Showing papers in "IEEE Transactions on Power Systems in 2021"

Definition and Classification of Power System Stability – Revisited & Extended

Abstract: Since the publication of the original paper on power system stability definitions in 2004, the dynamic behavior of power systems has gradually changed due to the increasing penetration of converter interfaced generation technologies, loads, and transmission devices. In recognition of this change, a Task Force was established in 2016 to re-examine and extend, where appropriate, the classic definitions and classifications of the basic stability terms to incorporate the effects of fast-response power electronic devices. This paper based on an IEEE PES report summarizes the major results of the work of the Task Force and presents extended definitions and classification of power system stability.

Understanding Small-Signal Stability of Low-Inertia Systems

Abstract: Large-scale integration of renewable generation, usually interfaced to the network through power electronics, has led to drastic changes in power system dynamics. This paper presents novel insights into stability properties of such systems. For that purpose, a high-fidelity dynamic model of a generic low-inertia power system has been developed. The full-order, state-of-the-art control schemes of both synchronous and converter-based generators are included, with the latter differentiating between grid-forming and grid-following mode of operation. Furthermore, the dynamics of transmission lines and loads are captured in the model. Using modal analysis techniques such as participation factors and parameter sensitivity, the most vulnerable segments of the system are determined and the adverse effects of timescale coupling and control interference are investigated. More precisely, this work characterizes the maximum permissible penetration levels of inverter-based generation as well as the nature of the associated unstable modes and the underlying dynamics. Finally, potential directions for improving the system stability margin under different generation portfolios are proposed for several benchmark systems.

An Enhanced IEEE 33 Bus Benchmark Test System for Distribution System Studies

Abstract: The transformation of passive distribution systems to more active ones thanks to the increased penetration of distributed energy resources, such as dispersed generators, flexible demand, distributed storage, and electric vehicles, creates the necessity of an enhanced test system for distribution systems planning and operation studies. The value of the proposed test system, is that it provides an appropriate and comprehensive benchmark for future researches concerning distribution systems. The proposed test system is developed by modifying and updating the well-known 33 bus distribution system from Baran & Wu. It comprises both forms of balanced and unbalanced three-phase power systems, including new details on the integration of distributed and renewable generation units, reactive power compensation assets, reconfiguration infrastructures and appropriate datasets of load and renewable generation profiles for different case studies.

Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM

Abstract: Accurate and rapid load forecasting for industrial customers has been playing a crucial role in modern power systems. Due to the variability of industrial customers’ activities, individual industrial loads are usually too volatile to forecast accurately. In this paper, a short-term load forecasting model for industrial customers based on the Temporal Convolutional Network (TCN) and Light Gradient Boosting Machine (LightGBM) is proposed. Firstly, a fixed-length sliding time window method is adopted to reconstruct the electrical features. Next, the TCN is utilized to extract the hidden information and long-term temporal relationships in the input features including electrical features, a meteorological feature and date features. Further, a state-of-the-art LightGBM capable of forecasting industrial customers’ loads is adopted. The effectiveness of the proposed model is demonstrated by using datasets from different industries in China, Australia and Ireland. Multiple experiments and comparisons with existing models show that the proposed model provides accurate load forecasting results.

Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation

Abstract: Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, time-synchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today's synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving the way for developing the next generation of energy management systems and novel system monitoring, control and protection tools to achieve better reliability and resiliency.

Two-Level Combined Control Scheme of VSC-MTDC Integrated Offshore Wind Farms for Onshore System Frequency Support

Abstract: This paper proposes a two-level combined control (TLCC) scheme of voltage source converter-based multi-terminal high-voltage direct current (VSC-MTDC) integrated offshore wind farms to provide frequency support for onshore system. The proposed TLCC scheme consists of two levels, which are the step start-up and adaptive inertial droop control of the offshore wind turbine level, and the communication-free allocation control of the onshore VSC station level. On the first level, each wind turbine adopts the inertial and droop control with adaptive coefficients, and all wind turbines (WTs) work at the maximum power point tracking (MPPT) mode without energy reserve. To reduce the second frequency drop (SFD), the WTs are divided into different clusters according to their rotor speed, and a step start-up control scheme is adopted for the WT clusters to provide frequency support sequentially. On the system level, the communication-free allocation control strategy is proposed using local frequency signal of onshore VSC stations to share the active power among onshore VSC stations reasonably. The proposed TLCC scheme can provide onshore system with frequency support and reduce the SFD simultaneously, while all WTs work at MPPT mode. Case studies are carried out on a 3-area 4-terminal VSC-MTDC based offshore wind farms (OWFs). Simulation results demonstrate the effectiveness and universality of the proposed TLCC scheme under different scenarios.

