Bulk Power System Research Articles

The evolving energy and capacity values of utility-scale PV-plus-battery hybrid system architectures

Abstract In this study, we explored how the value of hybrid systems comprising solar photovoltaics (PV) and lithium-ion battery storage could evolve over time. Using a price-taker model with hourly energy and capacity prices projected to 2050, we simulated the revenue-maximizing dispatch of three PV-plus-battery architectures, with fixed component sizing, in three locations. The architectures reflect different coupling types, which vary in terms of whether the PV and battery systems have separate inverters or a shared inverter and whether the battery can charge from the grid. We found that the highest-value architecture today varies largely based on PV penetration and the magnitude and timing of peak-price periods. As PV penetration increases over time—based on the evolution of the bulk power system—two trends emerge that indicate a convergence of the values of the systems studied. First, the energy values of the three architectures converge as an increasing fraction of energy from the coupled PV is used to charge the battery. Second, their capacity values converge to that of the battery as the capacity credit of stand-alone PV approaches zero. Of the systems studied, no single architecture has the highest year-one benefit-cost ratio in every region and year, and benefit-cost ratios of PV-plus-battery systems range from a 15% reduction to a 25% increase compared to separate PV and battery systems. Understanding the factors that influence the performance and economics of PV-plus-battery systems will help system planners and researchers evaluate the potential benefits of these hybrid resources to future power systems.

Increasing DER integration through discrete intraday settlements

Deployment of distributed energy resources (DERs) is accelerating across the United States. These assets have the potential to provide cost-effective grid services and bulk power while supporting a low-cost energy transition to carbon-free electricity. Full integration of these resources into the bulk power system remains a barrier to unlocking the potential value of these assets. The regional wholesale energy markets, originally designed to integrate centralized, dispatchable power plants, must be reformed to adapt to the changing potential resource mix. The current day-ahead and real-time system is poorly designed for DERs. Day-ahead DER forecast errors create exposure to real-time price variations, as resources committed in the day-ahead market are required to rebalance in real-time, challenging the business model for DERs. New intraday markets with multiple discrete intermediate auctions between the day-ahead market and real-time market are needed to enable proper integration of DERs and efficient mitigation of forecast uncertainty risk. These markets must incorporate financially-binding commitments, closer-to-dispatch price signals, and adequate liquidity to ensure competition. Intermediate auctions should be built on top of existing intraday processes in each regional market, incorporating the principles of the existing market structures and leveraging the benefits of the centralized dispatch systems. Regional transmission organization (RTOs) and Independent System Operators (ISOs) should pursue and implement intermediate auctions to support fair, open, and efficient markets in the U.S.

Data-Driven Engineering: The Reliability and Resilience of the North American Bulk Power System [Technology Leaders

Electricity is an essential need for modern society. Nearly everything we do relies on safe and affordable electric energy. The constant demand for reliable energy delivery exists during a time of rapid changes to and evolutions of the bulk power system (BPS) in North America. Inverter-based resources, such as wind, solar photovoltaic (PV), battery energy storage systems, and hybrid plants, continue to transform the mix of BPS-connected generating resources. Sustainability and climate change initiatives are driving innovations in end-use loads, such as the electrification of the transportation sector. Distributed energy resources (DERs), aggregators, and the Internet of Things are pushing consumers of electric energy toward becoming prosumers in our electrical ecosystem. The persistent threat of cyberattacks on the electrical infrastructure by cybercriminals and nation-states is bringing attention to securing the BPS. These challenges are significant, yet the common themes toward how to overcome them may be more straightforward: Collaboration is key, information sharing is critical, technology innovation is inevitable, and engineering decisions require adequate data to develop appropriate solutions.

A data‐driven approach to estimate emissions for market‐based power system test cases

A data-driven technique to determine greenhouse gas (GHG) and air-pollutant (AP) emissions from bulk-power system simulations is proposed. The proposed technique emulates the dispatch of a bulk-power system using open-source hourly fuel-energy data from an actual U.S. electricity market (i.e. PJM). Sixteen different fuel types were analysed from real generator data and dynamically assigned to power system test cases to statistically represent the real fuel-energy mix. Each test case generator is assigned a heat-rate function based on open-source real generator data to estimate realistic emissions from power system test case simulations. These augmented test cases can be used to simulate how changes in load and generation impact power system emissions to determine the environmental sustainability of new technologies (e.g. demand-side management). The proposed technique is implemented on three different power system test cases, and the simulated emissions are compared with the actual emissions of the PJM system. The results from the test systems are found to accurately emulate the time-series values of fuel-mix, emissions, and marginal costs of PJM.

