An official website of the United States government
Traditional load shedding schemes can be inadequate in grids with high renewable penetration, leading to unstable events and unnecessary grid islanding. Although for both manual and automatic operating modes load shedding areas have been predefined by grid operators, they have remained fixed, and may be sub-optimal due to dynamic operating conditions. In this work, a distributed tri-level linear programming model for automatic load shedding to avoid system islanding is presented. Preventing islanding is preferred because it reduces the need for additional load shedding besides the disconnection of transmission lines between islands. This is crucial as maintaining the local generation-demand balance is necessary to preserve frequency stability. Furthermore, uneven distribution of generation resources among islands can lead to increased load shedding, causing economic and reliability challenges. This issue is further compounded in modern power systems heavily dependent on non-dispatchable resources like wind and solar. The upper-level model uses complex power flow measurements to determine the system areas to shed load depending on actual operating conditions using a spectral clustering approach. The mid-level model estimates the area system state, while the lower-level model determines the locations and load values to be shed. The solution is practical and promising for real-world applications.
more »
« less
Exploring Multi-Objective Transmission Planning for Investment-Constrained Power Systems
In power systems comprised of a small number of generators and lines, additional investment significantly affects reliability, debt burden, and operating costs. Wise selection of candidate investments balancing multiple objectives is crucial, especially in developing countries where load shedding may already be in effect. In this work, a static transmission expansion methodology is presented using a multi-objective optimization framework, where investment cost, operating cost, and load shedding cost are combined. Pareto fronts are computed and examined to demonstrate trade-offs and sensitivities evident in the 6-bus Garver model, showing the applicability of the proposed approach.
more »
« less
- Award ID(s):
- 1757207
- PAR ID:
- 10227879
- Date Published:
- Journal Name:
- 2020 IEEE PES/IAS PowerAfrica
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Wildfires pose a growing risk to public safety in regions like the western United States, and, historically, electric power systems have ignited some of the most destructive wildfires. To reduce wildfire ignition risks, power system operators preemptively de-energize high-risk power lines during extreme wildfire conditions as part of "Public Safety Power Shutoff" (PSPS) events. While capable of substantially reducing acute wildfire risks, PSPS events can also result in significant amounts of load shedding as the partially de-energized system may not be able to supply all customer demands. In this work, we investigate the extent to which infrastructure investments can support system operations during PSPS events by enabling reduced load shedding and wildfire ignition risk. We consider the installation of grid-scale batteries, solar PV, and line hardening or maintenance measures (e.g., undergrounding or increased vegetation management). Optimally selecting the locations, types, and sizes of these infrastructure investments requires considering the line de-energizations associated with PSPS events. Accordingly, this paper proposes a multi-period optimization formulation that locates and sizes infrastructure investments while simultaneously choosing line de-energizations to minimize wildfire ignition risk and load shedding. The proposed formulation is evaluated using two geolocated test cases along with realistic infrastructure investment parameters and actual wildfire risk data from the US Geological Survey. We evaluate the performance of investment choices by simulating de-energization decisions for the entire 2021 wildfire season with optimized infrastructure placements. With investment decisions varying significantly for different test cases, budgets, and operator priorities, the numerical results demonstrate the proposed formulation's value in tailoring investment choices to different settings.more » « less
-
We propose a multistage multiscale linear stochastic model to optimize electricity generation, storage, and transmission investments over a long planning horizon. The multiscale structure captures ‘large-scale’ uncertainties, such as investment and fuel-cost changes and long-run demand-growth rates, and ‘small-scale’ uncertainties, such as hour-to-hour demand and renewable-availability uncertainty. The model also includes a detailed treatment of operating periods so that the effect of dispatch decisions on long-term investments are captured. The proposed model can be large in size. The progressive hedging algorithm is applied to decompose the model by scenario, greatly reducing computation times. We also derive bounds on the optimal objective-function value, to assess solution quality. We use a case study based on the state of Texas to demonstrate the model and show the benefits of its detailed representation of the operating periods in making investment decisions.more » « less
-
Abstract Capturing and sequestering carbon dioxide (CO2) from the atmosphere via large‐scale direct air capture (DAC) deployment is critical for achieving net‐zero emissions. Large‐scale DAC deployment, though, will require significant cost reductions in part through policy and investment support. This study evaluates the impact of policy interventions on DAC cost reduction by integrating energy system optimization and learning curve models. We examine how three policy instruments—incremental deployment, accelerated deployment, and R&D‐driven innovation—impact DAC learning investment, which is the total investment required until the technology achieves cost parity with conventional alternatives or target cost. Our findings show that while incremental deployment demands significant learning investment, R&D‐driven innovation is considerably cheaper at cost reduction. Under a baseline 8% learning rate, incremental deployment may require up to $998 billion to reduce costs from $1,154 to $400/tCO2, while accelerated deployment support could save approximately $7 billion on that investment. In contrast, R&D support achieves equivalent cost reductions at less than half the investment of incremental deployment. However, the effectiveness of R&D intervention varies with learning rates and R&D breakthroughs. R&D yields net benefits in all cases except at extremely low breakthroughs (5%) and very high learning rates (20%), where they are slightly more expensive. For learning rates below 20%, R&D provides net benefits even at minimal breakthroughs. These findings underscore the need for comprehensive public policy strategies that balance near‐term deployment incentives with long‐term innovation investments if we are to ensure DACS becomes a viable technology for mitigating climate change.more » « less
-
Abstract Enhancing the resilience and reliability of power grids is crucial amid rising cyber threats and system complexities. To address these challenges, this paper proposes an energy‐efficient, consortium blockchain‐based global alarm system for power grid management. Using smart contracts and the proof of‐authority consensus algorithm, the alarm system triggers global alarms upon detecting local anomalies, ensuring a prompt response to partition the power grid and mitigate failures. The effectiveness is validated by simulating the Iberian power system with 15 providers from various regions. Key metrics, such as load shedding, damage reduction, energy consumption, latency, and transaction costs, are used to assess the performance. Through simulations, we show that the blockchain‐based system effectively limits the damage propagation and the load shedding during cascading failures by delaying the onset of instability and maintaining lower damage levels compared to non‐blockchain scenarios. Our investigations reveal that the proposed global alarm mechanism reduces the damage and load shedding by up to 29% and 87%, respectively, showcasing its potential for preventing widespread outages.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/powerafrica49420.2020.9219964