Physics-Guided Deep Neural Networks for Power Flow Analysis

Abstract: Solving power flow (PF) equations is the basis of power flow analysis, which is important in determining the best operation of existing systems, performing security analysis, etc. However, PF equations can be out-of-date or even unavailable due to system dynamics, and uncertainties, making traditional numerical approaches infeasible. To address these concerns, researchers have proposed data-driven approaches to solve the PF problem by learning the mapping rules from historical system operation data. Nevertheless, prior data-driven approaches suffer from poor performance, and generalizability, due to overly simplified assumptions of the PF problem or ignorance of physical laws governing power systems. In this paper, we propose a physics-guided neural network to solve the PF problem, with an auxiliary task to rebuild the PF model. By encoding different granularity of Kirchhoff's laws, and system topology into the rebuilt PF model, our neural-network based PF solver is regularized by the auxiliary task, and constrained by the physical laws. The simulation results show that our physics-guided neural network methods achieve better performance, and generalizability compared to existing unconstrained data-driven approaches. Furthermore, we demonstrate that the weight matrices of the proposed neural networks embody power system physics by showing their similarities with the bus admittance matrices.

Deep Learning Based Densely Connected Network for Load Forecasting

Abstract: Load forecasting is of crucial importance for operations of electric power systems. In recent years, deep learning based methods are emerging for load forecasting because their strong nonlinear approximation capabilities can provide more forecasting precision than conventional statistical methods. However, they usually suffer from some problems, e.g., the gradient vanishment and over-fitting. In order to address these problems, an unshared convolution based deep learning model with densely connected network is proposed. In this model, the backbone is the unshared convolutional neural network and a densely connected structure is adopted, which could alleviate the gradient vanishment. What is more, we use a regularization method named clipped $L_2$ -norm to overcome over-fitting, and design a trend decomposition strategy to address the possible distribution differences between the training and validation data. Finally, we conduct five case studies to verify the outperformance of our proposed deep learning model for deterministic and interval load forecasting. Two high-voltage and an medium-voltage real load datasets from Australia, Germany and America are used for model training and validation, respectively. Results show that the proposed model can achieve higher load forecasting accuracy, compared with other existing methods including the popular conventional methods such as naive forecast and generalized additive model, and deep learning methods, e.g., long short-term memory network, convolutional neural network, fully connected network, etc.

Optimal Operation Analysis of Integrated Community Energy System Considering the Uncertainty of Demand Response

Abstract: Propose a demand response uncertainty model based on price incentives, describe the relationship between the incentive price and the demand response coefficient. Constructed energy coupling matrices for integrated community energy systems considering demand response based on traditional energy hub models. The uncertainty of the demand response is depicted using the interval approach. Considering the load characteristics and various constraints of the integrated community energy system, optimized operating model with the goal of minimizing operating costs. Using an example of an integrated community energy system for a campus, considering multiple operating scenarios to investigate the effect of integrated demand response under different approaches, the impact of price incentives and incentive schemes on integrated community energy systems. The results show that compared with the traditional method, this method effectively reduces system operating costs and improves the load profile, which helps to achieve a win-win situation for both energy companies and users.

A distributed and robust security-constrained economic dispatch algorithm based on blockchain

Abstract: Distributed optimization algorithms for security-constrained economic dispatch (SCED) problems have been the subject of significant research interest in recent years. However, existing distributed SCED algorithms can be ineffective in the presence of malicious participants and inefficient in the absence of a coordinator. On the other hand, blockchain, an emerging technique known as the trust machine, has not shown its potential to address the above challenges in state-of-the-art literature. This paper proposes a blockchain-based distributed SCED algorithm. Using blockchain to form a coordination committee and enable balance among committee members, the proposed method allows the use of hierarchical SCED algorithms in the absence of a coordinator and can disable malicious participants. Numerical results show the robustness and necessity of the proposed blockchain-based SCED algorithm, by comparing the SCED results with and without blockchain.

