With the continuous expansion of the power system scale and extensive application of phasor measurement units (PMUs), the secure operation of power systems has been increasingly concerned. In this paper, an integrated scheme for online dynamic security assessment (DSA) based on feature selection and regression prediction is proposed. First, partial mutual information (PMI) and the Pearson correlation

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In this paper, the issue of interarea oscillations in wide-area power systems with false data injection (FDI) attacks is studied. A wide-area robust sliding mode controller (WARSMC) is proposed to defense the hacker attacks and damp out the interarea oscillations. First, an extended observer is designed to estimate the states and defense actuator attacks simultaneously. Then, a WARSMC based on the

With the significant increase in integration of renewable energy generation into the electric grid, market-based transactive exchanges between energy producers and prosumers will become more common. Transactive energy systems (TESs) employ economic and control mechanisms to dynamically balance the demand and supply across the electrical grid. Emerging transactive control mechanism depends on a large

This paper presents a two-layer distributed cooperative control method for networked microgrid (NMG) systems, taking into account the proprietary nature of microgrid (MG) owners. The proposed control architecture consists of an MG-control layer and an NMG-control layer. In the MG layer, the primary and distributed secondary controls realize accurate power sharing among distributed generators (DGs)

This paper presents a framework for malicious corruption resilience in phasor measurement unit (PMU) data and a methodology for applying this in wide-area damping control. The problem of detecting corruptions in raw PMU measurements is formulated as a compressed sensing problem and the compromised signals are recovered using an lp-norm (0 <; p <; 1)-based online robust principal component analysis

The increasing penetration of distributed photovoltaics (PVs) has brought great challenges to the security and stability of distribution networks (DNs), especially voltage violation. Current voltage optimization methods rarely consider the mutual voltage support between high voltage (HV) and medium voltage (MV) DNs, which may cause PV generation losses in extreme scenarios. Limited by the convergence

Existing models featuring numerous electric vehicles (EVs) for centralized frequency regulation achieved high accuracy at the expense of heavy computational workloads and a high real-time communication requirement. This paper develops a state space model (SSM) that provides a probability to realize the real-time power control of aggregated EVs with high accuracy and computational efficiency but a low

The online monitoring data in distribution networks contain rich information on the running states of the networks. By leveraging the data, this paper proposes a spatio-temporal correlation analysis approach for anomaly detection and location in distribution networks. First, spatio-temporal matrix for each feeder line in a distribution network is formulated and the spectrum of its covariance matrix

This paper presents a two-stage microgrid scheduling strategy in which the frequency control reserve (FCR) is incorporated to ensure economic, reliable, and stable microgrid operation in a joint energy and ancillary service market environment. In addition, to using such reserve to limit frequency excursions caused by uncertainties of active loads and renewable generation, this paper is focused on determining

The cascaded control method with an outer voltage loop and an inner current loop has been traditionally employed for the voltage and power control of photovoltaic (PV) inverters. This method, however, has very limited power regulation capability. With the fast increasing penetration of PV power generation systems in the distribution network, the voltage rise/drop has become a serious problem impacting

Mobile energy storage systems (MESSs) provide promising solutions to enhance distribution system resilience in terms of mobility and flexibility. This paper proposes a rolling integrated service restoration strategy to minimize the total system cost by coordinating the scheduling of MESS fleets, resource dispatching of microgrids, and network reconfiguration of distribution systems. The integrated

Hurricanes can damage transmission network structure components, e.g., towers and conductors, and threat the system reliability. To quantify the consequence severity of hurricanes on transmission networks, a spatial-temporal reliability and damage assessment method is proposed to evaluate the effect of a specific hurricane. The hurricane can be a simulated, historical, or forecasting one for different

Trustworthy and secure operation of the cyber-power system calls for resilience against malicious and accidental failures. The objective of a resilient system is to withstand and recover operation of the system to supply critical loads despite multiple contingencies in the system. To take timely actions, we need to continuously measure the cyberphysical security of the system. We propose a cyber-physical

In this paper, an intelligent multi-microgrid (MMG) energy management method is proposed based on deep neural network (DNN) and model-free reinforcement learning (RL) techniques. In the studied problem, multiple microgrids are connected to a main distribution system and they purchase power from the distribution system to maintain local consumption. From the perspective of the distribution system operator