Reliability evaluation of bulk power systems with wind generation using small signal stability analysis

This paper presents a computationally efficient methodology for reliability evaluation of bulk power systems with wind power generation using small-signal stability analysis. The methodology is based on the application of Monte Carlo simulation to calculate reliability indices, where the sampled system states are evaluated by means of their small-signal stability condition. Therefore, the system small-signal stability is evaluated for scenarios that consider the failure of generation and transmission assets and the unavailability and power variations of wind farms. Unlike previous works, the proposed methodology does not consider the probability of small variations in system parameters, but instead the probability of occurrence of the scenarios themselves including the large and nonlinear variations resulting from network failures. This allows comparing different alternatives for the planning of large power systems, such as different PSS settings or system reinforcement solutions, in a more realistic approach. Additionally, the methodology uses a non-conventional partial eigenvalue solution focusing on a user-defined subset of poles of interest, efficient and practical for the small-signal analysis of large-scale power systems.

Bulk power system availability assessment with multiple wind power plants

The use of renewable non-conventional energy sources, as wind electric power energy and photovoltaic solar energy, has introduced uncertainties in the performance of bulk power systems. The power system availability has been employed as a useful tool for planning power systems; however, traditional methodologies model generation units as a component with two states: in service or out of service. Nevertheless, this model is not useful to model wind power plants for availability assessment of the power system. This paper used a statistical representation to model the uncertainty of power injection of wind power plants based on the central moments: mean value, variance, skewness and kurtosis. In addition, this paper proposed an availability assessment methodology based on application of this statistical model, and based on the 2m+1 point estimate method the availability assessment is performed. The methodology was tested on the IEEE-RTS assuming the connection of two wind power plants and different correlation among the behavior of these plants.

Determining Voltage Control Areas in Large Scale Power Systems Based on Eigenanalysis of the QV Sensitivity Matrix

Bulk power systems failures caused by voltage instability phenomena have become more frequent during the last years. Voltage control schemes capable of keeping voltage magnitude within specified limits, automatically, for a specific area of the power system can prevent this problem. The identification of potential voltage control areas of the power system is an essential part of voltage control scheme design. In some European countries these schemes have being contributing significantly both for safety and for the quality of operation. Regional and coordinated voltage control schemes are powerful mechanisms to avoid or mitigate this voltage instability phenomena. These control schemes known as secondary voltage control are based on pilot nodes and control areas, and their correct identifications are fundamental to an efficient voltage profile regulation. This paper proposes a methodology to identify voltage control areas based on the eigenvalues and eigenvectors of the load flow QV sensitivity matrix. The results are compared with the traditional voltage sensitivity analysis methods.

Quantitative assessment of U.S. bulk power systems and market operations during the COVID-19 pandemic

Starting in early 2020, the novel coronavirus disease (COVID-19) severely attached the U.S., causing substantial changes in the operations of bulk power systems and electricity markets. In this paper, we develop a data-driven analysis to substantiate the pandemic’s impacts from the perspectives of power system security, electric power generation, electric power demand and electricity prices. Our results suggest that both electric power demand and electricity prices have discernibly dropped during the COVID-19 pandemic. Geographically diverse impacts are observed and quantified, while the bulk power systems and markets in the northeast region are most severely affected. All the data sources, assessment criteria, and analysis codes reported in this paper are available on a GitHub repository.

