Western Electricity Coordinating Council Research Articles

Improvement of Economic Integration of Renewable Energy Resources through Incentive-Based Demand Response Programs

The integration of renewable generation presents a promising venue for displacing fossil fuels, yet integration remains a challenge. This paper investigates Demand Response (DR) as a means of economically integrating Renewable Energy Resources (RERs). We propose Incentive-Based DR (IBDR) programs, particularly suitable for small customers. The uncertainties in the electricity market price pose a challenge to IBDR programs, which is addressed in this paper through a novel and robust IBDR approach that considers both the electricity market price uncertainties and customer responses to incentives. In this paper, scenarios are simulated premised on the Western Electricity Coordinating Council (WECC) 240-bus system in which coal-fired power plants become inactivated, while the RER contribution increases in the span of one year. The simulation results indicate that the proposed IBDR program mitigates the issues associated with renewable expansion, such as utility benefit loss and market price volatility. In addition, the proposed IBDR effectively manages up to 30% of errors in day-ahead wind forecasts that significantly reduce financial risks linked to IBDR programs.

Critical zone identification framework for bulk electric system security assessment

Climate change introduces a new set of vulnerabilities. It might cause unexpected voltage and frequency violation problems that can damage the existing power system configurations. Identifying the zones containing more critical violations in a bulk system under different climatic conditions and contingencies is crucial to securing the power system operation and infrastructure safety. This paper presents an advanced critical zone identification framework that can retrieve information from extensive data yielded by dynamic contingency analysis results from a large interconnected power grid. A machine learning (ML) based clustering approach is applied to identify the critical zone under hundreds of scenarios. The developed framework is tested using the 2028 western electricity coordinating council (WECC) system, and the detailed results are discussed in this paper.

A Dataset for Electricity Market Studies on Western and Northeastern Power Grids in the United States

Efficient electricity market operations and cost-effective electricity generations are fundamental to a low-carbon energy future. The Western Electricity Coordinating Council (WECC) and Northeast Power Coordinating Council (NPCC) systems were built to provide efficient electrical grid simulation solutions for their respective U.S. regions. Data reuse for electricity economic studies remains a challenge due to the lack of credible and realistic economic data. This paper delivers a comprehensive dataset containing generator aggregations, generator costs, transmission limits, load distributions, and electricity prices for the WECC and NPCC systems based on real-world grid operation data at year 2020, including power plant geographic locations, generation profiles, regional power flow interchanges, and load distributions in both regions. The electricity price from the developed dataset is simulated based on the other items in the dataset, and we show that the variation of the simulated electricity price reasonably aligns with the real-world electricity price in both the WECC and NPCC regions. Overall, the developed dataset is of interest for various electricity market and economic studies, such as the economic dispatch and locational marginal price (LMP) analysis.

Stability model integration for large scale solar photovoltaic system using Western electricity coordinating council model

<span lang="EN-US">Due to the increased demand for renewable energy, the interest in the large-scale solar photovoltaic (LSSPV) power plant has recently grown dramatically. However, when a large amount of electricity is produced from the LSSPV power plant to the grid interconnection, the system commonly experiences instability and thus disrupt the grid system in disturbance issues such as bus fault, line-to-line fault, three-phase fault, and tripping. This sudden disturbance occurrence is tended to interrupt the stability of the system from providing balanced electrical production within the electrical grid. A dynamics response from the simulation is used to study the stability and the behavior of the photovoltaic (PV) plant into the grid interconnection by developing 118 bus system. The observation of critical clearing time (CCT) duration shows that the result from the simulation where the duration takes less than t=15 s for the system to get back to its pre-fault condition in three-phase fault and tripping in a dynamic simulation to shows that the system reaches its stability been observed through the simulation result by using from user-specific models to generic models like those advocated by the Western electricity coordinating council (WECC) in power system simulator for engineering (PSSE) software.</span>

Real-Time Transient Simulation and Studies of Offshore Wind Turbines

The real-time models of offshore wind turbine generators complied with industry standards are presented in this paper. The developed models provide essential capabilities for future electromagnetic transient controls and testing offshore wind farms, such as low-voltage ride-through, active/reactive power support, transient current limiting. Furthermore, the proposed models provide an additional capability of controlling negative sequence current, which meets the future requirements for protecting wind farm systems. This paper presents average-value and detailed switching real-time models in the Opal-RT real-time simulator. The efficacy of the proposed real-time models in terms of computational efficiency is demonstrated by comparing them with the non-real-time models. Extensive case studies are performed to demonstrate the control functions of the proposed models, such as dynamic-wind condition, power curtailment, low-voltage and fault ride-through, negative sequence current control, etc. The wind turbine model validation against the generic model presented by the Western Electricity Coordinating Council (WECC) is conducted to show their compliance with industry standards utilized by WECC. A real-time model of a 450 MW offshore wind farm is developed to show the feasibility of large-scale wind farm modeling.

