Operation Of Electric Power Grid Research Articles

Reinforcement-Learning-Based Proactive Control for Enabling Power Grid Resilience to Wildfire

Industrial electric power grid operation subject to an extreme event requires decision-making by human operators under stressful conditions. Decision making using system data informatics under adverse dynamic events, especially if forecasted, should be supplemented by intelligent proactive control. Power transmission system operation during wildfires requires resiliency-driven proactive control for load shedding, line switching, and resource allocation considering the dynamics of the wildfire and failure propagation to minimize the impact on the system. However, the possible number of line and load switching in an extensive industrial system during an event make traditional prediction-driven and stochastic approaches computationally intractable, leading operators to often use pre-planned or greedy algorithms. In this work, we model and solve the proactive control problem as a Markov decision process and introduce an integrated testbed for spatio-temporal wildfire propagation and proactive power-system operation. Our approach allows the controller to provide setpoints for all generation fleets in the power grid. We evaluate our approach utilizing the IEEE test system mapped onto a hypothetical terrain. Our results show that the proposed approach can help the operator to reduce load outage during an extreme event. It reduces power flow through lines that are to be de-energized, and adjusts the load demand by increasing power flow through other lines.

Co-Simulation of Electric Power Distribution Systems and Buildings including Ultra-Fast HVAC Models and Optimal DER Control

Smart homes and virtual power plant (VPP) controls are growing fields of research with potential for improved electric power grid operation. A novel testbed for the co-simulation of electric power distribution systems and distributed energy resources (DERs) is employed to evaluate VPP scenarios and propose an optimization procedure. DERs of specific interest include behind-the-meter (BTM) solar photovoltaic (PV) systems as well as heating, ventilation, and air-conditioning (HVAC) systems. The simulation of HVAC systems is enabled by a machine learning procedure that produces ultra-fast models for electric power and indoor temperature of associated buildings that are up to 133 times faster than typical white-box implementations. Hundreds of these models, each with different properties, are randomly populated into a modified IEEE 123-bus test system to represent a typical U.S. community. Advanced VPP controls are developed based on the Consumer Technology Association (CTA) 2045 standard to leverage HVAC systems as generalized energy storage (GES) such that BTM solar PV is better utilized locally and occurrences of distribution system power peaks are reduced, while also maintaining occupant thermal comfort. An optimization is performed to determine the best control settings for targeted peak power and total daily energy increase minimization with example peak load reductions of 25+%.

Soft Open Points-Assisted Resilience Enhancement of Power Distribution Networks Against Cyber Risks

Cyberattacks are increasingly threatening the normal operation of electric power grids. The purpose of this study is to develop an optimization model for planning and operation of distribution systems to enhance the resilience to cyberattacks. A new distribution system planning method is presented utilizing a Defender-Attacker-Defender (DAD) tri-level model to improve the system resilience to withstand potential disruptions from cyberattacks. In the first level of the model, the system planner optimizes the Soft Open Points (SOPs) installation in the planning stage. The attacker’s objective in the second level of the model is to change the status of the network circuit breakers and jam the communication maximizing disruption of the system. The optimization of Distribution Generators’ (DGs’) outputs, SOPs’ power flow, and topology reconfiguration are comprehensively considered in the third level. The novel model presented in this paper considers hardening measures of the SOPs in response to the attacks on distribution systems. The Column and Constraint Generation (C&CG) algorithm is utilized to solve the proposed DAD model. A procedure is devised to solve the two-layer subproblem (the Attacker-Defender model), with the bottom layer being a Mixed Integer Second Order Cone Programming (MISOCP) problem. Finally, the proposed models are validated in 33-bus and 135-bus test systems. The results show that the installation of SOPs significantly improves the service restoration under cyberattacks. Both the number and location of SOPs influence the extent of restored service, and differing SOP allocation schemes may lead to similar restoration outcomes. The proposed models and techniques could also be extended to deal with other similar problems in industrial cyber-physical systems.