In this paper, we propose a data-driven algorithm for probabilistic optimal power flow (POPF). In particular, we develop a nonparametric Bayesian framework based on the Dirichlet process mixture model (DPMM) and variational Bayesian inference (VBI) to establish a probabilistic model for capturing the uncertainties involved with wind generation and load power in power systems. In the proposed setup

Machine learning and artificial intelligence (AI) techniques can play a key role in resource allocation and scheduler design in wireless networks that target applications with stringent QoS requirements, such as near real-time control of community resilience microgrids (CRMs). Specifically, for integrated control and communication of multiple CRMs, a large number of microgrid devices need to coexist

The ever-growing requirement for reliability and quality of power supply suggests to enable self-healing features of smart distribution network using intelligent communication and control. In this article, a concept of fully decentralized multi-agent system (FDMAS) automation is proposed to build a unified restoration service framework for distribution network with distribution generators (DGs), where

Energy storage can play an important role in energy management of end users. To promote an efficient utilization of energy storage, we develop a novel business model to enable virtual storage sharing among a group of users. Specifically, a storage aggregator invests and operates the central physical storage unit, by virtualizing it into separable virtual capacities and selling to users. Each user purchases

The future electric grid will consist of significant penetration of renewable and distributed generation that is likely to create a homogenous transmission and distribution (T&D) system, requiring tools that can model and robustly simulate the combined T&D networks. Existing tools use disparate models and formulations for simulation of transmission versus distribution grids and solving for the steady-state

Optimally scheduling power consumption of appliances is the essential feature of smart grid, which enables Demand Response Management (DRM) and helps to shape the power usage profile. This problem is often required to be solved in face of a large number of appliances and many time slots; thus the computational efficiency of solving a large scale optimal power scheduling problem with limited computational

This paper proposes a day-ahead market framework for congestion management in smart distribution networks. The presented scheme provides a platform for collaboration between distribution-level market operator (DMO) and data traffic operator (DTO) to alleviate congested feeders such that data transmission traffic between market participants is effectively managed in a smart grid. In addition, a decentralized

This study is motivated by the recent advancements in developing non-contact line sensor technologies that come at a low cost, but have limited measurement capabilities. While they are intended to measure current, they cannot measure voltage and power. This poses a challenge to certain distribution system applications, such as topology identification (TI), because they commonly use voltage and power

Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed offline based on either the conceived “worst” case scenario or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical

Electric load forecasting, especially short-term load forecasting (STLF), is becoming more and more important for power system operation. We propose to use multiple kernel learning (MKL) for residential electric load forecasting which provides more flexibility than traditional kernel methods. Computation time is an important issue for short-term forecasting, especially for energy scheduling. However

This paper presents an approximate Reinforcement Learning (RL) methodology for bi-level power management of networked Microgrids (MG) in electric distribution systems. In practice, the cooperative agent can have limited or no knowledge of the MG asset behavior and detailed models behind the Point of Common Coupling (PCC). This makes the distribution systems unobservable and impedes conventional optimization

In the future power system, an increasing number of distributed energy resources will be integrated including intermittent generation like photovoltaic (PV) and flexible demand like electric vehicles (EVs). It has been long thought to utilize the flexible demand to absorb the PV output locally with the technical solution proposed while an effective commercial arrangement is yet to be developed due

The uncertainties of distribution energy resources (DERs) have significant impacts on active distribution networks (ADNs), and it is necessary to control the DER outputs to hedge the negative impacts of their uncertainties on ADNs. However, DER uncertainties are complicated, containing spatial and temporal correlation, which makes it challenging to design proper control schemes, especially when there

Targeting the right customers for energy efficiency (EE) programs is crucial for the power distribution or retail companies to enhance the efficiency of marketing budget allocation and the yield of energy savings. This work presents a scalable methodology for targeting residential customers for EE programs that focus on reducing unnecessary domestic energy consumption and replacing low efficient r

The presence of single-phase distributed generators unevenly injecting active power in three-phase microgrids may create undesired upstream current unbalance. Consequently, voltage asymmetry and even active power curtailment may occur in such networks with negative economic impact. Thus, this paper proposes an optimal multiobjective approach to regulate the active and reactive power delivered by distributed