Characterizing and Visualizing the Impact of Energy Storage on Renewable Energy Curtailment in Bulk Power Systems

The uncertain natures of renewable energy lead to its underutilization; energy storage unit (ESU) is expected to be one of the most promising solutions to this issue. This paper evaluates the impact of ESUs on renewable energy curtailment. For any fixed renewable power output, the evaluation model minimizes the total amount of curtailment and is formulated as a mixed integer linear program (MILP) with the complementarity constraints on the charging and discharging behaviors of ESUs; by treating the power and energy capacities of ESUs as parameters, the MILP is transformed into a multi-parametric MILP (mp-MILP), whose optimal value function (OVF) explicitly maps the parameters to the renewable energy curtailment. Further, given the inexactness of uncertainty’s probability distribution, a distributionally robust mp-MILP (DR-mp-MILP) is proposed that considers the worst distribution in a neighborhood of the empirical distribution built by the representative scenarios. The DR-mp-MILP has a max–min form and is reformed as a canonical mp-MILP by duality theory. The proposed method was validated on the modified IEEE nine-bus systems; the parameterized OVFs provide insightful suggestions on storage sizing.

Reduction of Power Imbalances Using Battery Energy Storage System in a Bulk Power System with Extremely Large Photovoltaics Interactions

Power imbalances such as power shortfalls and photovoltaic (PV) curtailments have become a major problem in conventional power systems due to the introduction of renewable energy sources. There can be large power shortfalls and PV curtailments because of PV forecasting errors. These imbalances might increase when installed PV capacity increases. This study proposes a new scheduling method to reduce power shortfalls and PV curtailments in a PV integrated large power system with a battery energy storage system (BESS). The model of the Kanto area, which is about 30% of Japan’s power usage with 60 GW grid capacity, is used in simulations. The effect of large PV power integration of 50 GW and 100 GW together with large BESS capacity of 100 GWh and 200 GWh has been studied. Mixed integer linear programming technique is used to calculate generator unit commitment and BESS charging and discharging schedules. The simulation results are shown for two months with high and low solar irradiance, which include days with large PV over forecast and under forecast errors. The results reveal that the proposed method eliminates power shortfalls by 100% with the BESS and reduce the PV curtailments by 69.5% and 95.2% for the months with high and low solar irradiance, respectively, when 200 GWh BESS and 100 GW PV power generation are installed.

Reliability of a Power System With High Penetration of Renewables: A Scenario-Based Study

Reliability is a crucial consideration in the expansion of generation or transmission in a bulk power system, especially in a power grid with a high penetration of renewables. Reliability indices, such as loss of load probability (LOLP), are generally evaluated to determine the adequacy of a bulk power system in the future. When a Monte-Carlo simulation is conducted to evaluate the LOLP, the computational time is long because chronological time-series data are involved. This work proposes a novel scenario-based method for studying the LOLP in a bulk power system with a high penetration of renewables. Scenarios are generated by aggregating Markov states of hourly loads, photovoltaic power generations and wind power generations. The power flow result in each scenario is examined to ensure power balance among demand, supply and losses, so the LOLP can be obtained. This novel scenario-based method is more efficient than the traditional chronological time-series approach because the number of considered scenarios is much smaller than the number of considered time-series cases. A set of realistic data regarding Taiwan power system, consisting of 2078 buses associated with a peak load of 39.178 GW, wind power of 6.938GW and photovoltaic power of 20 GW in 2025 is used to validate the proposed method.

An Algorithmic Approach for Identifying Critical Distance Relays for Transient Stability Studies

After major disturbances, power system behavior is governed by the dynamic characteristics of its assets and protection schemes. Therefore, modeling protection devices is essential for performing accurate stability studies. Modeling all the protection devices in a bulk power system is an intractable task due to the limitations of current stability software, and the difficulty in updating the setting data for thousands of protection devices. One of the critical protection schemes that is not adequately modeled in stability studies is distance relaying. Therefore, this paper proposes an iterative algorithm that uses two methods to identify the critical distance relays to be modeled in stability studies: (i) apparent impedance monitoring, and (ii) the minimum voltage evaluation (MVE). The algorithm is implemented in Python 3.6 and uses the GE positive sequence load flow analysis (PSLF) software for performing stability studies. The performance of the algorithm is evaluated on the Western Electricity Coordinating Council (WECC) system data representing the 2018 summer-peak load. The results of the case studies representing various types of contingencies show that to have an accurate assessment of system behavior, modeling the critical distance relays identified by the algorithm suffices, and there is no need for modeling all the distance relays.