A Unifying Framework of Attention-Based Neural Load Forecasting

Accurate load forecasting is critical for reliable and efficient planning and operation of electric power grids. In this paper, we propose a unifying deep learning framework for load forecasting, which includes time-varying feature weighting, hierarchical temporal attention, and feature-reinforced error correction. Our framework adopts a modular design with good generalization capability. First, the feature-weighting mechanism assigns input features with temporal weights. Second, a recurrent encoder-decoder structure with hierarchical attention is developed as a load predictor. The hierarchical attention enables a similar day selection, which re-evaluates the importance of historical information at each time step. Third, we develop an error correction module that explores the errors and learned feature hidden information to further improve the model's forecasting performance. Experimental results demonstrate that our proposed framework outperforms existing methods on two public datasets and performance metrics, with the feature weighting mechanism and error correction module being critical to achieving superior performance. Our framework provides an effective solution to the electric load forecasting problem, which can be further adapted to many other forecasting tasks.

Bi-level optimization based two-stage market clearing model considering guaranteed accommodation of renewable energy generation

The existing electricity market mechanisms designed to promote the consumption of renewable energy generation complicate network participation in market transactions owing to an unfair market competition environment, where the low cost renewable energy generation is not reflected in the high bidding price of high cost conventional energy generation. This study addresses this issue by proposing a bi-level optimization based two-stage market clearing model that considers the bidding strategies of market players, and guarantees the accommodation of renewable energy generation. The first stage implements a dual-market clearing mechanism that includes a unified market for trading the power generations of both renewable energy and conventional energy units, and a subsidy market reserved exclusively for conventional generation units. A re-adjustment clearing mechanism is then proposed in the second stage to accommodate the power generation of remaining renewable energy units after first stage energy allocations. Each stage of the proposed model is further described as a bi-level market equilibrium problem and is solved using a co-evolutionary algorithm. Finally, numerical results involving an improved IEEE 39-bus system demonstrate that the proposed two-stage model meets the basic requirements of incentive compatibility and individual rationality. It can facilitate the rational allocation of resources, promote the economical operation of electric power grids, and enhance social welfare.

Absence of pure voltage instabilities in the third-order model of power grid dynamics.

Secure operation of electric power grids fundamentally relies on their dynamical stability properties. For the third-order model, a paradigmatic model that captures voltage dynamics, three routes to instability are established in the literature: a pure rotor angle instability, a pure voltage instability, and one instability induced by the interplay of both. Here, we demonstrate that one of these routes, the pure voltage instability, requires infinite voltage amplitudes and is, thus, nonphysical. We show that voltage collapse dynamics nevertheless exist in the absence of any voltage instabilities.

Research and Implementation of Electric Topology Intelligent Checking System Based on Graph Computing

The information system of low-voltage distribution network correctly records the topological structure of distribution network, which is the premise of fine management and safe operation of electric power grid. In the topology, the most important element is the household change relationship [1]. At present, the low-voltage distribution network topology data in the information system is manually entered, so the correctness of the information can not be guaranteed. With the continuous expansion of the scale of power grid, the structure of low-voltage distribution network is complex, the amount of data of marketing measurement [2], GIS and other information systems is increasing rapidly, each information system operates independently, and the data circulation is poor, which makes it difficult to identify the topology data, so it is urgent to carry out topology verification. The method proposed in this paper effectively solves the problem of electric data verification, and carries out corresponding experiments and demonstrations on the results, with high accuracy.

Measurement placement in electric power transmission and distribution grids: Review of concepts, methods, and research needs

Sensing and measurement systems are quintessential to the safe and reliable operation of electric power grids. Their strategic placement is of ultimate importance because it is not economically viable to install measurement systems on every node and branch of a power grid, though they need to be monitored. An overwhelming number of strategies have been developed to meet oftentimes multiple conflicting objectives. The prime challenge in formulating the problem lies in developing a heuristic or an optimization model that, though mathematically tractable and constrained in cost, leads to trustworthy technical solutions. Further, large-scale, long-term deployments pose additional challenges because the boundary conditions change as technologies evolve. For instance, the advent of new technologies in sensing and measurement, as well as in communications and networking, might impact the cost and performance of available solutions and shift initially set conditions. Also, the placement strategies developed for transmission grids might not be suitable for distribution grids, and vice versa, because of unique characteristics; therefore, the strategies need to be flexible, to a certain extent, because no two power grids are alike. Despite the extensive literature on the present topic, the focus of published works tends to be on a specific subject, such as the optimal placement of measurements to ensure observability in transmission grids. There is a dearth of work providing a comprehensive picture for developing optimal placement strategies. Because of the ongoing efforts on the modernization of electric power grids, there is a need to consolidate the status quo while exposing its limitations to inform policymakers, industry stakeholders, and researchers on the research-and-development needs to push the boundaries for innovation.