In a wide-area control system (WACS) of power systems, delays will affect the control performance. Dealing with randomly distributed delays besides achieving desired control performance via the WACS control strategy will make the control strategy design more challenging. Moreover, delays from the centralized controller to actuators in WACS are unknown for the controller when calculating the control

Traditionally, energy consumers pay non-commodity charges (e.g., transmission, environmental and network costs) as a major component of their energy bills. With the distributed energy generation, enabling energy consumption close to producers can minimize such costs. The physically constrained energy prosumers in power networks can be logically grouped into virtual microgrids (VMGs) using telecommunication

Random attacks that jointly minimize the amount of information acquired by the operator about the state of the grid and the probability of attack detection are presented. The attacks minimize the information acquired by the operator by minimizing the mutual information between the observations and the state variables describing the grid. Simultaneously, the attacker aims to minimize the probability

As microgrids (MGs) interact with power distribution systems, the end-to-end transactive energy (TE) trading among MGs poses new challenges on operation and trading functions of distribution system operators (DSOs). This paper proposes a bi-level programming framework for the coordination of multi-MG energy TE trading and the operation of power distribution systems. The bi-level framework enables distributed

The implementation of optimal power flow (OPF) methods to perform voltage and power flow regulation in electric networks is generally believed to require extensive communication. We consider distribution systems with multiple controllable Distributed Energy Resources (DERs) and present a data-driven approach to learn control policies for each DER to reconstruct and mimic the solution to a centralized

The interdependence of different energy forms in multiple energy systems (MESs) could leverage their synergies to reduce carbon emissions. However, such synergies cannot be exploited without the right incentives. This paper studies the lowcarbon operation of MESs by coordinating the transmission-level and distribution-level via the energy-carbon integrated prices. Energy prices are decided by locational

Distributed control schemes have transformed frequency and voltage regulation into a local task in distributed generators (DGs) rather than by a central secondary controller. A distributed scheme is based on information shared among neighboring units; thus, the microgrid performance is affected by issues induced by the communication network. This paper presents a distributed predictive control applied

Microgrids (MGs) operate under harmonic conditions due to the integration of nonlinear loads. The autonomous harmonic compensation control of inverter-interfaced DG has been proposed to successfully mitigate the harmonics. However, the small-signal analysis of harmonic compensation controls has not been investigated in the microgrid with multiple inverters. This paper develops the modeling and analysis

Bi-level optimization and reinforcement learning (RL) constitute the state-of-the-art frameworks for modeling strategic bidding decisions in deregulated electricity markets. However, the former neglects the market participants' physical non-convex operating characteristics, while conventional RL methods require discretization of state and/or action spaces and thus suffer from the curse of dimensionality

The availability of high-fidelity energy networks brings significant value to academic and commercial research. However, such releases also raise fundamental concerns related to privacy and security as they can reveal sensitive commercial information and expose system vulnerabilities. This paper investigates how to release the data for power networks where the parameters of transmission lines and transformers

Probabilistic load forecasting is gaining growing interest by researchers and practitioners. Multi-stage forecasting systems have recently demonstrated their effectiveness in increasing the overall performances. In this paper, we address the effect of pre-processing load time series using wavelet-based decompositions, before using quantile regression forests and random forests to build probabilistic

Both deterministic and probabilistic load forecasting (DLF and PLF) are of critical importance to reliable and economical power system operations. However, most of the widely used statistical machine learning (ML) models are trained by optimizing the global performance, without considering the local behaviour. This paper develops a two-step short-term load forecasting (STLF) model with Q-learning based

This study proposes a cooperative secondary voltage and frequency control strategy to reduce the number of controller updates by using an event-triggered approach. The proposed approach is applied to the secondary control that will offset primary control deviations in islanded microgrids with limited computation resources. The controller updating mechanism considered here is event-triggered which judges

With the rapid advancement of phasor measurement units (PMUs) technology, system operators in different level of power systems have access to new and abundant measurements. Taking into account these measurements in active distribution systems (ADNs), a new algorithm for short-circuit fault detection and identification based on state estimation (SE) is introduced in this paper. In this regard, as the

This paper proposes a peer-to-peer (P2P) energy trading scheme that can help a centralized power system to reduce the total electricity demand of its customers at the peak hour. To do so, a cooperative Stackelberg game is formulated, in which the centralized power system acts as the leader that needs to decide on a price at the peak demand period to incentivize prosumers to not seek any energy from

