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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]

• Overview of energy interconnection system. • Review of distributed energy control structures. • The objective functions and constraints in optimization of distributed energy system. • Comprehensive review of energy control method and optimization method. • Unresolved issues in energy interconnection system. The objective of this paper is to offer theoretical references and practical guidelines for the effective management and optimization of distributed energy systems. This paper presents a comprehensive analysis of modeling and optimization methods for energy systems. Primarily, it presents a comprehensive overview of three predominant energy system frameworks, namely centralized, distributed, and local control frameworks, highlighting their distinctive characteristics. It provides a robust platform for fostering innovation and facilitating the transformative evolution of energy infrastructure. Subsequently, the paper outlines the optimization objectives and corresponding constraints for energy control systems. It is essential for building an efficient, economical, stable and sustainable energy supply. In addition, it summarizes the prevalent theories and algorithms in the field of energy control and optimization while providing a detailed analysis of their advantages, disadvantages, and relevant performance indicators. [ABSTRACT FROM AUTHOR]

The growing concern regarding climate change has prompted system planners and operators to prioritize transitioning towards zero-carbon emission policies, a crucial issue for a sustainable and eco-friendly society. This necessitates a reduced reliance on the utility grid, which is predominantly powered by fossil fuels. In this regard, the rapid penetration of renewable-based distributed energy resources and battery storage (BS) devices into distribution networks underscores the critical significance of optimizing the efficient utilization of these resources. Peer-to-peer (P2P) energy trading is introduced as a promising innovation, enabling these resources to actively manage their scheduling in the local market environment. This paper proposes a novel P2P energy trading model where agents, e.g., BSs, engage in direct negotiations with each other and the upstream grid simultaneously. To this end, an efficient peer-matching process based on the primal–dual gradient method is proposed to clear the local market without the intervention of any central coordinator, while preserving their privacy and considering their individual preferences. Furthermore, to incorporate the constraints associated with the exchanged power with the upstream grid and carbon emissions in the proposed framework, two separate transactive control signals are defined. Finally, the proposed model is applied to a case study, and the simulation results demonstrate its effectiveness and applicability. • This study proposes an efficient decentralized framework for P2P and P2G energy trading that allows market players to directly negotiate with each other and the upstream grid, incorporating product differentiation to reflect the customers' preferences. • By appropriate mathematical modeling of prosumer agents, such as BSs, and assigning them roles as either seller or buyer agents in each iteration and time interval, this paper effectively models energy storage devices within the fully decentralized energy trading scheme. • In addition to the allowable power, where buyers and prosumers can purchase electricity from the upstream grid based on their carbon emission permission, we propose a supplementary carbon emission permission that enables buyers to purchase non-allowable power from the upstream grid through penalty payments. • The constraints associated with the upstream line flow and the daily carbon emissions are integrated into the presented peer-matching process through the definition of corresponding TCSs. [ABSTRACT FROM AUTHOR]

Harmonic power flow simulation is the main method to deduce and evaluate the steady-state harmonic content of urban rail flexible traction power supply system (FTPSS). The AC power quality of FTPSS can be optimized by conducting harmonic power flow simulation. However, traditional modeling and simulation methods are inefficient, so that it is difficult to achieve the real-time and accurate deduction of the harmonic power flow. Therefore, a high precision real-time simulation method of harmonic power flow based on multi-layer perceptron (MLP) is proposed in this paper. Firstly, the harmonic model of fully controlled converter is established by adopting MLP, which reduces the complexity of the model and improves the efficiency of harmonic calculation. Subsequently, the high precision data samples are used for the regression training of MLP model to ensure the accuracy of harmonic calculation. Moreover, the high precision real-time simulation of harmonic power flow is realized through the synchronous and interactive solution of MLP model and power flow model. Finally, theoretical analysis and simulation are conducted. The results demonstrate that the steady-state harmonic calculation time of the MLP model is significantly reduced to merely 1.02% of the traditional mechanism model's duration, which enables the harmonic power flow simulation to be effectively integrated into real-time system applications. In addition, the RMSE of the MLP model is only 0.0147, indicating the accuracy of the real-time simulation. The method proposed in this paper can quickly and precisely calculate the harmonic power flow, which is of practical value for harmonic evaluation and optimization of FTPSS. [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]

• 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]

• 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]

