Electrification of transport compounded with climate change will transform hourly load profiles and their response to weather. Power system operators and EV charging stakeholders require such high-resolution load profiles for their planning studies. However, such profiles accounting whole transportation sector is lacking. Thus, we present a novel approach to generating hourly electric load profiles that considers charging strategies and evolving sensitivity to temperature. The approach consists of downscaling annual state-scale sectoral load projections from the multi-sectoral Global Change Analysis Model (GCAM) into hourly electric load profiles leveraging high resolution climate and population datasets. Profiles are developed and evaluated at the Balancing Authority scale, with a 5-year increment until 2050 over the Western U.S. Interconnect for multiple decarbonization pathways and climate scenarios. The datasets are readily available for production cost model analysis. Our open source approach is transferable to other regions.
This paper proposes a model for energy flexibility of cold climate air source heat pumps (ccASHPs) in both heating and cooling seasons. The proposed model ensures satisfying the customer comfort in terms of the desired temperature range while providing the energy flexibility as a function of the input power of ccASHPs. The proposed flexibility model is a linear programming problem, integrated into the home energy management system that maximizes the flexibility provision potential of ccASHPs by obtaining accurate models of ccASHPs’ power consumption, and heating and cooling capacity that are dependent on setpoint and indoor temperatures. The numerical results of implementing the proposed model in a sample house demonstrate the capability of the model in capturing the flexibility of ccASHPs without violating the comfortable temperature range of residents while lowering electricity consumption of heating and cooling by 7% and 11% in heating and cooling season, respectively.
This study investigates the operation of inverter-based resource (IBR) plants in a bulk power system, under different grid-strength conditions and for various penetration levels of static and dynamic loads. For this purpose, Monte-Carlo EMT simulations have been performed on a combined transmission-distribution network in PSCAD/EMTDC software. The four active distribution feeders are connected at different locations of the bulk power system. Each active distribution feeder comprises three-phase static and induction motor loads, single-phase and three-phase average-model distributed energy resources. The results show that operating the IBR plants in weak transmission-distribution networks leads to short-term voltage instability. But by increasing the induction motor loads share of the active distribution feeders total gross load, this IBR-driven instability phenomenon has been mitigated. As a result, this can improve weak transmission-distribution network operations that accommodate a high share of grid-following IBR plants.
Energy is a critical element to support the activities and well-being of society and it can provide a mechanism for strategic investment and development. This paper describes a framework to explore the role of electricity prosumers in regenerative communities. Electricity prosumers are electricity consumers who have acquired the means of local energy production and have strategic energy and economic objectives. We illustrate how an initial investment on managed solar PV by a community prosumer could be utilized to support regenerative community objectives, where the community is empowered to revitalize itself. We show how the proposed model realizes higher levels of resilience and enables improvement of other regenerative community capitals.
Fault location, isolation, and service restoration of a self-healing, self-assembling microgrid operating off-grid from distributed inverter-based resources (IBRs) can be a unique challenge because of the fault current limitations and uncertainties regarding which sources are operational at any given time. The situation can become even more challenging if data sharing between the various microgrid controllers, relays, and sources is not available. This paper presents an innovative robust partitioning approach, which is used as part of a larger self-assembling microgrid concept utilizing local measurements only. This robust partitioning approach splits a microgrid into sub-microgrids to isolate the fault to just one of the sub-microgrids, allowing the others to continue normal operation. A case study is implemented in the IEEE 123-bus distribution test system in Simulink to show the effectiveness of this approach. The results indicate that including the robust partitions leads to less loss of load and shorter overall restoration times.
This paper reports on the structure of a power hardware-in-the-loop (PHIL) simulation testbed that implements, tests, and validates a novel AI-based hierarchical resilient operation model for distribution systems. The testbed implements the central and distributed controllers of the hierarchical resilient operation model and integrates a Digital Real-Time Simulator (DRTS), protective relays, a Real-Time Automation Controller (RTAC), a Software Defined Network (SDN) switch, and a battery energy storage (BES) system. The testbed provides comprehensive real-time visualization and monitoring capability as an advanced situational awareness and operator interface solution. The IEEE 33-node system is used as a test case to test and validate the operation of the model in normal operation and recovery operation after major outages in a fully automated fashion.
Microgrid installations have regained significant interest, driven by the increasing adoption of distributed energy resources, decentralized controls, and ongoing technological advancements. While the number of microgrids increases, there are opportunities to coordinate networks of microgrids. In this study, Irving’s algorithm is applied to a framework for the coordinated self-assembly of networked microgrids. Compared with the authors’ previous work, this new study relaxes the constraint on participated microgrids to have a single global objective. Thus, each microgrid can rank others according to its local preference. This reduces the amount of shared information, improves the privacy, and enhances the flexibility. In addition, the proposed method has better performance compared to the consensus method employed in the authors’ previous work, when dealing with a substantially larger number of microgrids exploring interconnection possibilities. The adaptability to that proposed framework is maintained. Examples are presented to illustrate the networking processes and self-assembly operations.