On-Line Inertia Estimation for Synchronous and Non-Synchronous Devices

Abstract: This paper proposes an on-line estimation method able to track the inertia of synchronous machines as well as the equivalent, possibly time-varying inertia from the converter-interfaced generators. For power electronics devices, the droop gain of the Fast Frequency Response (FFR) is also determined as a byproduct of the inertia estimation. The proposed method is shown to be robust against noise and to track accurately the inertia of synchronous generators, virtual synchronous generators with constant and adaptive inertia, and wind power plants with inclusion of energy storage-based frequency control.

Hybrid AC/DC Transmission Expansion Planning Considering HVAC to HVDC Conversion Under Renewable Penetration

Abstract: In this paper, a dynamic (i.e., multi-year) hybrid model is presented for Transmission Expansion Planning (TEP) utilizing the High Voltage Alternating Current (HVAC) and multi terminal Voltage Sourced Converter (VSC)-based High Voltage Direct Current (HVDC) alternatives. In addition to new HVAC and HVDC lines, the possibility of converting existing HVAC transmission lines to HVDC lines is considered in the proposed model. High shares of renewable resources are integrated into the proposed hybrid AC/DC TEP model. Due to the intermittency of renewable resources, the planning of large-scale Energy Storage (ES) devices is considered. In order to accurately estimate the total TEP costs and hence capturing the scenarios of load and renewable generation uncertainty, using a clustering approach, each year of the planning horizon is replaced with four representative days. The proposed model is formulated as a Mixed-Integer Linear Programming (MILP) problem. Using Benders Decomposition (BD) algorithm, the proposed model is decomposed into a Master investment problem to handle the decision variables, and Sub-problems to check the feasibility of master problem solution and optimize the operation and ES investment cost. Three test systems are used as case studies to demonstrate the effectiveness of the proposed hybrid AC/DC TEP model.

DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flow

Abstract: We develop DeepOPF as a Deep Neural Network (DNN) approach for solving security-constrained direct current optimal power flow (SC-DCOPF) problems, which are critical for reliable and cost-effective power system operation. DeepOPF is inspired by the observation that solving SC-DCOPF problems for a given power network is equivalent to depicting a high-dimensional mapping from the load inputs to the generation and phase angle outputs. We first train a DNN to learn the mapping and predict the generations from the load inputs. We then directly reconstruct the phase angles from the generations and loads by using the power flow equations. Such a predict-and-reconstruct approach reduces the dimension of the mapping to learn, subsequently cutting down the size of the DNN and the amount of training data needed. We further derive a condition for tuning the size of the DNN according to the desired approximation accuracy of the load-generation mapping. We develop a post-processing procedure based on $\ell _1$ -projection to ensure the feasibility of the obtained solution, which can be of independent interest. Simulation results for IEEE test cases show that DeepOPF generates feasible solutions with less than 0.2% optimality loss, while speeding up the computation time by up to two orders of magnitude as compared to a state-of-the-art solver.

Placing Grid-Forming Converters to Enhance Small Signal Stability of PLL-Integrated Power Systems

Abstract: The modern power grid features the high penetration of power converters, which widely employ a phase-locked loop (PLL) for grid synchronization. However, it has been pointed out that PLL can give rise to small-signal instabilities under weak grid conditions. This problem can be potentially resolved by operating the converters in grid-forming mode, namely, without using a PLL. Nonetheless, it has not been theoretically revealed how the placement of grid-forming converters enhances the small-signal stability of power systems integrated with large-scale PLL-based converters. This paper aims at filling this gap. Based on matrix perturbation theory, we explicitly demonstrate that the placement of grid-forming converters is equivalent to increasing the power grid strength and thus improving the small-signal stability of PLL-based converters. Furthermore, we investigate the optimal locations to place grid-forming converters by increasing the smallest eigenvalue of the weighted and Kron-reduced Laplacian matrix of the power network. The analysis in this paper is validated through high-fidelity simulation studies on a modified two-area test system and a modified 39-bus test system. This paper potentially lays the foundation for understanding the interaction between PLL-based (i.e., grid-following) converters and grid-forming converters, and coordinating their placements in future converter-dominated power systems.