Privacy-preserving mechanism for collaborative operation of high-renewable power systems and industrial energy hubs

Nowadays, achieving operational solutions to boost the flexibility of bulk power systems has become one of the major challenges in both industry and academia. The outcomes of recent studies demonstrate that the deployment of large-scale energy hubs can help enhance the flexibility of power systems. However, centralized management of networked energy hubs may not be compatible with the power system operator when they are managed by private owners. Motivated by this observation, a privacy-preserving decision-making structure is proposed in this paper for the collaborative operation of private industrial energy hubs and renewable power system by considering the high penetration of renewable energy sources. The proposed structure is drawn up based on the decentralized two-stage robust–stochastic approach and solved using the Benders decomposition algorithm by relying on the private ownership of various entities. The main objectives of this study lie in (1) decreasing renewable power curtailment and (2) minimizing the total operation costs of the private entities. To achieve these objectives, the effects of the multi-energy demand response program and energy conversion facilities are investigated in the context of the developed model. The competency and robustness of the proposed collaborative decision-making structure are examined on the IEEE 30-bus test system using GAMS and DIgSILENT PowerFactory software. Results show that if industrial energy hubs are successfully deployed in industrial parks, the total operation cost of the renewable power system decreases by up to 16.33%, renewable power curtailment reduces by 92.9%, and flexibility of the renewable power system enhances by increasing spinning reserve.

Operational and control approach for PV power plants to provide inertial response and primary frequency control support to power system black-start

The renewable generation growth, which includes photovoltaic power plants, has posed challenges for the planning and operation of contemporary power systems. High penetration levels of generation based on fully rated converters makes the black-start of power systems more difficult due to the low equivalent inertia of the system, the performance deterioration of the primary frequency control, the reduced number of black-start units, and the energization of large loads. In the context of the planning and operational challenges imposed by renewable generation, this work proposes an operational and control approach for centralized photovoltaic generation to provide inertial response and primary frequency control support to the black-start of bulk power systems. The proposed control aims to provide inertial response and primary frequency control support only during the system restoration process. The work also proposes an analytical formulation to assess the impact of the operating point of photovoltaic modules on the dynamics of photovoltaic units. The obtained results have shown that photovoltaic power plants are capable of providing support to the black-start of power systems, improving the dynamic and steady-state performance of the system frequency response during the black-start. The performed dynamic analyses have shown that the operational region of the photovoltaic modules significantly affects the dynamics of the power balance in the photovoltaic unit and, consequently, the dynamics of the load pick-up of the photovoltaic modules. The proposed dynamic analysis is useful to support the formulation of control approaches for de-loaded operation of photovoltaic power plants.

Control and synchronization of a fixed-frequency control method in microgrid based on global positioning system

Control methods based on global positioning systems (GPS) are reported in multiple literatures recently, which achieve a fixed frequency operation of the microgrid and therefore has the tremendous benefit that any problems related to frequency instability are eliminated. However, the possible interruption of GPS timing signals may cause current circulating and finally leads to instability of the microgrid, yet it is largely neglected in literatures. This paper presents an angle synchronizing mechanism which utilizes the timing signal from GPS satellites and an auxiliary frequency droop loop to ensure synchronization during GPS offline events. Smooth transfer is guaranteed between primary and auxiliary control loops with discrete control architecture. Also, to allow microgrid using GPS-based control to work in tandem with the bulk power system or another frequency droop microgrid, an extra synchronization algorithm is proposed. The viability and performance of the proposed control structure is validated by case studies.

High electrification futures: Impacts to the U.S. bulk power system

Electrification of end-use technologies could have important implications for bulk power sector planning, operations, and impacts. By combining previously-defined electrification scenarios with capacity expansion modeling, we evaluate the potential challenges and opportunities associated with electrification. Our scenario analysis reveals that electrification reduces energy consumption and air emissions from across the energy sector, and its impacts on energy system costs are modest due, in part, to the abundance of low-cost resources in the United States.