Non-intrusive load monitoring (NILM) is a technique to recover source appliances from only the recorded mains in a household. NILM is unidentifiable and thus a challenge problem because the inferred power value of an appliance given only the mains could not be unique. To mitigate the unidentifiable problem, various methods incorporating domain knowledge into NILM have been proposed and shown effective

A novel affine arithmetic (AA) method with missed triangular domain (remaining area is convex polygons) is proposed in this paper for solving interval power flow (IPF) problem with uncertainties. An interval correlation model of random variables is established, and the original correlated interval variables are transformed into the standard space using interval arithmetic (IA) to reduce overestimation

The rapid deployment of intelligent electronic devices and the development of information and communication technology have improved distribution network (DN) reliability with feeder automation. Thus, the operation of DNs has become more dependent on the cyber-physical components. As the cyber security of smart grid has drawn increasing attention in recent years, this paper proposes a simple yet powerful

While the quality of the synchronized measurements is of paramount importance for real-time monitoring and protection of the power grids, collected measurements often contain missing values. This paper proposes a scheme for diagnosing attacks and faults in the presence of missing measurements in power grid data. The proposed scheme contains four modules for clustering, missing data imputation, decision-making

This paper develops an optimal electricity-gas flow (OEGF) framework considering power line and pipeline congestions to coordinate the energy supplies, flexible electricity and gas loads, electricity and gas storages within an integrated electricity and gas system. The objective is to maximize the social welfare, while balancing the supply and demand, and relieving the congestion. The formulated OEGF

During the deregulation of retail power market, multiple stakeholders are gradually involved in distribution network to invest distributed resources and participate in power transactions, bringing great uncertainty to grid operations. This paper proposes a novel robust coordination expansion planning method that considers non-network solutions in a deregulated retail market, including the closed-loop

We consider a Charging Network Operator (CNO) that owns a network of Electric Vehicle (EV) public charging stations and wishes to offer a menu of differentiated service options for access to its stations. This involves designing optimal pricing and routing schemes for the setting where users cannot directly choose which station they use. Instead, they choose their priority level and energy request

The utilization rate of generation assets significantly affects the profits of generation companies. Since the daily load profiles of customers are not considered in conventional time-of-use (TOU) pricing, a fairness issue may arise because of cross subsidy. To address this issue, a block-of-use (BOU) electricity retail pricing approach is proposed, in which the price varies from block to block instead

This paper presents a novel coordinated energy management approach for hybrid AC/DC distribution system with microgrid clusters considering multiple market players, which is modeled as a bi-level optimization problem by using noncooperative game theory and robust optimization. The hybrid AC/DC distribution network and each microgrid are regarded as different entities, which are in the upper/lower level

Uncertainties associated with the increasing penetration of renewable power generation (RPG) in microgrids have introduced numerous challenges to their effective energy management. This paper proposes a novel risk-based uncertainty set optimization method for the energy management of typical hybrid AC/DC microgrids, where RPG outputs are considered as the major uncertainties. The underlying risks of

The coordinated operation of an integrated electricity and heating system (IEHS) can improve overall energy efficiency and provide additional flexibility. However, the inherent uncertainties pertaining to pipeline parameters and ambient temperature in a district heating network (DHN) have not been addressed in the literature, although such uncertainties could affect the quality or even security of

This paper presents a data-driven distribution system load modeling methodology targeting quasi-static timeseries (QSTS) simulation. The proposed methodology is appropriate for modeling down to the level of the customer transformer, and it has three main features: 1) both load pattern diversity and intra-second load variability are considered, 2) the load profiles can be populated for multiple nodes

Electric power systems foresee challenges in stability due to the high penetration of power electronics interfaced renewable energy sources. The value of energy storage systems (ESS) to provide fast frequency response has been more and more recognized. Although the development of energy storage technologies has made ESSs technically feasible to be integrated in larger scale with required performance

This paper proposes a novel, fully decentralized load frequency control (LFC) approach based on dynamic state estimation (DSE). The proposed approach employs an unknown input observer (UIO) for each power system area for tracking the dynamic states in real time operation. In order to achieve the fully decentralized control approach, the demand fluctuation and tie-line power deviations are modeled as