Coordinated Optimal Voyage Planning and Energy Management of All-Electric Ship With Hybrid Energy Storage System

Abstract: All-electric (AES) ship power system (SPS) generally employs energy storage (ESS) to improve operation efficiency, redundancy, and flexibility while reducing environmental impacts. Depending on the operating characteristic, ramp rate, and load variation of the SPS, single or hybrid energy storage systems (HESS) with different operating characteristics are utilized to prevent frequent cycling, high depth of discharge (DOD), and accelerated degradation. Hence, this research work focuses on the coordination of optimal voyage, and multi-objective energy management of AES with HESS to optimize the vessel route, operation cost, emission, and degradation of ESS. The optimal route planning is carried out as a dynamic programming problem based on the sea states, and augmented $\epsilon$ -constraint and reactive approach to energy management strategy are adopted to solve the energy dispatch of the AES while addressing the uncertainty present in the load forecasting. The simulation results indicate that the voyage planning with the consideration of the sea states has strong influences on the path taken and velocity of the AES which in term will influence the propulsion power requirement. It also indicates that the operating characteristic of the HESS and uncertainty present in the load forecasting should be carefully addressed to achieve a realistic operation schedule.

A Reliability-Constrained Expansion Planning Model for Mesh Distribution Networks

Abstract: To achieve high reliability, the urban distribution networks are mesh-constructed and radial-operated, in which the outage load can be restored to adjacent feeders via tie-lines after faults. Conventionally, iterative optimization-simulation methods and heuristics are adopted for distribution network planning, which cannot guarantee global optimality. Besides, existing reliability-constrained planning model cannot explicitly assess the reliability indices for mesh distribution networks, so the resulted plan scheme may be overly invested. In this paper, we propose a novel multistage expansion planning model for mesh distribution networks, in which reliability assessment is explicitly implemented as constraints. The different investment/reliability preferences for buses are also customized. Specifically, post-fault load restoration between feeders through tie-lines is modeled as a case of post-fault network reconfiguration. The planning model is then cast as an instance of mixed-integer linear programming and can be effectively solved by off-the-shelf solvers. We use a 54-node system to test the performance of proposed model. Simulation results show the effectiveness and flexibility of this methodology.

Distributed Distributionally Robust Dispatch for Integrated Transmission-Distribution Systems

Abstract: This paper proposes a new distributed dispatch scheme to realize efficient coordinated real-time dispatch of the coupled transmission grid and active distribution grids (ADGs). In the proposed scheme, on the one hand, the concept of optimal power exchange interval is introduced to coordinate the transmission grid and ADGs so that the reserve capacity support from ADGs can be incorporated in the dispatch optimization of the transmission grid. On the other hand, uncertainties of renewable distributions are considered to ensure the robustness of dispatch decisions. With the analytical target cascading (ATC) method, the centralized distributionally robust dispatch model for the integrated transmission-distribution system is decoupled, leading to a number of independent small local optimization problems for the transmission grid and ADGs. Meanwhile, the diagonal quadratic approximation is adopted to develop an iterative coordination strategy where all local optimization problems are solved in a parallel manner, increasing the computational efficiency. By using the constrained cost variable technique (CCV) and a new affine policy, the original non-convex dispatch model is reformulated as a linear optimization problem, which ensures the convergence of the iterative process and further reduces the computational burden. Case studies on three test systems verify the effectiveness and efficiency of the proposed scheme.

Data-Driven Optimal Power Flow: A Physics-Informed Machine Learning Approach

Abstract: This paper proposes a data-driven approach for optimal power flow (OPF) based on the stacked extreme learning machine (SELM) framework. SELM has a fast training speed and does not require the time-consuming parameter tuning process compared with the deep learning algorithms. However, the direct application of SELM for OPF is not tractable due to the complicated relationship between the system operating status and the OPF solutions. To this end, a data-driven OPF regression framework is developed that decomposes the OPF model features into three stages. This not only reduces the learning complexity but also helps correct the learning bias. A sample pre-classification strategy based on active constraint identification is also developed to achieve enhanced feature attractions. Numerical results carried out on IEEE and Polish benchmark systems demonstrate that the proposed method outperforms other alternatives. It is also shown that the proposed method can be easily extended to address different test systems by adjusting only a few hyperparameters.