A General Design Method for Phasor Estimation in Different Applications

Driven by the increasing development and complexity of bulk power systems, the requirements of phasor estimation methods differ for different scenarios and applications. In this study, a general design method for phasor estimation algorithms in different applications is proposed based on a complex finite impulse response (FIR) bandpass filter. A design framework based on mathematical error models is proposed to facilitate the design of a complex bandpass filter for different requirements and to reduce the trial and error process. The general error models between the filter gain and the error limitations of all variables measured by the phasor measurement units (PMUs) are established separately, according to the absolute value inequality. The filter design criteria obtained from the error models can determine the passband and stopband gain range of the complex bandpass filters. Three phasor algorithms for different classes of PMUs are designed, and their performances are validated and compared using experimental tests. The results demonstrate that the proposed method can design phasor algorithms to provide accurate measurements under different scenarios.

Sizing energy storage to reduce renewable power curtailment considering network power flows: a distributionally robust optimisation approach

The limited reserve of fossil fuels and public awareness of environmental issues prompt the rapid development of renewable energy generation. However, the centralised utilisation of renewable energy in bulk power systems is impeded mainly by its volatile nature and transmission congestion, leading to the spillage of renewable power. The energy storage unit is expected to be a promising measure to smooth the output of renewable plants and reduce the curtailment rate. This study addresses the energy storage sizing problem in bulk power systems. To capture the operating status of the power system more accurately, the authors use a dedicated power flow model which involves voltage and reactive power. The uncertainty of renewable generation is described via inexact probability distributions encapsulated in a data-driven Wasserstein-metric based ambiguity set, based on which the renewable energy curtailment rate is formulated as a distributionally robust chance constraint. The objective is to minimise the total investment cost, and the optimal sizing problem gives rise to a distributionally robust chance-constrained program, and is reformulated as a tractable linear program via conservative approximation. Case studies conducted on the modified IEEE 30-bus and 118-bus systems demonstrate the effectiveness and performance of the proposed approach.

Distribution Feeder-Scale Fast Frequency Response via Optimal Coordination of Net-Load Resources—Part I: Solution Design

This article is the first of a two-part series that develops and experimentally demonstrates a first-of-its-kind hierarchical control solution for optimally dispatching thousands of deferrable loads and distributed energy resources (DERs) across a distribution feeder to provide fast frequency response (FFR) within 500 ms to the bulk power system. This approach rapidly coordinates resources online after a frequency event occurs, allowing fast-changing, behind-the-meter (BTM) resources to be incorporated and aggregate FFR power set points to be achieved more quickly and accurately than existing approaches. We also present a solution for determining the optimal amount of headroom to operate solar inverters with to minimize opportunity cost while ensuring the FFR response viability of a building with the inverter and deferrable loads. In Part I, we develop practical algorithms for fast, cost-based optimal dispatch at multiple aggregation scales (single building, multiple buildings, and full distribution feeder), establish their optimality, and demonstrate via simulation that they are faster than state-of-the-art, coordinated frequency response approaches. In Part II, the entire platform is implemented and experimentally verified using a unique power hardware-in-the-loop demonstration, including more than 100 powered loads and DERs connected to a real-world distribution network model and over 10,000 net-load resources dispatched.

A Novel Framework for Optimizing Ramping Capability of Hybrid Energy Storage Systems

Hybrid Energy Storage System has been widely applied in aerospace, electric vehicle, and microgrid applications. The advantages are that they include complimentary technologies with both high power and energy capabilities. HESS have the potential to be useful to the bulk power systems, for example to increase the value of energy produced by variable generation resource through enabling participation in ancillary service markets. Actualizing the benefits of HESSs requires optimizing the combined ramping capability of aggregated HESS resources. To provide quality ancillary service, source of HESSs locating at multiple sites of variable generation resource must be optimally sized and cohesively controlled. Optimizing aggregated resources can be formulated as an optimization problem. Successfully solving this problem not only requires using effective solution methods but also depends on accurately and rapidly setting the necessary parameters in the problem formula. This article proposes a novel framework with double-layer structure to solve the optimization problem of aggregating ramping capability. Program developed on the upper layer focuses on solving the optimization of aggregating ramping capability among multiple HESSs and is compatible with most existing optimization algorithms. Program on lower layer of the framework targets at optimizing the local control of a single HESS based on a thorough analytics of HESS operation strategy presented in this article. Result of local optimization is also provided to upper layer program for updating the parameters in formula of optimization problem. Real time hardware-in-the-loop test is conducted to verify the performance of the optimization framework developed. The work presented is expected to provide guidance for implementing this framework in practical operation.