Enhanced probabilistic contingency analysis considering fast ramping events of renewable generation

One of the major challenges in transmission planning is accounting for the impacts of renewable generation. Contingency Analysis (CA) is a technique used by utilities to meet transmission planning criteria mandated by the North American Electric Reliability Corporation (NERC). In existing CA practices, renewable generation is treated like conventional generation and does not consider the intermittency and fluctuations inherent to renewables. As renewable generation continues to grow, considering the impacts of renewable intermittency is becoming increasingly important. In this paper, an innovative approach is proposed to model the renewable intermittency as generator outages by, for the first time, introducing the concept of intermittency-induced outages (IIOs) for a single renewable plant and common mode outages (CMOs) for multiple renewable plants. A probabilistic outage model features different outage modes and the associated parameters that can be derived based on historical renewable generation data. The IIOs and CMOs can be seamlessly integrated into the existing probabilistic contingency analysis (PCA) framework. This paper presents the details for modeling and parameterization of the renewable outage modes. Such probabilistic outage models have been implemented using a Python-based enhanced PCA (ePCA) tool that drives and enhances the capability of the built-in PCA module of the PSS/E software platform, a widely used tool by the industry. Using the Western Electricity Coordinating Council (WECC) system, a proof-of-concept case study is presented and discussed to illustrate the unique impacts of increased penetration levels of renewables on transmission planning studies.

Intelligent and rapid event-based load shedding pre-determination for large-scale power systems: Knowledge-enhanced parallel branching dueling Q-network approach

With the increasing penetration of renewable energy, power system instability factors are rising. Transient voltage instability is a common power system problem that can cause blackouts and severe economic losses. An important measure to ensure transient voltage stability during emergencies is event-based load shedding (ELS). However, formulating ELS measures by experts gradually becomes inadaptable and time-consuming currently. With the increased complexity and uncertainty of modern new power systems, there is an urgent need for more intelligent and rapid ELS. This paper proposes a knowledge-enhanced parallel branching dueling Q-network (BDQ) framework for intelligent and rapid ELS against transient voltage instability. Firstly, an event-based Markov decision process (MDP) that differs from the conventional response-based MDP is established, which can effectively guide the training process. Secondly, to condense the huge conventional exponential decision space, a multi-branch BDQ structure is designed, which has higher training effectiveness and decision capability compared to branchless agents. Then, the domain proprietary knowledge that low-voltage buses are prioritized for ELS is incorporated into the BDQ agent. In comparison with a purely data-driven BDQ approach, incorporating knowledge can significantly enhance both training effectiveness and decision capability. Next, to further improve the applicability in large-scale real power systems, the parallel BDQ is proposed. Finally, the advantages of the proposed approach are demonstrated in the China Electric Power Research Institute 36-bus system and the Western Electricity Coordinating Council 179-bus system.

Parameter Tuning for WECC Generic Photovoltaic System Models Using Latin Hypercube Sampling and Pareto Optimality

With attention increasing towards worldwide ecological conservation, the energy policies of various countries are moving towards renewable energy systems, primarily with solar and wind as the primary power sources. The Western Electricity Coordinating Council (WECC) has developed a generic model as a simulation framework for a typical solar wind generation system to analyze the response of the actual equipment. In this paper, we propose a parameter tuning process for a generic model controller that combines Latin hypercube sampling and Pareto optimization to enable a generic model to effectively match the output of an actual inverter device. Different operating scenarios were considered to match the dynamic characteristics of the inverter device using the multi-objective particle swarm algorithm (MOPSO) along with Pareto optimization. The effectiveness of the proposed parameter tuning process was verified on a user-defined test system and, later, the IEEE 39 bus system with a large solar power facility.

