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Power grid data are going big with the deployment of various sensors. The big data in power grids creates huge opportunities for applying artificial intelligence technologies to improve resilience and reliability. This paper introduces multiple real-world applications based on artificial intelligence to improve power grid situational awareness and resilience. These applications include event identification, inertia estimation, event location and magnitude estimation, data authentication, control, and stability assessment. These applications are operating on a real-world system called FNET-GridEye, which is a wide-area measurement network and arguably the world-largest cyber-physical system that collects power grid big data. These applications showed much better performance compared with conventional approaches and accomplished new tasks that are impossible to realized using conventional technologies. These encouraging results demonstrate that combining power grid big data and artificial intelligence can uncover and capture the non-linear correlation between power grid data and its stabilities indices and will potentially enable many advanced applications that can significantly improve power grid resilience.
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The cost of the power distribution infrastructures is driven by the peak power encountered in the system. Therefore, the distribution network operators consider billing consumers behind a common transformer in the function of their peak demand and leave it to the consumers to manage their collective costs. This management problem is, however, not trivial. In this paper, we consider a multi-agent residential smart grid system, where each agent has local renewable energy production and energy storage, and all agents are connected to a local transformer. The objective is to develop an optimal policy that minimizes the economic cost consisting of both the spot-market cost for each consumer and their collective peak-power cost. We propose to use a parametric Model Predictive Control (MPC)-scheme to approximate the optimal policy. The optimality of this policy is limited by its finite horizon and inaccurate forecasts of the local power production-consumption. A Deterministic Policy Gradient (DPG) method is deployed to adjust the MPC parameters and improve the policy. Our simulations show that the proposed MPC-based Reinforcement Learning (RL) method can effectively decrease the long-term economic cost for this smart grid problem.
This brief introduction to Model Predictive Control specifically addresses stochastic Model Predictive Control, where probabilistic constraints are considered. A simple linear system subject to uncertainty serves as an example. The Matlab code for this stochastic Model Predictive Control example is available online.
Motivated by FERCs recent direction and ever-growing interest in cloud adoption by power utilities, a Task Force was established to assist power system practitioners with secure, reliable and cost-effective adoption of cloud technology to meet various business needs. This paper summarizes the business drivers, challenges, guidance, and best practices for cloud adoption in power systems from the Task Forces perspective, after extensive review and deliberation by its members that include grid operators, utility companies, software vendors and cloud providers. The paper begins by enumerating various business drivers for cloud adoption in the power industry. It follows with the discussion of challenges and risks of migrating power grid utility workloads to cloud. Next for each corresponding challenge or risk, the paper provides appropriate guidance. Importantly, the guidance is directed toward power industry professionals who are considering cloud solutions and are yet hesitant about the practical execution. Finally, to tie all the sections together, the paper documents various real-world use cases of cloud technology in the power system domain, which both the power industry practitioners and software vendors can look forward to design and select their own future cloud solutions. We hope that the information in this paper will serve as useful guidance for the development of NERC guidelines and standards relevant to cloud adoption in the industry.
In modern power grids, a local failure or attack can trigger catastrophic cascading failures, which make it challenging to assess the attack vulnerability of power grids. In this Brief, we define the $K$-link attack problem and study the attack vulnerability of power grids under cascading failures. Particularly, we propose a link centrality measure based on both topological and electrical properties of power grids. According to this centrality, we propose a greedy attack algorithm and an optimal attack algorithm. Simulation results on standard IEEE bus test data show that the optimal attack is better than the greedy attack and the traditional PSO-based attack in fracturing power grids. Moreover, the greedy attack has smaller computational complexity than the optimal attack and the PSO-based attack with an adequate attack efficiency. Our work helps to understand the vulnerability of power grids and provides some clues for securing power grids.
Artificial Intelligence (AI) has already made a huge impact on our current technological trends. Through AI developments, machines are now given power and intelligence to behave and work like human mind. In this research project, we propose and implement an AI based health physician system that would be able to interact with the patient, do the diagnosis and suggest quick remedy or treatment of their problem. A decision tree algorithm is implemented in order to follow a top down searching approach to identify and diagnose the problem and suggest a possible solution. The system uses a questionnaire based approach to query the user (patient) about various Symptoms, based on which a decision is made and a medicine is recommended
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