Transmission Planning With Battery-Based Energy Storage Transportation For Power Systems With High Penetration of Renewable Energy

Abstract: Battery-based Energy Storage Transportation (BEST) is the transportation of modular battery storage systems via train cars or trucks representing an innovative solution for a) enhancing Variable Renewable Energy (VRE) utilization and load shifting, and b) providing a potential alternative for managing transmission congestions. This paper focuses on point b) and proposes a long-term transmission-planning model coordinated with both stationary and mobile storage units. The planning-problem objective function minimizes the total system cost, i.e., the sum of i) the investment cost of candidate transmission lines, stationary and mobile storage systems, and ii) the operation cost, including conventional generating units fuel consumption, load shedding penalty and BEST transportation costs. An alternative approach for BEST vehicle scheduling problem is implemented. The contribution lies in the accomplishment of the spatial-temporal scheduling of the mobile storage units by including the Number-of-nonzero mathematical function in the optimization model set of constraints instead of using additional binary variables as generally accomplished. The identification of either storage systems optimal location, or both optimal location and size of storage systems is also allowed. BEST usefulness is analyzed and discussed for a test-system emulating a reals system in China-Northwestern-grid with high VRE penetration divided in five regional areas, of which the most promising one for BEST implementation is identified.

Convex Optimization of Integrated Power-Gas Energy Flow Model With Applications to Probabilistic Energy Flow

Abstract: Energy flow calculation is a fundamental problem of the integrated power and gas system (IPGS) operation and planning. However, the nonlinear gas flow model introduces major challenges to the energy flow calculation. In this paper, we propose a tractably convex optimization model to solve the energy flow problem in IPGSs. It is demonstrated that the proposed optimization model has the same optimal solution as the original nonlinear steady energy flow model. Also, piecewise linearization is adopted to tightly linearize the nonlinear objective function of the model, which transforms the formulated convex optimization into a linear program one. Thus, the computation complexity of the proposed energy flow model is significantly reduced as compared with the existing methods. In addition, the proposed model can be extended to probabilistic energy flow estimation. Extensive case studies are conducted to validate the effectiveness of the proposed energy flow model using three IPGSs.

Energy Management of Grid Interconnected Multi-Microgrids Based on P2P Energy Exchange: A Data Driven Approach

Abstract: Grid interconnected multi-microgrids provides potential benefits to the consumers, where the microgrids (MGs) equipped with distributed generators (DGs), energy storage systems (ESSs), and diesel generators. However, intermittency of DGs, high cost of ESSs, and depleting fossil fuels are the major challenges for the economic operation of interconnected multi-microgrids. One potential way to address these challenges is to develop an energy management strategy (EMS) for the grid interconnected multi-microgrids. This paper proposes an EMS to reduce consumer energy consumption cost (ECC) using fuzzy-based peer-to-peer (P2P) energy exchange algorithm with dynamic pricing. In this context, the MGs consumers load power demand (LPD) and DGs output behaviors are modeled using random vector functional link network approach to predict future time slot values. Then, a fuzzy-based P2P energy exchange algorithm is developed to enable the surplus energy transfer to grid and/or MGs with dynamic pricing. Furthermore, an ESS charging/discharging energy control and diesel generator turn on strategies are developed based on the MGs deficit power. Then, the MGs consumer LPD reduction strategy is implemented based on the consumer ECC margin and energy consumption index. Finally, an EMS is proposed that includes on demand-supply strategy and consumer energy consumption cost reduction strategy based on the future time slot values. The novelty of the proposed work lies within the energy management of grid interconnected multi-microgrids and the reduction of consumers ECC through surplus energy transfer to grid and/or MGs using fuzzy-based P2P energy exchange algorithm with dynamic pricing. Historical data are used to demonstrate the effectiveness of the proposed EMS for grid interconnected multi-microgrids.