• A mechanism for IECs to bid in real-time demand response markets is proposed. • An "IEC-main grid" hierarchical regulation model is established. • Deep Deterministic Policy Gradient solution algorithm is improved with MP and PER. Integrated Energy Cluster (IEC), the regional aggregation of integrated energy systems (IES), has accumulated plenty of dispatchable resources with the development of energy market. This, while significantly providing system Demand Response (DR) potential, also complicates the interaction of the IEC with the main grid and increases the difficulty of system scheduling. To address this issue, this paper proposes a Reinforcement Learning-driven multi-agent hierarchical regulation framework that makes full use of DR to maximize the benefits of both IEC and main grid. Firstly, in the context of the DR market, a mechanism for IECs to bid in the real-time DR market is proposed. Furthermore, an "IEC-main network" hierarchical regulation model taking account of DR is established to minimize the IEC operation cost and maximize the societal benefit. Moreover, an optimization algorithm utilizing Deep Deterministic Policy Gradient (DDPG) with Multi-process (MP) and Priority Experience Replay (PER) mechanism is proposed to allow adaptability to high-latitude and large-scale applications. In case study, the proposed model and algorithm is tested on an 8-node system and a 24-node system. The result indicates that the hierarchical regulation model considering DR can improve the system economy by 2.59 % than that without DR and the improved DDPG algorithm can enhance training effectiveness in comparison with DDPG and PER-DDPG. [ABSTRACT FROM AUTHOR]

Service restoration (SR) consists of automatically generating and executing a plan to restore the service in healthy zones using the least number of maneuvers after detecting and isolating a permanent fault in the distribution system zone. This component is essential to self-healing functionality in smart grids and allows customers to reconnect quickly to the distribution grid after a power outage. Distributed generation (DG) supports the distribution network when there is insufficient capacity to restore all zones out of service or supply the loads locally through microgrids. The power supply must be restored to the highest priority customers in case of partial restoration. Also, most research works use simplified or linearized models to propose restoration algorithms. This paper proposes a complete AC formulation for the service restoration problem in distribution systems considering network reconfiguration (NR), the integration of distributed generation (DG), and priority customers (PCs) into the solution. The optimization problem is solved by a centralized algorithm based on combining the Differential Evolution (DE) and Continuous Population-Based Incremental Learning (PBILc) metaheuristics techniques. Simulation results are presented for three case studies in which the IEEE 33-bus distribution system is tested for different fault scenarios. The numerical results show the robustness and efficiency of the proposed algorithm. • Full AC Non-Convex Modeling. • Differential Evolution and Continuous Population-Based Incremental Learning. • Network reconfiguration considering priority customers and microgrids. [ABSTRACT FROM AUTHOR]

The way to produce and use energy is undergoing deep changes with the fast-pace introduction of renewables and the electrification of transportation and heating systems. As a consequence, the electrical grid sees much higher power variability than in the past, challenging its frequency and voltage regulation. Energy storage systems will be fundamental for ensuring the energy supply and the voltage power quality to customers. This survey paper offers an overview on potential energy storage solutions for addressing grid challenges following a "system-component-system" approach. Starting from system challenges, the energy storage technologies and their power electronics integration in the grid are described at component level considering the last scientific trends, including the hybrid energy storage concept. The impact of the energy storage technologies on the power systems are then described by exemplary large-scale projects and realistic laboratory assessment with Power Hardware In the Loop techniques, returning at system level. Finally, this work addresses some of the most important challenges for a sustainable and safe integration of energy storage systems, such as the circular economy and the safety aspects. [ABSTRACT FROM AUTHOR]

Distribution Level Flexibility Markets (DLFMs) constitute a timely development towards facilitating bottom-up RES and flexibility investments as well as instantiating a fair and regulated interaction framework among energy sector stakeholders, namely System Operators (DSO, TSO), aggregators, Flexibility Service Providers (FSPs), the recently introduced actor called Flexibility Market Operator (FMO) that operates the DLFM and the end energy prosumers. Moreover, the interaction of DLFMs with existing Transmission Level markets and with distributed energy resources is of special interest. In this paper, we present and analyze three possible ways to integrate DLFMs in today's regulatory framework, each corresponding to a different market architecture, and we evaluate their implications. A comparative analysis reveals the strengths and drawbacks of each one of them both from a system's as well as from each individual stakeholder's perspective. The effects of different distributed RES and flexibility penetration levels are also analyzed through a sensitivity analysis. The comparison of the proposed DLFM architectures, both with each other and the current electricity market paradigm, testifies the necessity and the importance of DLFMs and quantifies the impact that they have in: (i) reducing distribution grid management costs, (ii) guaranteeing distribution service quality, and (iii) increasing the penetration of bottom up RES/flexibility investments. • Coordination schemes between TSO and DSO are needed for high RES penetration. • Interactive, reactive and proactive flexibility market schemes are proposed. • Flexibility markets can accommodate the desired high RES penetration rates. • Flexibility markets can have a key role in minimizing overall cost. [ABSTRACT FROM AUTHOR]