Situational awareness of coherency behavior of synchronous generators in a power system with utility-scale photovoltaics

The smart grid innovation targets at the problems of integrating large portion of intermittent generation resources such as utility-scale and rooftop photovoltaics (PVs), while ensuring both the security and reliability of the electric power grid operation during severe disturbances. To enhance the operators’ performance, the information processing of the large-scale widely interconnected electric power grid needs to be simplified and presented with situational awareness (SA) in a meaningful way, for timely response by the system operators in control centers. Coherency group identification separates the generators in the system into groups that feature similar behavior, which is a method for grid simplification. Meanwhile, wide area measurement systems (WAMS) using data from phasor measurement units (PMUs) provides a solution – enhanced situational awareness to power operation control centers addressing the concerns of capturing and transmitting dynamic data in near real-time. Based upon these technologies, a synchrophasor based identification of generator coherency groups and new visualizations for SA are proposed and developed for online application with using K-harmonic means clustering. Furthermore, a generator vulnerability index (GVI) and a corresponding power system vulnerability index (SVI) are proposed based on the dynamic coherency groups identified. These indices alert system operators on the coherency behavior of the synchronous generators in the system for variations of PV power and/or under severe system disturbances. Typical results are provided for four different disturbances including sudden PV power increase and decrease, unexpected bus faults on major tie-line (inter-area) transmission corridors. In summary, new algorithms, indices and visualization tools have been developed to enhance SA and provide actionable information to system operators in control centers in the presence of variable utility-scale PV power in a power system.

A Finite Difference Time Domain Investigation of Electric Field Enhancements Along Ocean‐Continent Boundaries During Space Weather Events

AbstractGeomagnetic disturbances caused by solar storms have the potential to create large‐scale geomagnetically induced currents in long conductors at the Earth's surface. These may disrupt the operation of electric power grids and cause blackouts. Ocean‐continent boundary regions are of particular concern because of the sharp contrast between the higher ocean electrical conductivity compared to the lower continental conductivity. This contrast may generate high‐amplitude geoelectric fields and cause power grids in coastal regions to be more vulnerable to space weather hazards. Previously, analytical calculations were used to estimate geomagnetically induced currents at ocean‐continent boundaries. However, for the analytical equations to be solvable, the physics and geometries of the problem were simplified. As a result of these simplifications and due to a lack of published measurements examining this issue in coastal regions, it is difficult to know for sure whether there are unique hazards to electric power grids at ocean‐continent boundaries. In this paper, a grid‐based, time domain modeling approach is used to solve the complete Maxwell's equations, which permits accommodation of (1) the complete physics of the propagating electromagnetic fields from disturbed ionospheric currents through the air and into the lithosphere and even into the ocean via the skin effect and (2) more realistic coastal geometries. Using this more robust approach, in the variety of scenarios studied in this paper, only a local enhancement of the electric fields was observed, which are expected to only pose potential hazards to power grids only a local scale near ocean‐continent boundaries.

Optimal Placement of Virtual Inertia in Power Grids

A major transition in the operation of electric power grids is the replacement of synchronous machines by distributed generation connected via power electronic converters. The accompanying "loss of rotational inertia" and the fluctuations by renewable sources jeopardize the system stability, as testified by the ever-growing number of frequency incidents. As a remedy, numerous studies demonstrate how virtual inertia can be emulated through various devices, but few of them address the question of "where" to place this inertia. It is however strongly believed that the placement of virtual inertia hugely impacts system efficiency, as demonstrated by recent case studies. In this article, we carry out a comprehensive analysis in an attempt to address the optimal inertia placement problem. We consider a linear network-reduced power system model along with an H2 performance metric accounting for the network coherency. The optimal inertia placement problem turns out to be non-convex, yet we provide a set of closed-form global optimality results for particular problem instances as well as a computational approach resulting in locally optimal solutions. Further, we also consider the robust inertia allocation problem, wherein the optimization is carried out accounting for the worst-case disturbance location. We illustrate our results with a three-region power grid case study and compare our locally optimal solution with different placement heuristics in terms of different performance metrics.

