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This presentation provides updates to the SEPA cybersecurity working group related to IEEE 1547.3 Guide for DER Cybersecurity, DOE Laboratory Coordination Committee, and other standards development efforts.
This report proposes a generic model specification of a photovoltaic inverter applied in transmission-connected plants for implementation in an electromagnetic transients simulator. The model specification contains the necessary control loops to meet the fault ride-through performance requirements in the IEEE 2800-2022 standard. The report details the implementation and example parameterization of the model in PSCAD™. Simulation results verify the model meets the performance requirements. Comparisons of the model’s response to laboratory test measurements of a commercial 2.2 MVA storage inverter show the model can reasonably predict the response of commercial equipment.
Driven by the need to capture the electromagnetic transients of transmission lines, inverter switching behavior, and detailed control systems, electromagnetic transient (EMT) studies have become increasingly important in industry, such as IBR interconnection study and fault study. However, original equipment manufacturer (OEM) inverter models typically include extensive parameters and proprietary settings that are unavailable to protection engineers. This paper introduces a data-driven, reduced-order model (ROM) developed as a PSCAD library component for use in EMT-based fault studies. The ROM replicates key OEM model behaviors without requiring detailed knowledge of control design or parameterization. The accompanying Python automation scripts streamline data generation, parameter fitting, and validation. The ROM's performance is demonstrated through comparison with both IEEE 2800-compliant and non-compliant OEM models in a real-world power system. Relay responses show nearly identical results, while simulation runtime is reduced by an average of 32.8\%, highlighting the ROM's practicality for protection engineers.
This study reviewed existing conventional and nonconventional protection schemes for grid-connected and islanded mode operations in North American microgrid projects. The microgrid projects investigated in this study used different types of distributed energy resources (DERs) and integrated hydropower/diesel generators, gas/steam/wind turbines, and photovoltaic systems with energy storage. In this work, conventional protection schemes were defined as those within the IEEE Standard C37.2-2008, whereas nonconventional schemes were those not defined within this standard. The pros and cons of conventional and nonconventional protection schemes were discussed in detail. The overvoltage, undervoltage, and frequency elements were the most common conventional protection schemes applied in microgrid projects in North America. These protection elements were used to detect the islanded conditions and faults that could not be sensed by overcurrent relays because of small fault currents contributed by low-inertia DERs and power-electronic sources. Directional overcurrent elements were used to distinguish between external (grid) and internal (microgrid) faults. Adaptive protection was the most popular nonconventional protection scheme applied to the microgrid projects. In conclusion, different types of DERs and operational modes must be considered in order to address the protection and control challenges of each microgrid and to obtain the best technical and economical solution.
Microgrids can be operated in a utility-connected mode with the neighboring distribution system or in an islanded mode in separation with the distribution system. As the major disasters occur more and more often, islanded microgrid is proposed as a possible solution to serve essential loads until the utility service is restored. This paper evaluates the feasibility of using microgrids as a resiliency resource by a field test. A control scheme, integrating droop control and feedback control, is applied on a modified IEEE 13-node test feeders. The simulation results validate the proposed control scheme and the transient stability
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A power distribution system operates most efficiently with voltage deviations along a feeder kept to a minimum and must ensure all voltages remain within specified limits. Recently with the increased integration of photovoltaics, the variable power output has led to increased voltage fluctuations and violation of operating limits. This study proposes an optimization model based on a recently developed heuristic search method, grey wolf optimization, to coordinate the various distribution controllers. Several different case studies on IEEE 33 and 69 bus test systems modified by including tap changing transformers, capacitors and photovoltaic solar panels are performed. Simulation results are compared to two other heuristic-based optimization methods: harmony search and differential evolution. Finally, the simulation results show the effectiveness of the method and indicate the usage of reactive power outputs of PVs facilitates better voltage magnitude profile.
Increased penetration of renewables, along with uncertainties associated with them, have transformed how power systems are operated. High levels of uncertainty means that it is not longer possible to guarantee operational feasibility with certainty, instead constraints are required to be satisfied with high probability. We present a chance-constrained economic dispatch model that efficiently integrates energy storage and high renewable penetration to satisfy renewable portfolio requirements. Specifically, it is required that wind energy contributes at least a prespecified ratio of the total demand and that the scheduled wind energy is dispatchable with high probability. We develop an approximated partial sample average approximation (PSAA) framework to enable efficient solution of large-scale chanceconstrained economic dispatch problems. Computational experiments on the IEEE-24 bus system show that the proposed PSAA approach is more accurate, closer to the prescribed tolerance, and about 100 times faster than sample average approximation. Improved efficiency of our PSAA approach enables solution of WECC-240 system in minutes.
