method overcomes the limitations of traditional fault location techniques by using a third harmonic model of the faulted system, eliminating the need for complex mathematical models to approximate the fault behavior and voltage at the fault local. Simulation tests were conducted to validate the proposed method, demonstrating its effectiveness in accurately locating VHIFs. The technique achieved error rates below 1.6 %, making it a promising solution for real-world applications in power distribution systems. The ability to precisely locate VHIFs meets a proactive protection philosophy and contributes to improved power system protection and ensures uninterrupted power supply. [ABSTRACT FROM AUTHOR]

• Locational nodal pricing for distribution system based on DER value. • DER locational value can be determined to mitigate voltage and capacity violations. • Novel linear approach to determine DER value in distribution systems is introduced. • DER value in terms of dollars per kilowatt can provide locational value. • Scores for critical nodes emphasize those with the greatest value. Under most NEM policies, DER customers sell excess electricity at full retail price. Although this structure helped the widespread growth of DER, it is argued to subsidize DER at the expense of non-DER customers. In that regard, value-based compensation emerged, and as of 2022, at least 12 states have conducted studies to investigate alternative NEM compensation methods to inform new policy decisions. DER can provide various benefits to the distribution system, including energy, reliability, resilience, environmental, societal, and investment deferral. Each benefit has an individual value, and calculating these values is challenging. This paper presents a practical methodology to calculate the DER location value. The proposed approach utilizes established utility planning and reliability criteria. It analyzes the distribution system's current status, capturing any planning or reliability criteria violations, and determines the utility upgrade investment costs to remediate them. The proposed approach then determines the size, locational, and temporal profile of the DER that can be added to the distribution network and defer any of the utility upgrade investment costs, thus obtaining the value to the system and assigning a monetary value. The results provide a nodal distribution value of DER to the distribution system. [ABSTRACT FROM AUTHOR]

• A three-stage RCM framework is proposed for power distribution systems. • Critical component identification is carried out using BWM and TOPSIS. • The proposed ranking is benchmarked with a state-of-the-art SECA approach. • The optimal maintenance strategy is determined to minimize maintenance costs. • Cold-line, hot-line, and run-to-failure are considered for strategy determination. In this paper, a three-stage reliability-centered maintenance (RCM) framework for critical feeder identification, failure modes prioritization, and optimal maintenance strategy assignment is presented for the power distribution system. This three-stage methodology addresses a notable gap in the literature by incorporating these three stages together. The first stage ranks critical feeders using the BWM and TOPSIS methods. In the second stage, the failure modes are prioritized using feeder rank, severity, occurrence, and detection factors and are assigned a score representing their priority. Finally, in the third stage, a maintenance strategy assignment, formulated as a mixed-integer linear programming (MILP) optimization problem is proposed. The optimization problem considers three strategies, cold-line, hot-line, and run-to-failure (RTF), assigned to each failure mode. Besides, the possibility of implementing cold-line repairs overlapping with the upstream grid's scheduled outages and the availability of several maintenance crews are taken into account. The objective function is to minimize the total maintenance costs, which include operation, equipment, energy not supplied (ENS), expected energy not supplied (EENS), and future corrective maintenance costs. The proposed approach is implemented on a real distribution system. The results indicate that the proposed RCM framework leads to a 26.32 % reduction in the EENS compared to selecting the RTF strategy for all components. Besides, the proposed optimization-based method leads to a 29 % lower objective function than the business-as-usual maintenance planning, and the proposed three-stage RCM framework outperforms a previously published state-of-the-art method by decreasing the objective function and the EENS. [ABSTRACT FROM AUTHOR]

To improve the power supply availability in an island microgrid, this paper proposes a new approach that integrates distributed energy sources economically, reliably and efficiently. In an island mode, a microgrid must ensure its self-sufficiency of energy production since it cannot make an energetic exchange with a main grid. However, in this mode, the random behavior of the resources affected by meteorological factors presents a major constraint. The challenge related to the power availability in microgrids is to find a solution that faces the operation of intermittent power sources. The microgrid should guarantee a useful power management in order to achieve a high availability of energy. In this paper, we present a mathematical model to describe the influence of the meteorological factors on the sources production. We propose a multi-agent control strategy based on the production forecasting and load shedding for a high availability of the microgrid power supply. The proposed multi-agent system uses the master-slave model in which the communication and negotiation between the defined agents are performed by a concept of tokens. The developed control system is implemented on Spartan 6 FPGA-Board. The paper's contribution is applied to a Tunisian petroleum platform where several blackouts are recorded between 2012 and 2014. Simulation and experimental results show clearly a high availability as a performance of the proposed control strategy. [ABSTRACT FROM AUTHOR]