Methodology for time‐domain estimation of storm time geoelectric fields using the 3‐D magnetotelluric response tensors

AbstractGeoelectric fields at the Earth's surface caused by magnetic storms constitute a hazard to the operation of electric power grids and related infrastructure. The ability to estimate these geoelectric fields in close to real time and provide local predictions would better equip the industry to mitigate negative impacts on their operations. Here we report progress toward this goal: development of robust algorithms that convolve a magnetic storm time series with a frequency domain impedance for a realistic three‐dimensional (3‐D) Earth, to estimate the local, storm time geoelectric field. Both frequency domain and time domain approaches are presented and validated against storm time geoelectric field data measured in Japan. The methods are then compared in the context of a real‐time application.

南方电网在地震灾害下的弹性评估与改进

电网的安全稳定运行对于经济繁荣，国家安全和公共卫生安全至关重要。然而在地震多发地区，高烈度地震的发生可能同时造成电网中的大量设施元件损坏，并导致电网内部和依赖电力供应的基础设施系统发生级联故障。因此，电网的弹性对于确保电力快速恢复和保障受灾人民电力需求至关重要。本文介绍了一种概率性的建模方法以对当前电网的弹性进行量化评估。这种方法包括地震情形生成模型，元件脆弱性模型，电网功能变化模型和元件恢复模型。综合这四个模型可以量化电网的弹性。本文应用上述方法对中国南方电网的弹性进行了评估，并提出了弹性策略以改进电网弹性。 Safe and stable operation of electric power grids is critical to economic prosperity, national security, public health and safety. However, in earthquake-prone areas, a severe earthquake may si-multaneously cause extensive component failures in a power grid and lead to cascading failures within it and across other power-dependent utility systems. Hence, the seismic resilience of power systems is crucial to ensure their rapid recovery and support the needs of the population in disaster areas. This paper introduces a probabilistic modeling approach for quantifying the seismic re-silience of contemporary electric power grids. This approach includes an earthquake scenario generation model, component fragility models, a power grid performance model, and a system restoration model. These coupled four models enable quantifying seismic resilience. This paper applies the proposed approach to the seismic resilience assessment of the China Southern Power Grid, and several resilience strategies have been introduced to display how seismic resilience can be improved.

Guest Editorial Special Issue on “Neural Networks and Learning Systems Applications in Smart Grid”

The electric power grid is spatially and temporally complex, nonconvex, nonlinear, and nonstationary system with uncertainties at many levels. The integration of renewable sources of energy, such as wind and solar farms, energy storage, and plug-in hybrid electric vehicles, further adds complexity and challenges to efficient, reliable, and safe operation of electric power grids. A smart grid is aimed at improving power system’s reliability, security, sustainability, efficiency, and flexibility through distributed and coordinated intelligence at all levels of the electric power grid—generation, transmission, and distribution. The challenges faced in evolving a smart grid include variable and uncertain generation (such as wind and solar), stochastic load profiles and power flows, cyber attacks (unintentional and malicious), communication latencies and data loss, and so on.

Important Transfer Power Lines Analysis under Large Scale Direct Electricity Purchases

开展大用户直购电能够激发电力市场的潜力，是电力工业市场化改革的突破口。大规模直购电交易一般为跨区跨省的长距离电力交易，对电网的运行方式和潮流计算产生影响。分析大用户直购电交易的输电路径，将为电网运行稳定性分析和支路阻塞管理提供支持。本文提出一种结合广度优先搜索的灵敏度分析方法，广度优先搜索方法可以搜索出所有直购电交易的输电路径，结合灵敏度分析法得到直购电交易对输电线路的灵敏度。对于大规模的直购电交易，可以计算出多个直购电交易对输电线路的叠加灵敏度，分析直购电交易的重要输电线路。本文以IEEE118节点系统为例进行算例分析，结果表明了该方法的合理性和有效性。 It can stimulate the potential of electric power market to carry out the direct electricity purchase by large consumers, and it is a breakthrough in the electric power industry marketization. Large scale direct electricity purchases are generally inter-district inter-provincial long-distance power transactions and affect the operation mode and power flow calculation of power grid. Transfer power lines analysis of the direct electricity purchase by large consumers will provide support for the stability analysis of the electric power grid operation and congestion management of the power transmission branches. A sensitivity analysis method combined with breadth first search is proposed in this paper. Breadth first search method can search out all transfer power lines of the direct electricity purchases, and then breadth first search method combined with sensitivity analysis can get the sensitivity of the direct electricity purchase by large consumers on the transfer power lines. For large scale direct electricity purchases, the method proposed in this paper can be used to calculate the stack sensitivity of a plurality of direct electricity purchases on the transfer power lines. In this paper, based on the IEEE 118 nodes system as an example for example analysis, the results show the rationality and validity of the proposed method.

