Driven by the goals of clean energy and zero carbon emissions, the power industry is undergoing significant transformations. The rapid growth of diverse distributed energy resources (DERs) at grid edge such as rooftop photovoltaics (PVs) and electric vehicles is transforming the traditional centralized power grid management to a decentralized, bottom-up, and localized control paradigm. Establishing

Deep reinforcement learning (DRL) has scored unprecedented success in finding near-optimal solutions in high-dimensional stochastic problems, leading to its extensive use in operational research, including the operation of power systems. However, in practice, it has been adopted with extreme caution because the standard DRL does not guarantee the satisfaction of operational constraints. In this paper

Transient frequency deviation resulting from load pick-up operations is a barrier to DG-based service restoration. The prime mover dynamics and control-parameter configuration have a significant effect on the frequency response rate (FRR) in the restoration process, but have not been sufficiently considered in previous works. To address this issue, this letter analyzes the FRR based on the transfer

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As the “smart grid” paradigm becomes more prevalent, fundamental techno-economic challenges prominently arise. The variability of renewables energy sources (RES) requires conventional generators to remain active and operate inefficiently. The grid’s inertia grows weaker, jeopardizing its stability. Ever more frequent network-related issues threaten the grid’s security. The industry experiences extensive

Radiality constraints are widely embedded in optimization problems associated with distribution systems. In this letter, a novel mathematical formulation of radiality constraints is proposed based on the maximum density, and proved to be the necessary and sufficient condition for radial topology. Compared with the existing formulation, the number of involved binary variables and constraints can be

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This paper presents OpenGridGym, an open-source Python-based package that allows for seamless integration of distribution market simulation with state-of-the-art artificial intelligence (AI) decision-making algorithms. We present the architecture and design choice for the proposed framework, elaborate on how users interact with OpenGridGym, and highlight its value by providing multiple cases to demonstrate

The increasing integration of renewable energy requires a good model of the existing power distribution infrastructure, represented by its admittance matrix. However, a reliable estimate may either be missing or quickly become obsolete, as distribution grids are continuously modified. In this work, we propose a method for estimating the admittance matrix from voltage and current measurements. By focusing

Distribution system (DS) restoration after natural disasters often faces the challenge of communication failures to feeder automation (FA) facilities, resulting in prolonged load pick-up process. This letter discusses the utilization of drone small cells for wireless communication recovery of FA, and proposes an integrated DS restoration strategy with communication recovery. Demonstrative case studies

This letter proposes a DC microgrid for sustainable power generation on the Mars/Moon for a human inhabitation base. The proposed microgrid includes: (i) A wind turbine (WT) system with a dual rotor generator (DRG) whose output is rectified using a passive rectification state and connected to the microgrid common DC bus with a fixed voltage using a DC-DC converter. (ii) Solar panels that use Passive

In this letter, we present a single mixed-integer linear programming (MILP) model for high-stealth false data attacks (FDAs) on overloading a set of lines by injecting stealthy false data. The proposed model reveals that an intelligent attacker is able to deliberately construct a valid attack vector to overload multiple transmission lines while hiding it among normal data to evade advanced anomaly

The existing bidding models for retailers managing electric vehicles (EVs) in distribution-level day-ahead (DA) electricity markets have not fully addressed EVs’ temporal distribution, charging and discharging management, or bidding curves. To address these challenges, this paper proposes a DA optimal bidding model for a retailer with flexible participation of EVs. First, the investigating period for

The rapid expansion of distributed energy resources and variable renewable energy is occurring in most utilities around the world, pertaining to the clean energy transition. These technologies have brought new challenges and opportunities to transmission grid planners. In this context, the planning process cannot continue to focus solely on the instant of the peak demand to make decisions regarding

By sharing common assets such as the power grid, prosumers are closely interrelated by their actions and interests. Game theory provides powerful tools for increased coordination among the prosumers to optimize the energy resources. However, depending on the prosumer profiles and the market rules, the individual bills may notably differ and prove to be unfair. In this work, we analyze the outcomes

As a typical cyber-physical system, dispersed Phasor Measurement Units (PMUs) are networked together with advanced communication infrastructures to record the Distribution Synchrophasor (DS) which represents the states and dynamics of distribution power networks. Source information of DS is critical for many DS-based applications which is potentially vulnerable to data integrity attacks. To ensure

The automatic generation control (AGC) is one of the core control systems in power grids that regulate frequency within the permissible range. However, its dependence on communication makes it highly vulnerable to cyber-attacks. An arbitrary false data injection attack (FDIA) on AGC frequency and tie-line flow measurements will likely be detectable by bad data detection methods; however, if an attack