Power quality (PQ) has become an important research area to utilities, especially with the rapid rise in power-electronics-based load and generation that can exchange non-sinusoidal waveforms into distribution systems. Additionally, utilities are looking to improve the quality of power delivered to their customers, often looking towards new technologies like Distribution STATCOMs (DSes). This paper presents the background and motivation of installing three DSes to address the PQ issues present on a distribution system. Furthermore, this paper proposes various simulation and field measurement based approaches to quantify the impact of DSes on a distribution system in cases like fault response, impact to coefficient of grounding, harmonic injection, etc. Finally, this paper highlights the additional considerations that need to be made while installing DSes on a legacy 34kV system.
This paper investigates the transient stability of the direct droop-controlled grid-forming (GFM) inverter which employs the current clipping-based approach for overcurrent limitation. An equivalent circuit model of the direct-droop GFM inverter with current clipping is developed for this purpose. It is shown that during overcurrent conditions, the GFM inverter with current clipping acts as a voltage source with a virtual resistance behind the inverter filters. To compute the parameters of this virtual resistance an analytical expression is given in the paper. Then utilizing the developed circuit, a criteria to exit current limiting during post-fault conditions is given. The developed analytical approaches are then validated through electromagnetic transient (EMT) simulation results in PSCAD software and finally concluding remarks are drawn.
Building energy management - as a tool to effect day-to-day energy savings - is influenced by space occupancy levels. The use of video images to estimate occupancy levels has privacy concerns and is cost ineffective. In this paper, a Deep Learning (DL) based approach is proposed to estimate the number of people in a given space using environmental sensor data. Five environmental factors are considered to train and test the proposed model. The input data are pre-processed with $Z_{scor\mathrm{e}}$ normalization for better performance of the model. Further, the proposed method is compared with Long Short Term Memory (LSTM) with higher $F_{1}$ score. In addition, to improve the estimation accuracy of space occupancy, one hot encoding is done for output data. The proposed model estimates the number of students in classrooms with high accuracy in a cost-effective manner while maintaining privacy. The application of the proposed approach is to improve energy efficiency by utilising the estimated headcount information.
A significant increase in inverter-based distributed energy resources (DERs) is expected in the near future. Existing electromagnetic models and sequential electromechanical models of DERs supporting dynamic analysis cannot be extended to largescale three-phase distribution systems usually with unbalanced construction. As such, there is a need for appropriate industry models that can support distribution system dynamic studies. This paper describes the latest phasor models of grid-following (GFL) and grid-forming (GFM) inverters that can capture distribution system dynamics under grid disturbances. The overall modeling performance is examined by test cases including setpoint changes, balanced and/or unbalanced grid faults. The derived models can retain high accuracy with normalized root mean squared errors (NRMSEs) less than 3%, as verified by comparison with controller hardware-in-the-loop (CHIL) testing, MATLAB/Simulink phasor model and/or PSCAD electromagnetic transient (EMT) switching model simulation results.
Medium and heavy-duty (MDHD) electric vehicles (EVs) with sizable batteries and high charging power pose voltage stability risks in distribution networks. This paper introduces a novel approach using reactive power compensation from MDHD EVs through metaheuristic algorithms. Genetic algorithms, particle swarm optimization, moth flame optimization, salp swarm algorithm, whale optimization, and grey wolf optimization are assessed for their performance in voltage stability, power loss reduction, and computational efficiency. The proposed approach, primarily utilizing the salp swarm algorithm, optimizes power flow to mitigate voltage deviations and enhance stability. Simulations on a modified IEEE 33 bus system validate the algorithm’s effectiveness in improving voltage stability and reducing power losses, facilitating the integration of MDHD EVs into distribution networks.
The power grid has evolved over the course of many decades with the usage of cyber systems and communications such as Supervisory Control And Data Acquisition (SCADA); however, the cyber-power system can be infiltrated by malicious attackers due to this connectivity. Encryption is not a singular solution. Currently, there are several cyber security measures in development, including those based on artificial intelligence. However, there is a need for a varying but consistent attack algorithm to serve as a testbed for these AI tools to be trained and tested. This is important because in the event of a real attacker, it is not possible to know exactly where they will strike and in what order. Therefore, the proposed method in this paper is to use criminology concepts and fuzzy logic inference to create this algorithm and determine its effectiveness in making decisions on a cyber-physical system model. The method takes various characteristics of the attacker as an input, builds their ideal target node, and then compares the nodes to the high-impact target and chooses one as the goal. Based on that target and their knowledge, the attackers will attack nodes if they have resources. The results show that the proposed method can be used to model a variety of attacks with varying damaging effects, and one other set of tests shows the possibility for multiple attacks, such as denial of service and false data injection. The proposed method has been validated using an extended cyber-physical IEEE 13-node distribution system and sensitivity tests for the ruleset created.