Optimal Stochastic Operation of Integrated Electric Power and Renewable Energy With Vehicle-Based Hydrogen Energy System

Abstract: The renewable energy-based hydrogen production can lead to the integrated electric power and hydrogen system (IPHS) and offer a pathway to a sustainable energy utilization. Hydrogen is mainly transported via hydrogen tube trailers (HTs), making the hydrogen energy system (HES) operation quite different from those of other energy technologies. This paper proposes an optimal IPHS operation strategy which utilizes HT for transportation. The proposed strategy coordinates hydrogen generation, transportation, and storage stages considering constrained operations of electric power system (EPS), transportation system, and variable renewable energy. The proposed solution method is based on the alternating direction method of multipliers (ADMM) in which HES and EPS constraints are managed individually and the respective solutions are coordinated accordingly. The case studies using the modified IEEE-RTS79 have verified the validity of the proposed IPHS model and its solution method and confirmed the necessity of considering HES in enhancing the EPS operation. The synergies between EPS and HES are studied via numerical examples and the impact of the flexibilities in hydrogen generation, transportation and demand are highlighted.

Co-Optimizing Virtual Power Plant Services Under Uncertainty: A Robust Scheduling and Receding Horizon Dispatch Approach

Abstract: Market and network integration of distributed energy resources can be facilitated by their coordination within a virtual power plant (VPP). However, VPP operation subject to network limits and different market and physical uncertainties is a challenging task. This paper introduces a framework that co-optimizes the VPP provision of multiple market (e.g., energy, reserve), system (e.g., fast frequency response, inertia, upstream reactive power), and local network (e.g., voltage support) services with the aim of maximizing its revenue. To ensure problem tractability, while accommodating the uncertain nature of market prices, local demand, and renewable output and while operating within local network constraints, the framework is broken down into three sequentially coordinated optimization problems. Specifically, a scenario-based robust optimization for day-ahead resource scheduling, with linearized power flows, and two receding horizon optimizations for close-to-real-time dispatch, with a more accurate second-order cone relaxation of the power flows. The results from a real Australian case study demonstrate how the framework enables effective deployment of VPP flexibility to maximize its multi-service value stack, within an uncertain operating environment, and within technical limits.

Improving Hosting Capacity of Unbalanced Distribution Networks via Robust Allocation of Battery Energy Storage Systems

Abstract: Distribution system operators aim to improve hosting capacity (HC) of distribution networks (DNs) to accommodate more distributed rooftop photovoltaics (PVs). Although PV power generation delivers numerous benefits, power unbalance and voltage rise are two major obstacles that limit the network HC. To mitigate these issues, battery energy storage systems (BESSs) can be applied. Thus, this paper proposes a robustly optimal allocation method for BESSs, which aims to reduce the power unbalance and alleviate the voltage rise, and thus improve the HC of the unbalanced three-phase DNs. Considering that locations and capacities of distributed rooftop PVs are determined by customers, future PV installations are regarded as uncertainties. In addition, to deal with the uncertainties, the proposed BESS allocation problem is formulated as an adaptive robust optimization (ARO) model with integer recourse variables. Accordingly, a solution algorithm which integrates an alternating optimization procedure into a column-and-constraint generation algorithm is developed to efficiently solve the ARO model. With the proposed BESS allocation method, a new perspective on HC improvement is provided, which not only considers the worst power unbalance situation but also satisfies the allowed maximum PV capacity. The simulation results verify high efficiency and solution robustness of the proposed allocation method.

Data-driven Event Identification in the U.S. Power Systems Based on 2D-OLPP and RUSBoosting Trees

Abstract: Accurate event identification is an essential part of situation awareness ability for power system operators. Therefore, this work proposes an integrated event identification algorithm for power systems. First, to obtain and filter suitable inputs for event identification, an event detection trigger based on the rate of change of frequency (RoCoF) is presented. Then, the wave arrival time difference-based triangulation method considering the anisotropy of wave propagation speed is utilized to estimate the location of the detected event. Next, the two-dimensional orthogonal locality preserving projection (2D-OLPP)-based method, which is suitable for multiple types of measured data, is employed to achieve higher effectiveness in extracting the event features compared with traditional one-dimensional projection and principle component analysis (PCA). Finally, the random undersampling boosted (RUSBoosted) trees-based classifier, which can mitigate the data sample imbalance issue, is utilized to identify the type of the detected event. The proposed approach is demonstrated using the actual measurement data of U.S. power systems from FNET/GridEye. Comparison results show that the proposed event identification algorithm can achieve better performance than existing approaches.

Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France

Abstract: The coronavirus disease 2019 (COVID-19) pandemic has urged many governments in the world to enforce a strict lockdown where all nonessential businesses are closed and citizens are ordered to stay at home. One of the consequences of this policy is a significant change in electricity consumption patterns. Since load forecasting models rely on calendar or meteorological information and are trained on historical data, they fail to capture the significant break caused by the lockdown and have exhibited poor performances since the beginning of the pandemic. In this paper we introduce two methods to adapt generalized additive models, alleviating the aforementioned issue. Using Kalman filters and fine-tuning allows to adapt quickly to new electricity consumption patterns without requiring exogenous information. The proposed methods are applied to forecast the electricity demand during the French lockdown period, where they demonstrate their ability to significantly reduce prediction errors compared to traditional models. Finally, expert aggregation is used to leverage the specificities of each predictions and enhance results even further.

Optimal Solution Analysis and Decentralized Mechanisms for Peer-to-Peer Energy Markets

Abstract: This paper studies the optimal clearing problem for prosumers in peer-to-peer (P2P) energy markets. It is proved that if no trade weights are enforced and the communication structure between successfully traded peers is connected, then the optimal clearing price and total traded powers in P2P market are the same with that in the pool-based market. However, if such communication structure is unconnected, then the P2P market is clustered into smaller P2P markets. If the trade weights are imposed, then the derived P2P market solutions can be significantly changed. Next, a novel decentralized optimization approach is proposed to derive a trading mechanism for P2P markets, based on the alternating direction method of multipliers (ADMM) which naturally fits into the bidirectional trading in P2P energy systems and converges reasonably fast. Analytical formulas of variable updates reveal insightful relations for each pair of prosumers on their individually traded prices and powers with their total traded powers. Further, based on those formulas, decentralized learning schemes for tuning parameters of prosumers cost functions are proposed to attain successful trading with total traded power amount as desired. Case studies on a synthetic system and the IEEE European Low Voltage Test Feeder are then carried out to verify the proposed approaches.

Dynamic State Estimation for Power System Control and Protection IEEE Task Force on Power System Dynamic State and Parameter Estimation

Abstract: Dynamic state estimation (DSE) accurately tracks the dynamics of a power system and provides the evolution of the system state in real-time. This paper focuses on the control and protection applications of DSE, comprehensively presenting different facets of control and protection challenges arising in modern power systems. It is demonstrated how these challenges are effectively addressed with DSE-enabled solutions. As precursors to these solutions, reformulation of DSE considering both synchrophasor and sampled value measurements and comprehensive comparisons of DSE and observers have been presented. The usefulness and necessity of DSE based solutions in ensuring system stability, reliable protection and security, and resilience by revamping of control and protection methods are shown through examples, practical applications, and suggestions for further development.

A Lagrange Multiplier Based State Enumeration Reliability Assessment for Power Systems With Multiple Types of Loads and Renewable Generations

Abstract: With the integration of multiple types of loads and renewable generations, the number of system states significantly grows. As a result, running optimal power flow (OPF) to analyze a myriad of system states is challenging and this seriously restricts the efficiency of the state enumeration method. To address that, this paper proposes a Lagrange Multiplier based State Enumeration (LMSE) approach to accelerate the analysis without loss of accuracy. The core idea is to directly obtain the optimal load shedding of contingency states by Lagrange multiplier-based functions, rather than the time-consuming OPF algorithms. This approach can also be conveniently integrated with the impact-increment method and the clustering technique for further efficiency enhancement. Case studies are performed on the RTS-79 and IEEE 118-bus systems considering multiple types of loads, photovoltaics (PVs), and wind turbines (WTs). Results indicate that the proposed method can significantly reduce the computing time without compromising the calculation accuracy.

Fully Distributed Robust Reserve Scheduling for Coupled Transmission and Distribution Systems

Abstract: 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 for the coupled transmission and distribution networks (CTD) is proposed in this paper. This model coordinates the generation resources both in the normal state and re-dispatch state to enhance the cost-effectiveness and reliability in a system perspective. A fully distributed framework based on the alternating direction method of multipliers (ADMM) is employed to solve this problem in a decentralized way. As the regional problem of the transmission system is non-convex, a two-layer iterative process (TIP) is presented to enhance the convergence property of standard ADMM. Since only the boundary information needs to be exchanged between the transmission and distribution systems, the communication burden is light and regional information is encrypted. Case studies on the T6D2 and T118D10 systems demonstrate the effectiveness of the proposed model and approach.