For a Battery Energy Storage System (BESS)-based autonomous DC microgrid, owing to the coupling complexity between multiple control objectives under a hierarchical control framework, coordination control for large-signal stabilization is well-acknowledged as a non-trivial problem. This paper aims to present a self-disciplined nonsmooth coordination control strategy to address the multi-objective task

This paper proposes a decentralised pool-based local market model for a small energy community, intended to operate in parallel with an existing energy market, to manage the unscheduled active-hour (AH) appliances without violating the day-ahead nodal price of the community. The paper defines and targets this specific class of appliances with a two-stage iterative model. Stage-I calculates tentative

The coordination between system operators is a key element for the decarbonization of the power system. Over the past few years, many EU-funded research projects have addressed the challenges of Transmission System Operators (TSO) and Distribution System Operators (DSO) coordination by implementing different data exchange architectures. This paper presents a review of the ICT architectures implemented

Enhancing restoration capabilities of distribution systems is one of the main strategies for resilient power systems to cope with extreme events. However, most of the existing studies assume the communication infrastructures are intact for distribution automation, which is unrealistic. Motivated by the applications of the emergency communication vehicles (ECVs) in quickly setting up wireless communication

We propose a novel demand response framework based on two similarities in building electricity consumption patterns for curtailment service providers (CSP)s to optimally manage the aggregated building energy. We find the first similarity in spatio-temporal correlations of building demand. To avoid conflicts of interests among buildings, we synthesize building demand and renewable energy scenarios based

As an important cyber-physical system (CPS), smart grid is highly vulnerable to cyber attacks. Amongst various types of attacks, false data injection attack (FDIA) proves to be one of the top-priority cyber-related issues and has received increasing attention in recent years. However, so far little attention has been paid to privacy preservation issues in the detection of FDIAs in smart grids. Inspired

This paper describes a method to cost-optimally locate and size chargers for electric vehicles (EVs) in power distribution grids as a function of the driving demand while respecting the grid’s constraints. The problem accounts for the notion of single-port chargers (SPCs), where a charger can interface one EV maximum, and multi-port chargers (MPCs), where the same charger can interface multiple EVs

This paper addresses the distribution system state estimation (DSSE) with unknown topology change. A specific kernel that can transfer across tasks is adopted to find relevant patterns from samples under different topologies and induce knowledge transfer. This enables the proposed method to achieve effective inductive reasoning when only limited data are available under a new topology. The Bayesian

With increasing installation of behind-the-meter distributed energy resources (DERs) at the household level, the power load profile has been significantly masked. As a result, original load forecasting models are becoming not suitable for the continuously masked-load. Besides, present masked-load may not have sufficient samples to train an accurate forecasting model. This letter proposed a transfer

State estimation algorithms deployed in modern grids are vulnerable towards false data injection attacks. This work demonstrates a novel attack vector (AV) formulation scheme by exploiting the low-rank subspace of the topology matrix. With constraints over the state deviation vector, this work showcases that the developed AV can effectively bypass the residue test for a demonstrated test bench.

The dynamic simulation of the integrated electricity and gas system (IEGS) is effective for accurate operational guidance. However, the IEGS model governed by partial and ordinary differential-algebraic equations (PDAE) is challenging for analysis, because it poses heterogeneous properties, high dimensionality, and strong nonlinearity. To eliminate the nonlinear terms, this paper firstly derives the

In recent years, electric vehicle (EV) charging stations have experienced an increasing supply-demand mismatch due to their fluctuating renewable generation and unpredictable charging demands. To reduce their operating costs, this paper proposes a distributed online algorithm to promote energy sharing between charging stations. We begin with the offline and centralized version of the EV charging stations

Intelligent electrical power grids, widely referred to as smart grids (SGs), rely on digital technology resources, especially communication and measurement devices, becoming a cyber-physical energy system. Massive data flow between grid elements makes smart grids more vulnerable to cyber-attacks. Power system state estimation (SE)—an essential function of energy management systems—is one of these data

Due to the influence of inverter control, the short-circuit current provided by distributed photovoltaics (PVs) exhibits new characteristics, such as a controlled amplitude and phase angle, resulting in the malfunction of the current protection and differential protection of distribution networks with high PV penetration. In this paper, the equivalent high-frequency impedance model (EHFIM) of PV power

The interest in the integration of distributed power generation and microgrids into power grids has significantly increased. In this context, interfacing microgrids with a host grid is of significant relevance, and therefore, in this paper, a cost-effective controllable grid interface for microgrids is proposed, modeled, and studied under various conditions, including smooth and abrupt power fluctuations

With the rapid development of microgrids, the types of users and loads in microgrids are increasing. Comprehensively reflecting the value of loads in load shedding decisions is one of the main challenges for the intelligent control of an islanded microgrid. However, the existing load shedding schemes ignore the user’s subjective preference for multiple attributes of loads, which are insufficient for