Maintaining a stable frequency in power grid requires effective frequency regulation mechanisms to ensure supply and demand balance in real-time operation. Hydropower units benefit from fast frequency response and ramping capability, which makes them suitable for frequency regulation in power grids with high penetration of renewable energy sources (RES) and severe load disturbances. However, hydropower units depend on water flow and availability to operate, making their modeling and control challenging. This paper proposes a model predictive control approach for real-time frequency control to utilize the capability of hydropower units for providing frequency regulation in power system caused by real-time variations of the load and RES, while respecting the dynamics of different components in hydropower and thermal generators. The simulations results demonstrate the efficiency of the proposed controller in utilizing the potential of hydropower units to maintain the system frequency for different variation scenarios.
The power grid is vulnerable to cyberattacks due to the multitude of utilized communication-based devices. For example, remote terminal units (RTU) communicate commands or grid measurements. An encryption and decryption method is one way of preventing cyberattacks. However, sophisticated encryption and decryption methods can be time-consuming due to the large amount of data exchanged in a power system. This paper combines a Duffing oscillator, a time-based one-time frequency (TOTF) algorithm, and fast Fourier transform for preventing cyberattacks. The proposed method detects when an attacker distorts data in a high-noise environment. The proposed method is validated using simulation results on a modified communication layer by enabling the quadrature phase shift keying (QPSK) digital modulation method to transfer packets.
This paper investigates upgrading the classical cyber layer of power systems to support semi-quantum key distribution. With such an upgrade, only a few cyber nodes are required to have full quantum capabilities (i.e., generation, transmission, and measurement of qubits) while the rest of the cyber nodes are required to have limited quantum capabilities (i.e., transmission and measurement of qubits). As a result, unconditionally secure keys can be shared between the control center and the power substations to encrypt and decrypt critical measurement and control data. We study the problem of allocating the minimum number of quantum servers (i.e., nodes with full quantum capabilities) on the pre-existing cyber layer to satisfy the required key distribution rate in an attacker’s presence. Due to the associated computational complexity, we propose a greedy algorithm to solve the allocation problem. We examine the proposed allocation algorithm on the cyber layer of the IEEE-14 bus test system. Our results demonstrate that the target key rate can be satisfied at different attack levels.
Data security and cyberattack have become critical issues in the distributed power system where adversaries can swap the source information of sensors or even spoof and alter measurements. However, the cyber security of the power system is challenged by the unpredictability and stealth of the spoofing attacks. To protect the data security at the grid edge, this paper developed a synchrophasor data spoofing attack detection framework based on the time-frequency feature extraction techniques including the short-time Fourier transform (STFT) and object detection network for real-time synchrophasor data categorization and spoofing attack localization. The proposed approach outperforms earlier work in terms of spoofing attack detection and offers a vital localization function employing distributed synchrophasor sensors.
Modern Electric Power Sector is adopting policies for transitioning towards zero emission. The implementation of the policies for developing a smooth and efficient transition approach strongly relies on effective metrics to quantify emissions in a power system. Marginal Emission Rate (MER) is an important metric to encapsulate the change in emission in response to changes in demand. This paper proposes a computationally efficient MER calculation algorithm to analyze the impacts of reserve requirements on MER. The proposed algorithm uses a computationally efficient model to calculate the changes in the outputs of marginal generators in response to infinitesimal changes in demand. A systematic method is proposed to identify the marginal units with the presence of reserve requirements constraints. The MER can be calculated by multiplying the changes in outputs with corresponding emission rates. Numerical results on a small 3-Bus test system have been demonstrated to explain and validate the accuracy of the proposed method.
This paper introduces a high-fidelity cyber-physical Hardware-in-the-Loop (HIL) testbed to tackle cybersecurity challenges arising from the transition to active distribution grids with the increasing penetration of grid-edge Distributed Energy Resources (DERs). The testbed incorporates commercially available hardware devices, communication network emulators, and grid-edge Intelligent Electronic Devices (IEDs). Its controlled environment facilitates the study of cyber vulnerabilities, risk assessments, and the development of robust detection and restoration mechanisms. Notably, the testbed explores scenarios involving unobservable False-Data-Injection (FDI) attacks on inverter-based DERs as coordinated multiwave cyber attacks. Leveraging a multi-core real-time digital simulator from OPAL-RT, the testbed enables real-time execution of distribution grid simulation and communication network emulation. A modified IEEE 13-bus distribution feeder was used for testing and demonstration, with connections to various external devices, including SEL 411L relays, SEL 3350 RTAC, and the OpenDSO platform.
The proliferation of electric vehicles (EVs) necessitates accurate EV charging load forecasting for demand-side management and electric-grid planning. Conventional machine learning-based load forecasting methods like long short-term memory (LSTM) neural networks rely on large amounts of historical data, which can be resource-intensive and time-consuming to collect. In this study, we employ Transfer Learning (TL) and Model-Agnostic Meta-Learning (MAML) for short term EV charging load forecasting. These methods involve pre-training a base model on a larger comprehensive EV charging dataset followed by fine-tuning using a few days' worth of EV charging data in our target location. We find that the performance of both the TL and MAML models outperform traditional LSTM models and other classic machine learning models in the context of forecast accuracy when working in three different settings with limited data , load variance, and diverse geographical locations. The error metrics from TL and MAML are up to 24% and 61% lower than deep learning and classic machine learning models respectively.