Optimal Modeling for Dynamic Response of Energy Storage Systems

Using energy storage systems with solar and wind energy can overcome the intermittence of these types of renewable energy. According to the regulations made by the utilities in each country, facilities that are connected to the power grid should be assessed on how they influence the power grid. Power system simulation software such as PSS/E, DIgSILENT, and DSATools are widely used by utilities. The default models in these kinds of software are defined by the Western Electricity Coordinating Council (WECC). Improper parameters will produce estimation results that are not informative, and the utilities may make wrong decisions. To avoid this problem, this study designed the process to find suitable parameters for real energy storage systems. Using models created by the WECC and reducing it according to the application condition decreased the burden of the optimization process. We used two algorithms and their improved versions to search for an appropriate value of variables that can represent a real energy storage system. We also compared the results between the BESS simplified model that we built and the original model from PSS/E.

AI-Based Faster-Than-Real-Time Stability Assessment of Large Power Systems with Applications on WECC System

Achieving clean energy goals will require significant advances in regard to addressing the computational needs for next-generation renewable-dominated power grids. One critical obstacle that lies in the way of transitioning today’s power grid to a renewable-dominated power grid is the lack of a faster-than-real-time stability assessment technology for operating a fast-changing power grid. This paper proposes an artificial intelligence (AI) -based method that predicts the system’s stability margin information (e.g., the frequency nadir in the frequency stability assessment and the critical clearing time (CCT) value in the transient stability assessment) directly from the system operating conditions without performing the conventional time-consuming time-domain simulations over detailed dynamic models. Since the AI method shifts the majority of the computational burden to offline training, the online evaluation is extremely fast. This paper has tested the AI-based stability assessment method using multiple dispatch cases that are converted and tuned from actual dispatch cases of the Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. The results show that the AI-based method could accurately predict the stability margin of such a large power system in less than 0.2 milliseconds using the offline-trained AI agent. Therefore, the proposed method has great potential to achieve faster-than-real-time stability assessment for practical large power systems while preserving sufficient accuracy.

A hybrid transfer learning method for transient stability prediction considering sample imbalance

Data-driven transient stability prediction (TSP) exists with issues of model robustness and sample imbalance. An instance-based and parameter-based of hybrid transfer learning (HTL) method for TSP considering sample imbalance is proposed to address these two issues. The instance-based transfer learning is firstly utilized to select those applicable samples from the source domain system when the boundary conditions such as the network topology and the operational mode of power system change, which may significantly shorten the time for time-domain simulation (TDS) of the target domain system. Subsequently, the conditional generative adversarial network (CGAN) is employed to augment the unstable samples for obtaining a more balanced training data set. Finally, the parameter-based transfer learning is adopted to quickly update the model for TSP. Case studies conducted on a New England 10-machine 39-bus system, an IEEE 50-machine 145-bus system and a Western Electricity Coordinating Council (WECC) 29-machine 179-bus system demonstrate superior quality and diversity of the generated samples obtained by the CGAN-based data augmentation algorithm than the ones of traditional methods involving SMOTE, ADASYN, GAN and DCGAN. Furthermore, the results from numerous numerical experiments also indicate that the proposed HTL method considering sample imbalance improves the model robustness of TSP.

Grid Reserve and Flexibility Planning Tool (GRAF-Plan) for Assessing Resource Balancing Capability Under High Renewable Penetration

High penetration of intermittent generation increases uncertainty and variability in balancing reserve needs. New tools are needed to help the balancing authority system operator plan for intraday and intra-hour balance between generation and load. The Grid Reserve and Flexibility Planning tool (GRAF-Plan) helps plan for adequate balancing reserves for future years or seasons for expected wind and solar generation. It also assesses the flexibility of the scheduled generation fleet to meet such requirements. The estimations are based on utilities’ operational practices (e.g., forecasting and time frame of reserve deployment), and it incorporates detailed data from renewable generation and load. Application of the tool in estimating reserve requirements in Central America under different levels of renewable generation (high and low) and for the Western Electricity Coordinating Council (WECC) 2030 Anchor Data Set scenario is discussed.

A Machine Learning-Based Method for Identifying Critical Distance Relays for Transient Stability Studies

Protective relays play a crucial role in defining the dynamic responses of power systems during and after faults. Therefore, modeling protective relays in stability studies is crucial for enhancing the accuracy of these studies. Modeling all the relays in a bulk power system is a challenging task due to the limitations of stability software and the difficulties of keeping track of the changes in the setting information of these relays. Distance relays are one of the most important protective relays that are not properly modeled in current practices of stability studies. Hence, using the Random Forest algorithm, a fast machine learning-based method is developed in this paper that identifies the distance relays required to be modeled in stability studies of a contingency, referred to as critical distance relays (CDRs). GE positive sequence load flow analysis (PSLF) software is used to perform stability studies. The method is tested using 2018 summer peak load data of Western Electricity Coordinating Council (WECC) for various system conditions. The results illustrate the great performance of the method in identifying the CDRs. They also show that to conduct accurate stability studies, only modeling the CDRs suffices, and there is no need for modeling all the distance relays.