Here, we identify the desired characteristics and parameters of a β>1 magnetized plasma, possibly with highly tangled, open field lines, that could be a suitable target to be compressed to fusion conditions by a spherically imploding plasma liner (Hsu et al. in IEEE Trans Plasma Sci 40:1287, 2012) formed by merging hypersonic plasma jets. This concept is known as plasma-jet-driven magneto-inertial fusion (PJMIF). We set requirements on the target and liner such that (a) compressional heating dominates over thermal transport in the target, and (b) magnetic amplification due to compression dominates over dissipation over the entire implosion. We also evaluate the requirements to avoid drift-instability-induced anomalous transport and current-driven anomalous resistivity in the target. Next, we describe possible approaches to create such a magnetized, β>1 plasma target. Finally, assuming such a target can be created, we evaluate the feasibility of a proof-of-concept experiment using presently achievable plasma jets to demonstrate target compressional heating at a plasma-liner kinetic energy of ≲100kJ (a few hundred times below that needed in a PJMIF reactor).
Stellarators may have advantages for certain liquid metal options as Plasma Facing Components (PFC) for divertor targets and first walls due to the wide range of possible magnetic configurations, which additionally are free of disruptions and fast field variations. In a previous work (V. Queral et al., IEEE Trans. Plasma Sci. 52, 2024), a concept of stellarator reactor (ASTER-CP) based on swirling Li-molten salts and liquid lithium floating on the molten salt as PFC was presented. The divertor matters were not studied then and, thus, they are being studied and experimentally tested now. The ASTER-CP reactor concept, the initial liquid metal experiments and potential concepts for the ASTER-CP divertor and first wall are reported. Concerning the experiments, several small scale experiments of galinstan in a small rotating cylinder under magnetic field have been produced, including one experiment with high viscosity galinstan-mixture for increased thickness of layer. An experiment of floating lithium on the molten salt LiCl-PbCl2 gave fast volatilization/decomposition of the molten salt. Particularly for divertors, the traditional free-flow, Capillary Porous Systems and ‘divertorlets’ have been studied for application to ASTER-CP. Surface waves (hot spots), lack of enough surface fluid turbulence and excessive fluid speed are the main issues found in fast free-flow. The perhaps original concept of Distributed Divertor and Equi-power Surface is tentatively proposed and studied, taking advantage of stellarator fields and low recycling regime.
Here this article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Recent advances show promising results for model-free DRL-based methods in power systems control problems. But in power systems applications, these model-free methods have certain issues related to training time (clock time) and sample efficiency; both are critical for making state-of-the-art DRL algorithms practically applicable. DRL-agent learns an optimal policy via a trial-and-error method while interacting with the real-world environment. It is also desirable to minimize the direct interaction of the DRL agent with the real-world power grid due to its safety-critical nature. Additionally, the state-of-the-art DRL-based policies are mostly trained using a physics-based grid simulator where dynamic simulation is computationally intensive, lowering the training efficiency. We propose a novel model-based DRL framework where a deep neural network (DNN)-based dynamic surrogate model (SM), instead of a real-world power grid or physics-based simulation, is utilized within the policy learning framework, making the process faster and more sample efficient. However, having stable training in model-based DRL is challenging because of the complex system dynamics of large-scale power systems. We addressed these issues by incorporating imitation learning to have a warm start in policy learning, reward-shaping, and multi-step loss in surrogate model training. Finally, we achieved 97.5% reduction in samples and 87.7% reduction in training time for an application to the IEEE 300-bus test system.
Here, the high-confinement mode (H-mode) is very important for the fusion performance of ITER and future fusion reactors. The H-mode pedestal is a region featuring strongly reduced turbulence and transport just inside the limiting flux surface with strong plasma gradients, which drive edge localized modes (ELMs) in tokamaks. The ELMs, however, are quasiperiodic relaxations of the pedestal, resulting in a series of hot plasma eruptions similar to magnetospheric substorms, solar and stellar flares, which could potentially damage the ITER divertor plates and first walls. To mitigate and suppress the ELMs in H-mode plasmas, the fusion community has tried to develop effective actuators over the past two decades. Two separate excellent inventions have been awarded to recognize their achievements. The 2018 APS excellence award in Plasma Physics Research was given to Drs. Todd E. Evans, Max E. Fenstermacher, and Richard Alan Moyer for their first experimental demonstration of the stabilization of ELMs in high-confinement diverted discharges by application of very small edge-resonant magnetic perturbations, which led to the adoption of suppression coils in the ITER design. The 2018 Fusion Technology Award from the IEEE’s Nuclear and Plasma Science Society (NPSS) was given to Dr. Larry Baylor for his work designing the fueling, pumping and disruption mitigation system for the US ITER Project and for the implementation of pellet ELM mitigation for ITER.
Increasing variable generation penetration and the consequent increase in short-term variability makes energy storage technologies look attractive, especially in the ancillary market for providing frequency regulation services. This paper presents slow dynamics model for compressed air energy storage and battery storage technologies that can be used in automatic generation control studies to assess the system frequency response and quantify the benefits from storage technologies in providing regulation service. The paper also represents the slow dynamics model of the power system integrated with storage technologies in a complete state space form. The storage technologies have been integrated to the IEEE 24 bus system with single area, and a comparative study of various solution strategies including transmission enhancement and combustion turbine have been performed in terms of generation cycling and frequency response performance metrics.