Rail transport of liquefied natural gas (LNG), as an essential means of transmitting energy across regions, has been widely implemented and promoted. The significant growth of gas-fired power and power-to-gas units has intensified the interaction and interdependency between wind power penetrated transmission system and LNG transported railway network. Hence, a robust and hierarchical scheduling approach

Modern power systems are vulnerable to false data injection attacks (FDIAs) due to the widespread applications of two-way communication networks for system operation and control. Diagnosing such malicious attacks are of great significance for the resilient operations of power systems. Yet, in literature, the majority of studies in FDIA detection focus on power transmission systems. In this paper, a

Modern power systems are facing the tremendous challenge of integrating vast amounts of variable (non-dispatchable) renewable generation capacity, such as solar photovoltaic or wind power. In this context, the required power system flexibility needs to be allocated in other units that can include energy storage, demand management or providing reserve from renewables curtailing the output power. The

While most smart grids approaches assume that the user consumes services in a fixed location, the case of charging Battery Electric Vehicles (BEVs) adds an interesting mobility dimension. Drivers increasingly need to rely on public charging facilities, and given local grid conditions, such facilities often cannot offer their services without smart charging and tight integration in the smart grid. In

This paper presents a peer-to-peer (P2P) local energy trading strategy in distribution networks with a novel decentralized voltage management scheme based on voltage sensitivity factors. A social welfare maximizing optimization problem is formulated for P2P energy trading, which is solved by an alternating direction method of multipliers (ADMM) algorithm. The proposed approach is especially designed

The comprehensive utilization of multi-energy resources has been recognized as an effective way to improve the energy utilization efficiency. This paper proposes a distributed double-side multi-energy coordination framework to minimize the total system cost by optimizing the energy supply of energy hubs (EHs) and the energy consumption of consumers, considering the tradeoff between the fuel cost of

Optimal gas-power flow (OGPF) is one of the most fundamental problems for the operation of integrated electric-gas systems (IEGSs), where the gas flow dynamics partial differential equations (PDEs) are computational challenging. This paper aims to propose a lightweight and accurate model for gas flow dynamics for the OGPF problem. Firstly, the second-order hyperbolic PDEs are equivalently derived based

Malicious traffic attack can prolong the transmission delay of normal data flows by exhausting the limited bandwidth with injecting numerous invalid data packets. For the networked load frequency control (LFC) systems, the traffic attacks can cause the control performance deterioration or even instability. In this paper, an adaptive LFC scheme where the controller gains can be flexibly adjusted according

A rapidly growing amount of small-scale distributed energy resources (DERs) integrated into distribution systems call for an effective distribution electricity market to manage the uncertainty of DERs and remove barriers to the participation of DERs in wholesale electricity markets. To this end, this paper proposes an uncertainty-aware distribution locational marginal pricing (DLMP) mechanism based

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Economic and environmental constraints, as well as technological advancements, are reshaping the traditional view of power distribution systems. In a future distribution grid, as the number of inverter- and converter-based devices increases to more than hundreds of thousands, it is rather intuitive that the current state-of-the-art technical solutions and industry practices will no longer be effective

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This paper proposes the use of annealing-based quantum computing for solving combinatorial optimal power flow problems. Quantum annealers provide a physical computing platform which utilises quantum phase transitions to solve specific classes of combinatorial problems. These devices have seen rapid increases in scale and performance, and are now approaching the point where they could be valuable for

Cryptographic schemes are often deployed in microgrids to protect communication security. However, most existing schemes widely used in microgrids cannot ensure forward secrecy and non-repudiation under key leakage and dishonest insiders. Although some existing cryptographic techniques can theoretically overcome these vulnerabilities, their computational complexity may significantly extend the communication

This paper presents a novel load-flow method for unbalanced islanded ac microgrids. The principles of this method are based on the modified augmented nodal analysis (MANA) formulation. It is shown that it can considerably improve the performance over conventional mismatch equation-based solvers for islanded microgrids, while being more generic and simpler to formulate. An unbalanced 25-bus ac microgrid

With the increase in inverter interfaced distribution generations (IIDGs), converter-dominated distribution networks (CDDNs) are expected. When a single-phase-to-earth (SPE) fault occurs in a CDDN, since the inverter transformer effects can affect the transient fault characteristics of fault signals, the existing faulty feeder detection techniques may not be applicable. This paper focuses on the inverter

The increasing integration of distributed energy resources (DERs) into the distribution system has caused interference with the signals stimulated by high impedance faults (HIFs). Many existing HIF detection or location methods that are only based on the superficial nonlinearity features of signals rather than the deep laws, will become invalid. This paper proposes a detection and location scheme for