Driven by the recent advances and applications of smart-grid technologies, our electric power grid is undergoing radical modernization. Microgrid (MG) plays an important role in course modernization providing a flexible way to integrate distributed renewable energy resources (RES) into grid. However, RES, such as solar wind, can be highly intermittent stochastic. These uncertain combined with load demand result random variations both supply sides, which make it difficult effectively operate...

The dependence on advanced information and communication technology increases the vulnerability in smart grids under cyber-attacks. Recent research unobservable false data injection attacks (FDIAs) reveals high risk of secure system operation, since these can bypass current bad detection mechanisms. To mitigate this risk, paper proposes a data-driven learning-based algorithm for detecting FDIAs distribution systems. We use autoencoders efficient dimension reduction feature extraction...

This paper develops a model-free volt-VAR optimization (VVO) algorithm via multi-agent deep reinforcement learning (DRL) in unbalanced distribution systems. method is novel since we cast the VVO problem networks to an intelligent Q-network (DQN) framework, which avoids solving specific model directly when facing time-varying operating conditions We consider statuses/ratios of switchable capacitors, voltage regulators, and smart inverters installed at distributed generators as action...

This paper proposes a data-driven distributionally robust co-optimization model for the peer-to-peer (P2P) energy trading and network operation of interconnected microgrids (MGs). In particular, three-phase unbalanced MG networks are considered to account implementation practices, emerging soft open point (SOP) technology is used flexible connection multi-MGs. First, management in individual MGs modeled as optimization (DRO) problem considering P2P options various operational constraints....

Energy storage systems (ESS) are indispensable building blocks of power with a high share variable renewable energy. As energy-limited resources, ESS should be carefully modeled in uncertainty-aware multistage dispatch. On the modeling side, we develop two-stage model for that respects nonanticipativity dispatch, and implement it into distributionally robust predictive control scheme. The features multiple selective operational modes, which enable its interval to scheduled chronological...

The Energy Internet (EI), an interlocked combination of energy systems and the Internet, is emerging concept that embodies contours next-generation system. EI a new ecosystem with better interconnectedness, openness, flexibility, aiming to accommodate deep-penetration, clean energies; improve efficiency; create novel sharing economy significantly reduce cost consumption.

Investor-owned photovoltaic-battery storage systems (PV-BSS) can gain revenue by providing stacked services, including PV charging and frequency regulation, performing energy arbitrage. Capacity scheduling (CS) is a crucial component of PV-BSS management, aiming to ensure the secure economic operation PV-BSS. This article proposes Proximal Policy Optimization (PPO)-based deep reinforcement learning (DRL) agent perform CS Unlike previous work that uses value-based methods with discrete action...

The sluggish kinetics and unclear mechanism have significantly hindered the development of Li-CO2 batteries. Here, a battery cathode catalyst based on porphyrin-based covalent organic framework (TTCOF-Mn) with single metal sites is reported to reveal intrinsic catalytic aprotic CO2 conversion from molecular level. TTCOF-Mn exhibits low overpotential 1.07 V at 100 mA/g as well excellent stability 300 mA/g, which one best catalysts date. unique features including uniform single-Mn(II)-sites,...

Distributed energy resources bring about challenges related to the participation of an increasing number prosumers with strong social attributes in peer-to-peer (P2P) sharing markets, resulting increased complexity socio-technical systems. Previous research has focused on analysis based rational games without considering prosumers, which are not typically used real scenarios. In this article, interdisciplinary P2P framework that considers both technical and sociological aspects is proposed....

This paper proposes a novel peer-to-peer (P2P) multi-grade energy trading design to encourage demand side flexibility locally absorb the uncertainty of renewable distributed resources (DERs) in distribution networks. In particular, reliability credit (RC) assignment method is developed for customers differentiate grades considering heterogeneity supplying DERs and consumption preferences customers. Later, an innovative P2P model introduced where different types are matched up with...

This paper develops a resilience analysis framework to study the fault ride-through capability of direct current (DC) microgrids in unknown denial service (DoS) cyber incidents. DoS can be frequent threat DC with advanced controllers that hinge on active information exchanges: it paralyze data communications and cause control ineptness or even system instability. Furthermore, we show temporal incidents render microgrid cyber-physical topology parameters time-varying them jump between faulty...

With the increasing complexity of modern power systems, conventional dynamic load modeling with ZIP and induction motors (ZIP + IM) is no longer adequate to address current characteristic transitions. In recent years, WECC composite model (WECC CLM) has shown effectively capture responses over traditional models in various stability studies contingency analyses. However, a detailed CLM typically high degree complexity, one hundred parameters, systematic approach identifying calibrating these...

To realize the potential of active distribution networks (ADNs) for improving power system flexibility and to cope with multiple uncertainties, a coordinated robust reserve scheduling (CRRS) model coupled transmission (CTD) is proposed in this paper. This coordinates generation resources both normal state re-dispatch enhance cost-effectiveness reliability perspective. A fully distributed framework based on alternating direction method multipliers (ADMM) employed solve problem decentralized...

Peer-to-peer energy sharing in the distribution networks (DN) is an emerging issue with large-scale development of photovoltaic (PV) prosumers. The DN can be classified into energy-shared regions (ESR) to enable zonal trading. A Stackelberg-game-based energy-sharing framework recommended for multi-ESR, where provider (ESP) works as a leader dynamic pricing whereas PV prosumers serve followers demand response's (DR) ability choose ESR link and modify their flexible loads. profit maximization...

Battery swapping stations (BSSs) are ideal candidates for fast frequency regulation services (FFRS) due to their large battery stock capacity. In addition, BSSs can precharge batteries customers and the that not in charging provide a stable capacity market. However, uncertainties, such as ACE signals EV per-hour visit counts, introduce stochastic nonlinear dynamics into operation of BSS-based FFRS. Currently, there is no quantification method ensure its optimal economical operation. To close...

This paper proposes a novel modeling framework and decomposition-based solution strategy combining stochastic programming (SP) robust optimization (RO) to deal with multiplex uncertainties in coordinated mid- long-term power system planning. The problem is formulated as multi-year generation transmission planning from an independent operator (ISO)'s perspective minimize both expansion operational costs under binary continuous uncertainties, <inline-formula...

Motivated by increasing penetration of distributed generators (DGs) and fast development micro-phasor measurement units (μPMUs), this paper proposes a novel graph-based faulted line identification algorithm using limited number μPMUs in distribution networks. The core the proposed method is to apply advanced system state estimation (DSSE) techniques integrating μPMU data fault location. We propose DSSE efficiently restrict searching region for source feeder between two adjacent μPMUs. Based...

In recent years, with the rapid growth of rooftop photovoltaic (PV) generation in distribution networks, power system operators call for accurate forecasts behind-the-meter (BTM) load and PV generation. However, existing forecasting methodologies are incapable quantifying such BTM measurements as smart meters can merely measure net time series. Motivated by this challenge, article presents spatiotemporal (ST-BTMLPVF) problem. The objective is to disaggregate historical loads neighboring...

A distribution service restoration algorithm as a fundamental resilient paradigm for system operators provides an optimally coordinated, solution to enhance the performance. The problem is formulated coordinate generators and controllable switches optimally. model-based control scheme usually designed solve this problem, relying on precise model resulting in low scalability. To tackle these limitations, work proposes graph-reinforcement learning framework problem. We link power topology with...