To ensure the continuous operation of a microgrid, proactive planning is essential, especially when contemplating possible dynamic events that alter the scheduled scenarios for the islanded operation or during its transition from connected to islanded mode. This paper introduces an innovative mathematical programming model for the AC optimal power flow (AC-OPF) with dynamic security constraints (DSCs), considering two scenarios: the islanded operation and the transition. This model uses positive-sequence equations to represent the inverter-based resources (IBRs) for grid-forming and grid-following roles, along with a fourth-order synchronous generator model equipped with excitation and frequency control systems. They assess the dynamic response to specific events such as short-circuits, decreases in PV generation, increases in load demand during the islanded operation, and the transition itself. The DSCs are applied to dispatchable distributed energy resources (DERs), which react to variations in the microgrid, considering generation and opportunity costs to minimize the discrepancy between planned and secure operating points. The mathematical programming model is implemented using AMPL, and solutions are obtained through the nonlinear optimization solver IPOPT. The tests are conducted in an adapted version of the microgrid being developed at the University of Campinas that includes a synchronous generator, photovoltaic (PV) generation, and a battery energy storage system (BESS). Results demonstrate the model's effectiveness in adjusting generation dispatch to withstand defined events and optimizing generation resources, even when limits are not reached. [ABSTRACT FROM AUTHOR]

• The paper presents a novel dual control strategy combining Adaptive Model Predictive Direct Power Control (AMP-DPC) for the grid-side converter (GSC) and Adaptive Direct Power Control (ADPC) for the Dual Active Bridge (DAB) converter. • The proposed control strategy significantly improves power quality, ensuring a near-unity power factor (0.9897 during charging and 0.9898 during discharging) and reducing Total Harmonic Distortion (THD) from 29.301 % to 2.008 %. • Comparative analysis with conventional VDPC strategy shows that the proposed ADPC strategy converges faster (8 ms vs. 12 ms for load decrease and 7 ms vs. 10 ms for load increase) with lower voltage peaks and dips, demonstrating its superior stability and efficiency. • The control strategy effectively manages various operating modes, including Grid-to-Vehicle (G2V), Vehicle-to-Grid (V2G), and Vehicle-to-Load (V2L), ensuring continuous power exchange and maintaining uninterrupted power supply (UPS) to connected loads. • The effectiveness of the proposed control strategy is validated through experimental testing on a 0.5 kW off-board EVC prototype, demonstrating practical feasibility and robustness in various conditions. Electric vehicle (EV) chargers face significant challenges in maintaining grid power quality (PQ) and ensuring efficient power management during grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations. Additionally, they must seamlessly switch to vehicle-to-load (V2L) mode during grid disturbances to provide uninterrupted power supply (UPS) for domestic loads. This article explores a dual control approach incorporating adaptive model predictive direct power control (AMP-DPC) on the rectifier side and adaptive direct power control (ADPC) on the dual active bridge (DAB) side to address these challenges. The AMP-DPC employs a control strategy referred to the second-order generalized integrator (SOGI) which estimate synchronizing voltage templates specifically designed for single-phase systems. The proposed optimization control aims to mitigate the cost function value by selecting appropriate switching modes. The optimized function is independent of the weighting factor, expressing the optimal modulation function across various weighting factors without additional design or selection. The current reference used by the charger is designed to ensure that the power factor remains at closer unity throughout G2V and V2G modes. The proposed control algorithm ensures the grid current remains low in harmonics during G2V, V2G, and V2L modes of operation. The experimental validation of the proposed control strategy is performed in the laboratory on a 0.5 kW off-board electric vehicle charger (EVC) prototype under various conditions to demonstrate its effectiveness in compliance with the IEEE 519–2022 standard. [ABSTRACT FROM AUTHOR]