Distributed Feasibility Algorithms with Application to Power Flow Problems

Given a network of interconnected nodes, each with its own value (such as a measurement, position or vote) and with constraints between each node and its neighbors, a feasibility algorithm assigns a value to each node such that constraints between neighboring nodes are satisfied simultaneously. This paper presents two novel feasibility algorithms that are based on the Method of Alternating Projections (MAP) and the Projected Consensus algorithm. Our algorithms solve convex feasibility problems by distributing computation among nodes and require only local information exchanges. A well-motivated application discussed throughout the paper is the power flow problem, which is vital to the operation of electric power grids. Although the power flow problem is a non-convex feasibility problem, our algorithms are demonstrated to be effective heuristics using various IEEE test beds.

Decentral Smart Grid Control

Stable operation of complex flow and transportation networks requires balanced supply and demand. For the operation of electric power grids—due to their increasing fraction of renewable energy sources—a pressing challenge is to fit the fluctuations in decentralized supply to the distributed and temporally varying demands. To achieve this goal, common smart grid concepts suggest to collect consumer demand data, centrally evaluate them given current supply and send price information back to customers for them to decide about usage. Besides restrictions regarding cyber security, privacy protection and large required investments, it remains unclear how such central smart grid options guarantee overall stability. Here we propose a Decentral Smart Grid Control, where the price is directly linked to the local grid frequency at each customer. The grid frequency provides all necessary information about the current power balance such that it is sufficient to match supply and demand without the need for a centralized IT infrastructure. We analyze the performance and the dynamical stability of the power grid with such a control system. Our results suggest that the proposed Decentral Smart Grid Control is feasible independent of effective measurement delays, if frequencies are averaged over sufficiently large time intervals.

Estimating zonal electricity supply curves in transmission-constrained electricity markets

Many important electricity policy initiatives would directly affect the operation of electric power networks. This paper develops a method for estimating short-run zonal supply curves in transmission-constrained electricity markets that can be implemented quickly by policy analysts with training in statistical methods and with publicly available data. Our model enables analysis of distributional impacts of policies affecting operation of electric power grid. The method uses fuel prices and zonal electric loads to determine piecewise supply curves, identifying zonal electricity price and marginal fuel. We illustrate our methodology by estimating zonal impacts of Pennsylvania's Act 129, an energy efficiency and conservation policy. For most utilities in Pennsylvania, Act 129 would reduce the influence of natural gas on electricity price formation and increase the influence of coal. The total resulted savings would be around 267 million dollars, 82 percent of which would be enjoyed by the customers in Pennsylvania. We also analyze the impacts of imposing a $35/ton tax on carbon dioxide emissions. Our results show that the policy would increase the average prices in PJM by 47–89 percent under different fuel price scenarios in the short run, and would lead to short-run interfuel substitution between natural gas and coal.

Self-Organized Synchronization in Decentralized Power Grids

Robust synchronization (phase locking) of power plants and consumers centrally underlies the stable operation of electric power grids. Despite current attempts to control large-scale networks, even their uncontrolled collective dynamics is not fully understood. Here we analyze conditions enabling self-organized synchronization in oscillator networks that serve as coarse-scale models for power grids, focusing on decentralizing power sources. Intriguingly, we find that whereas more decentralized grids become more sensitive to dynamical perturbations, they simultaneously become more robust to topological failures. Decentralizing power sources may thus facilitate the onset of synchronization in modern power grids.

An Advanced Framework for Improving Situational Awareness in Electric Power Grid Operation

AbstractWith the deployment of new smart grid technologies and the penetration of renewable energy in power systems, significant uncertainty and variability is being introduced into power grid operation. Traditionally, the Energy Management System (EMS) operates the power grid in a deterministic mode, and thus will not be sufficient for the future control center in a stochastic environment with faster dynamics. One of the main challenges is to improve situational awareness. This paper reviews the current status of power grid operation and presents a vision of improving wide-area situational awareness for a future control center. An advanced framework, consisting of parallel state estimation, state prediction, parallel contingency selection, parallel contingency analysis, and advanced visual analytics, is proposed to provide capabilities needed for better decision support by utilizing high performance computing (HPC) techniques and advanced visual analytic techniques. Research results are presented to support the proposed vision and framework.