5 research outputs found
Chance-Constrained Outage Scheduling using a Machine Learning Proxy
Outage scheduling aims at defining, over a horizon of several months to
years, when different components needing maintenance should be taken out of
operation. Its objective is to minimize operation-cost expectation while
satisfying reliability-related constraints. We propose a distributed
scenario-based chance-constrained optimization formulation for this problem. To
tackle tractability issues arising in large networks, we use machine learning
to build a proxy for predicting outcomes of power system operation processes in
this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains
cheaper and more reliable plans than other candidates
Energy System Digitization in the Era of AI: A Three-Layered Approach towards Carbon Neutrality
The transition towards carbon-neutral electricity is one of the biggest game
changers in addressing climate change since it addresses the dual challenges of
removing carbon emissions from the two largest sectors of emitters: electricity
and transportation. The transition to a carbon-neutral electric grid poses
significant challenges to conventional paradigms of modern grid planning and
operation. Much of the challenge arises from the scale of the decision making
and the uncertainty associated with the energy supply and demand. Artificial
Intelligence (AI) could potentially have a transformative impact on
accelerating the speed and scale of carbon-neutral transition, as many decision
making processes in the power grid can be cast as classic, though challenging,
machine learning tasks. We point out that to amplify AI's impact on
carbon-neutral transition of the electric energy systems, the AI algorithms
originally developed for other applications should be tailored in three layers
of technology, markets, and policy.Comment: To be published in Patterns (Cell Press
Verification of Neural Network Behaviour: Formal Guarantees for Power System Applications
This paper presents for the first time, to our knowledge, a framework for
verifying neural network behavior in power system applications. Up to this
moment, neural networks have been applied in power systems as a black-box; this
has presented a major barrier for their adoption in practice. Developing a
rigorous framework based on mixed integer linear programming, our methods can
determine the range of inputs that neural networks classify as safe or unsafe,
and are able to systematically identify adversarial examples. Such methods have
the potential to build the missing trust of power system operators on neural
networks, and unlock a series of new applications in power systems. This paper
presents the framework, methods to assess and improve neural network robustness
in power systems, and addresses concerns related to scalability and accuracy.
We demonstrate our methods on the IEEE 9-bus, 14-bus, and 162-bus systems,
treating both N-1 security and small-signal stability.Comment: published in IEEE Transactions on Smart Grid
(https://ieeexplore.ieee.org/abstract/document/9141308
An Analytical Methodology To Security Constraints Management In Power System Operation
In a deregulated electricity market, Independent System Operators (ISOs) are responsible for dispatching power to the load securely, efficiently, and economically. ISO performs Security Constrained Unit Commitment (SCUC) to guarantee sufficient generation commitment, maximized social welfare and facilitating market-driven economics. A large number of security constraints would render the model impossible to solve under time requirements. Developing a method to identify the minimum set of security constraints without overcommitting is necessary to reduce Mixed Integer Linear Programming (MILP) solution time. To overcome this challenge, we developed a powerful tool called security constraint screening. The proposed approach effectively filters out non-dominating constraints by integrating virtual transactions and capturing changes online in real-time or look-ahead markets. The security-constraint screening takes advantage of both deterministic and statistical methods, which leverages mathematical modeling and historical data. Effectiveness is verified using Midcontinent Independent System Operator (MISO) data. The research also presented a data-driven approach to forecast congestion patterns in real-time utilizing machine learning applications. Studies have been conducted using real-world data. The potential benefit is to provide the day-ahead operators with a tool for supporting decision-making regarding modeling constraints