Abstract Our recent work (Ayral et al. in Proceedings of IEEE computer society annual symposium on VLSI, ISVLSI, pp 138–140, 2020. 10.1109/ISVLSI49217.2020.00034 ) showed the first implementation of the Quantum Divide and Compute (QDC) method, which allows to break quantum circuits into smaller fragments with fewer qubits and shallower depth. This accommodates the limited number of qubits and short coherence times of quantum processors. This article investigates the impact of different noise sources—readout error, gate error and decoherence—on the success probability of the QDC procedure. We perform detailed noise modeling on the Atos Quantum Learning Machine, allowing us to understand tradeoffs and formulate recommendations about which hardware noise sources should be preferentially optimized. We also describe in detail the noise models we used to reproduce experimental runs on IBM’s Johannesburg processor. This article also includes a detailed derivation of the equations used in the QDC procedure to compute the output distribution of the original quantum circuit from the output distribution of its fragments. Finally, we analyze the computational complexity of the QDC method for the circuit under study via tensor-network considerations, and elaborate on the relation the QDC method with tensor-network simulation methods.
This chapter describes a framework to synthesize provably stable local Volt/Var controllers for distributed energy resources (DERs) in power distribution grids (DGs). The goal is to control the reactive power injections of DERs to improve the system performance as quantified by a generic optimal reactive power flow (ORPF) problem. To achieve this, we jointly design for each DER the control function, which prescribes the reactive power update rule, and the equilibrium function, which approximates the ORPF solutions from local measurements of voltages and powers. We provide conditions on the equilibrium functions and the control parameters ensuring the stability of the closed-loop system. In particular, we discuss the trade-offs between each set of conditions accounting for practical considerations, like fully exploiting the DERs' generation capabilities and reducing the optimality gap. These conditions are then translated into learning constraints on the neural networks' parameters that are enforced in the training phase. We validate our framework with numerical simulations on the IEEE 37-bus network and through a comparison with an optimized version of standard piece wise linear control rules.
The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was compared through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Lastly, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.
The integration of distributed generators (DGs) exacerbates the feeder power flow fluctuation and load unbalanced condition in active distribution networks (ADNs). The unbalanced feeder load causes inefficient use of network assets and network congestion during system operation. The flexible interconnection based on the multi-terminal soft open point (SOP) significantly benefits the operation of ADNs. The multi-terminal SOP, which is a controllable power electronic device installed to replace the normally open point, provides accurate active and reactive power flow control to enable the flexible connection of feeders. An enhanced SOCP-based method for feeder load balancing using the multi-terminal SOP is proposed in this paper. Furthermore, by regulating the operation of the multi-terminal SOP, the proposed method can mitigate the unbalanced condition of feeder load and simultaneously reduce the power losses of ADNs. Then, the original non-convex model is converted into a second-order cone programming (SOCP) model using convex relaxation. In order to tighten the SOCP relaxation and improve the computation efficiency, an enhanced SOCP-based approach is developed to solve the proposed model. Finally, case studies are performed on the modified IEEE 33-node system to verify the effectiveness and efficiency of the proposed method.
Concentrating solar power (CSP) plants are able to provide both renewable energy and operational flexibility at the same time due to its thermal energy storage (TES). It is ideal generation to power systems lacking in flexibility to accommodate variable renewable energy (VRE) generation such as wind power and photovoltaics. However, its investment cost currently is too high to justify its benefit in terms of providing renewable energy only. In this paper we evaluate the economic benefit of CSP in high renewable energy penetrated power systems from two aspects: generating renewable energy and providing operational flexibility to help accommodating VRE. In order to keep the same renewable energy penetration level during evaluation, we compare the economic costs between the system with a high share of VRE and another in which some part of the VRE generation is replaced by CSP generation. The generation cost of a power system is analyzed through chronological operation simulation over a whole year. The benefit of CSP is quantified into two parts: (1) energy benefit - the saving investment of substituted VRE generation and (2) flexibility benefit - the reduction in operating cost due to substituting VRE with CSP. The break-even investment cost of CSP is further discussed. The methodology is tested on a modified IEEE RTS-79 system. The economic justifications of CSP are demonstrated in two practical provincial power systems with high penetration of renewable energy in northwestern China, Qinghai and Gansu, where the former province has massive inflexible thermal power plants but later one has high share of flexible hydro power. The results suggest that the CSP is more beneficial in Gansu system than in Qinghai. The levelized benefit of CSP, including both energy benefit and flexibility benefit, is about 0.177-0.191 $/kWh in Qinghai and about 0.238-0.300 $/kWh in Gansu, when replacing 5-20% VRE generation with CSP generation.