Active distribution networks integrate distributed energy resources into traditional power grids, mainly guided by the intention of addressing climate change concerns. However, integrating distributed energy resources has introduced several challenges for traditional overcurrent protection, including variations in pre-fault and fault current magnitudes and bidirectional power flows. These challenges make it difficult to guarantee reliable protection of active networks in grid-connected and islanded modes. In response to the challenges posed by these new networks, this paper presents an adaptive protection approach that leverages local measurements and data clustering to identify the network operating states accurately. In this approach, reference relay coordination is obtained by considering all distributed energy resources that supply the network during the grid-connected operating state, providing optimal settings for each relay. Next, based on the current operating state, the adaptive relay pickup current is estimated based on local measurements, which is used to adjust the time dial setting of each directional overcurrent relay. The proposed approach is contrasted with the conventional relay, considering several operating states, including DER switching, grid-connected, and islanded modes, for single-phase and three-phase faults and several fault resistances, using the IEEE 34-node test feeder. The results demonstrate the proposal's high performance in avoiding relay miscoordination. Finally, the proposed approach is also simple and suitable for immediate implementation in existing digital relays, as it has a low computation burden and does not require communication systems. • Proposes an approach for communication-less adaptive protection to update the relay settings adequately. • The approach detects the ADN OS originated by the connection/disconnection of DERs, starting from a reference case and using clustering and classification approaches. • The relay parameters are online estimated from the local pre-fault current and voltage measurements. • The proposed solution does not use a centralised computation scheme. • The protection approach uses straightforward computation, resulting in a low computational burden. [ABSTRACT FROM AUTHOR]

• An improved double loop control for the traction substation's voltage source converter (VSC) is proposed based on active disturbance rejection control (ADRC) in the dc traction power supply system (TPSS). • The impact of the dynamic interaction among the source-network-load units on the system stability is analyzed using the modal analysis. The participation extent of each unit's state variables in the system's oscillation modes is evaluated quantitatively. • The improved ADRC control can attenuate the strong interaction among the source-network-load units and enhance the stability of the dc TPSS. • The proposed ADRC control can improve the system's disturbance rejection capability and maintain voltage stability under the varying disturbances in the dc TPSS. • Simulations verify the effectiveness of the proposed control strategy under different TPSS's operating conditions. The medium voltage dc traction power supply system (TPSS) has become a new trend in the development of high-speed railroad power supply system. In this system, the traction substation (TSS), traction network and train form the nonlinear dynamic interaction through the converters. Meanwhile, the system uncertainties are aggravated by the change of the train's operating conditions and positions. This makes the control of the voltage source converter (VSC) at the TSS quite challenging. In order to reduce the impact of the system parameter disturbances, model uncertainties and other factors on the dynamic performance of the TSS's VSC, this paper proposes an improved control system for the VSC double loop at the TSS based on active disturbance rejection theory. The train's operating conditions and positions are taken as disturbances of the VSC's double loop, and the extended state observer is established to estimate the disturbances in real time and make feedforward compensation. Since the interaction among the multiple interconnected units will change the system damping and the dynamic characteristics, it has significant effects on the system stability. Based on the modular modeling method, the source-network-load state space model of the dc traction system is established. The modal analysis method is used to evaluate the participation extent of each unit's state variables in the system's oscillation modes, and the impact of the dynamic interaction among the source-network-load on the system stability is analyzed. Finally, it is verified that the proposed double loop control method based on active disturbance rejection theory can enhance the controller's disturbance rejection capability and dynamic performance by establishing the simulation model in the PSCAD/EMTDC. When applied to the dc TPSS, the proposed control method can attenuate the strong interaction among the source-network-load units and enhance the stability of the system voltage. [ABSTRACT FROM AUTHOR]

• A flexible tube model predictive control-based energy management regime is proposed for prosumer community microgrids, enabling robustness against uncertainties to be added to the optimal real-time energy scheduling strategy with both fewer economic sacrifices and appropriate computational sophistication. • Constructing a multistage prosumer community microgrids energy management optimization model, which allows the prosumers to quantitatively adjust their local energy management strategy based on real-time and predicted information. This realizes a reliable energy supply for the loads by minimizing daily operating costs. • A novel E-ATC algorithm is tailored for the constructed multistage optimization model, which accelerates the convergency rate of optimum energy management strategy and reinforces prosumer information privacy-preserving. Achieving reliable, cost-effective, and low-carbon energy management for community microgrids (CMs) under suitable computational complexity is challenging due to uncertainties in renewable generation and load demand. To address this challenge, this paper proposes a flexible tube model predictive control-based energy management regime (EMR) for prosumer CMs, enabling robustness against system uncertainties in the energy management strategy with fewer sacrifices in economic performance and computational efficiency. The proposed EMR adopts a multistage receding horizon optimization structure, which allows the prosumers to dynamically adjust their energy scheduling according to the updated energy price and forecast information. Further, a decentralized algorithm with a conditional communication strategy is developed, highlighting less communication burden and private information preservation. Case studies on a typical prosumer CM demonstrate the effectiveness of the proposed method in confronting high system uncertainties. Notably, the proposed method ensures that CMs operate with high reliability (having a maximum energy supply shortage probability of just 0.42 %), in a low-carbon and economical manner (the local accommodation rate of renewable energy generation with low costs reaches 98.83 %), even in scenarios where disturbances occur simultaneously on both the energy supply and demand sides. [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]