Effect of modeled time horizon on quantifying the need for long-duration storage

Long-Duration Energy Storage (LDES) has gained interest due to its key role in attaining a decarbonized, low-cost, and stable grid driven by variable renewable electricity (VRE). Currently, there is a wide range of LDES technologies being developed to provide electricity with 8+ hours of consecutive discharge. However, current capacity expansion models used in long-term planning processes rarely consider low cost LDES as a candidate technology. If they do, the storage balancing horizon (SBH) of the model usually only considers non-consecutive 1-day periods that do not capture the potential of LDES to shift energy across multiple days or even seasons. Addressing these limitations in existing models, this work explores the ways in which the optimal energy storage changes when increasing the number of consecutive days in the SBH and how these changes will impact planners who are determining the future roles of energy storage. Our analysis uses SWITCH, an open-source capacity expansion model with a high spatial resolution for the entire Western Electricity Coordinating Council (WECC) in a zero-carbon scenario in 2050. We find that the number of consecutive days in the SBH changes both the total selected power and energy capacity of LDES when storage energy and power capacity overnight costs are $13 USD/kWh (or less) and $113 USD/kW, respectively. We also find that the amount of required energy in storage to drive a future VRE-driven WECC grid ranges from 2.5 TWh to 16.0 TWh depending on the length of the SBH. The optimal storage duration (energy to power ratio) we obtain ranges from 10 h to 620 h among all the scenarios. Furthermore, depending on the storage cost assumption, we observe different charge/discharge patterns when varying the length of the SBH. Given our results, we anticipate that as more LDES technologies become commercially available, it will be critical to increase the length of the SBH to fully capture the benefits of LDES assets in long-term planning processes of high VRE-driven grids.

A Hybrid Technique for Analysis of Low-Frequency Oscillation in Power System

Estimating the low-frequency oscillation in an interconnected power system is the most important requirement to keep the power system in a stable operating condition. This research work deals with a hybrid robust and accurate approach using a combination of Estimation of signal parameters via rotational invariant techniques (ESPRIT) and Prony algorithm to extract the low-frequency oscillatory modes present in the power system. The observation inspires the hybrid method that the true modes of the signal are present in any signal processing technique (for example, Prony algorithm) along with other fictitious modes regardless of the order of the power system. Moreover, this research obtained true modes by calculating Euclidean distance and applying the threshold value concept. The proposed technique is tested with different noise conditions and varying sampling rates of Phasor Measurement Unit (PMU) to check the proposed hybrid technique’s robustness compared to Prony and the multiple ESPRIT method. Finally, the proposed method is applied to the real signal obtained from the Western Electricity Coordinating Council (WECC) network, and it estimates accurate and precise parameters compared to other methods. The accuracy for estimation of frequency and attenuation factor is calculated for the three-mode synthetic signal at a noise level of 10dB by the hybrid algorithm, multiple ESPRIT, and Prony algorithm, which shows that hybrid algorithm has minimum percentage error. Thus, the proposed hybrid algorithm accurately estimates the parameters of low-frequency oscillation as compared to other existing methods without involving any fictitious modes.

New Assessment of Wave Energy in Relation to Geomorphological and Demographic Characteristics on the Pacific Coast of Baja California, Mexico

The electrical grid of the Baja California peninsula, Mexico, operates separately from the National Interconnected System, with Baja California connected to the Western Electricity Coordinating Council of the USA; and Baja California Sur having an isolated, independent network. Based on the need for alternative renewable sources of electricity generation in the region, this work evaluates the wave energy power at specific locations on the Pacific coast of the peninsula, and identifies wave generation sites (WEGS). The WEGS were defined as rocky shore sites associated with a wave energy potential of over 10 kW/m (where the installation of oscillating water columns (OWC) is feasible) and the presence of coastal settlements that are less than 10 km from the shoreline. It was found that 370 km of the Pacific coast of the peninsula have characteristics suitable for the installation of OWC, and up to 1.4 million inhabitants could be benefited by this wave energy harvesting.

U.S. Resilience to large-scale power outages in 2002–2019

Prolonged power outages debilitate the economy and threaten public health. Existing research is generally limited in its scope to a single event, an outage cause, or a region. Here, we provide one of the most comprehensive analyses of large-scale power outages in the U.S. from 2002 to 2019. This analysis is based on the outage data collected under U.S. federal mandates that concern large blackouts, typically of transmission systems and exclude much more common but smaller blackouts, typically, of distribution systems. We categorized the data into four outage causes and computed reliability metrics, which are commonly used for distribution-level small outages only but useful for analyzing large blackouts. Our spatiotemporal analysis reveals six of the most resilient U.S. states since 2010, improvement of power resilience against natural hazards in the south and northeast regions, and a disproportionately large number of human attacks for its population in the Western Electricity Coordinating Council region. Our regression analysis identifies several statistically significant predictors and hypotheses for U.S. resilience to large blackouts. Furthermore, we propose a novel framework for analyzing outage data using differential weighting and influential points to better understand power resilience. We share curated data and code as Supplementary Materials.

Coordinated Wide-Area Damping Control Using Deep Neural Networks and Reinforcement Learning

This paper proposes the design of two coordinated wide-area damping controllers (CWADCs) for damping low frequency oscillations (LFOs), while accounting for the uncertainties present in the power system. The controllers based on Deep Neural Network (DNN) and Deep Reinforcement Learning (DRL), respectively, coordinate the operation of different local damping controls such as power system stabilizers (PSSs), static VAr compensators (SVCs), and supplementary damping controllers for DC lines (DC-SDCs). The DNN-CWADC learns to make control decisions using supervised learning; the training dataset consisting of polytopic controllers designed with the help of linear matrix inequality (LMI)-based mixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_2/H_\infty$</tex-math></inline-formula> optimization. The DRL-CWADC learns to adapt to the system uncertainties based on its continuous interaction with the power system environment by employing an advanced version of the state-of-the-art deep deterministic policy gradient (DDPG) algorithm referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bounded exploratory control</i> -based DDPG (BEC-DDPG). The studies performed on a 33 machine, 127 bus equivalent model of the Western Electricity Coordinating Council (WECC) system-embedded with different types of damping controls demonstrate the effectiveness of the proposed CWADCs.

Effective damping of local low frequency oscillations in power systems integrated with bulk PV generation

High penetration of renewable sources into conventional power systems results in reduction of system inertia and noticeable low-frequency oscillations (LFOs) in the rotor speed of synchronous generators. In this paper, we propose effective damping of LFOs by incorporating a supplementary damping controller with a photovoltaic (PV) generating station, where the parameters of this controller are coordinated optimally with those of a power system stabilizer (PSS). The proposed method is applied to damp local electromechanical modes by studying a system comprising a synchronous generator and a PV station connected to an infinite bus. The PV station is modeled following the instructions of the Western Electricity Coordinating Council. The problem is modeled as an optimization problem, where the damping ratio of the electromechanical modes is designed as the objective function. Constraints including upper and lower limits of decision parameters and damping ratio of other modes are considered by imposing penalties on the objective function. Different optimization algorithms are used to pursue the optimal design, such as political, improved gray wolves and equilibrium optimizers. The results validate the effectiveness of the proposed controller with PSS in damping local modes of oscillations.

Secondary Frequency Regulation from Variable Generation Through Uncertainty Decomposition: An Economic and Reliability Perspective

The rapid integration of variable generation (VG), such as photovoltaics (PV), necessitates an increase in the secondary frequency regulation (SFR) to handle the system intra-dispatch interval imbalance because of VG's variation. Although PV has the control capability to provide SFR, the main challenge is in guaranteeing the delivery of PV's energy and SFRs capacities in the automatic generation control (AGC) within each dispatch interval considering its uncertainty. This paper proposes a deliverable VG SFR provision model with endogenous VG's power uncertainty decomposition. First, the uncertainty of VG is decomposed using distributionally robust chance constraints through which the deliverable SFR provision is guaranteed. Next, the intra-interval frequency response of PV's SFR is validated with a user defined AGC model including the PV power plant. Finally, the economic benefits and the reliability improvements—such as the frequency deviation reduction with PV providing SFR—can be integrated and evaluated. The proposed model is tested in a modified 18-bus system and in the 240-bus simplified Western Electricity Coordinating Council test system. Results demonstrate that with the deliverable SFR from PV, the system cost and the frequency reliability can be improved simultaneously. PV's SFR performance can be guaranteed with the proposed model.