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The Evolving Electric Power Grid -Energy Internet, Iot and AI The Evolving Electric Power Grid -Energy Internet, IoT and AI Di Shi AI & System Analytics GEIRI North America (GEIRINA) www.geirina.net San Jose, CA, US May 8, 2020 @CURENT Strategic Planning Meeting Outline 1. Recent Development: Energy Internet and IoT 2. The New-gen. AI Technologies 3. Challenges and Opportunities Unbalance in Resources and Load Resource 76% of coal in North and Northwest 80% of hydropower in Southwest, mainly in upper stream of Yangtze River All inland wind in Northwest Solar resources mainly in Northwest Load 70%+ of load in Central and Eastern parts Fig. West to east power transmission Transmission Distances between resource and load center reaches up to 2000+km UHVDC and UHVAC are good options to transfer huge amount of power over long distance Challenges Hybrid operation of AC and DC systems System stability, security, and reliability Fig. China power transmission framework 3 4 Wind Solar 18,000MW 30,120MW 48,200MW 19.400MW Thermal 42,200MW 32.400MW 1,000MW Hydro 8,000MW 20,000MW 2,000MW 8,000MW HVDC HVDC or HVAC 28% Investment/MW UHVDC 50% Loss/MW UHVDC Developmental Trend: Source-Grid-Load Interaction Source-Grid-load interaction proposed by SGCC Jiangsu UHVDC/UHVAC/HVDC/HVAC development in China Technical aspect: Holistic generation- transmission-demand coordination Conventional Conventional load generation Pumped- Energy storage storage Source Grid Load Renewable generation EV/PHEV Battery energy storage Power electronics interfaced load Market aspect: real-time pricing, incentives, etc The existing load control methods are of low UHVDC Fault Caused Outage in Brazil granularity, and the load response is slow. The load monitoring/control is highly dependent on the SCADA network and it costs much to extend to end-user level. It is urgent to develop cost-effective methods to integrate higher levels of renewable generation. G. Wen, G. Hu, J. Hu, X. Shi and G. Chen, "Frequency Regulation of Source-Grid-Load Systems: A Compound Control Strategy," in IEEE Transactions on Industrial Informatics, vol. 12, no. 1, pp. 69-78, Feb. 2016. Developmental Trend: Internet-of-Things (IoT) Pic from: https://www.valuecoders.com/blog/technology-and-apps/11-mobile-app-development-trends-stay-2017/ • Advanced metering infrastructure (AMI) • SCADA (supervisory control and data acquisition) • Smart inverters • Remote control operation of energy consuming devices • Various type of interconnected sensors Developmental Trend: Energy Internet (Interconnection) Pic from: https://www.economist.com/technology-quarterly/2004/03/13/building-the-energy-internet 7 Known Challenges and Opportunities Challenges Opportunities Increasing dynamics and stochastics. The need for faster and enhanced system situational awareness tools/platforms. Traditional operational rules and • WAMS with good coverage of PMUs procedures, which are derived from offline • Point-on-wave measurements/devices studies or historical experiences, tend to be • Progress in computation/simulation less optimal (over-conservative or risky). • Recent progress in AI (Deep learning) Limited capabilities to adapt to various, The need for faster, preferably real-time, including unknown, system operating decision-support tools/platforms. conditions. • Most existing operational rules are offline Causes determined considering the worst-case • Increasing penetration of DERs scenarios • Transportation electrification • Lack of preventive/corrective measures to • Fast demand responses mitigate operational risks • New market behaviors • Proven capability of AI in decision • Inaccurate grid models making/support under highly complexed situations. Lack of approaches to collect and synthesize overwhelming amounts of data from millions of smart sensors nationwide to make timely decisions on how to best allocate energy resources. Changes in Way of Thinking Data Rules Traditional Approach Answers New Approach Rules Data (Model-centric) Answers (Data-centric, hybrid approaches) Automatic Program Autonomous Program (Fixed and pre-determined rules, (Intelligent and evolving) automatic execution) 9 The New-gen. AI Technologies Oct. 2017,《Nature》, AlphaGo Zero beat Dec. 2018, AlphaStar mastered the real-time Robot arms learn to pick things up, AlphaGo Lee with a score of 100:0, after 3 strategy game StarCraft II and beat top teams, hard and soft objects in different ways, days’ training by learning from scratch. by learning from human and then self play. with little human interference. Credit of pics: Google Core technologies: Deep learning + Reinforcement learning 2015, AlphaGoFan (5:0 vs Hui Fan) 2017, AlphaZero Google acquired 2016, AlphaGoLee (4:1 vs Lee Sedol) 2019, MuZero 2018, AlphaStar Deep Mind Founded Deep Mind 2017, AlphaGoMaster (3:0 Jie Ke) 2010 2014 2015-2017 2018 2019 Notes: • No/limited labeled data (raw data input), play against itself for improvement. • Learn from human, and then play against itself for improvement. Hints for power system applications： • Lack of large amount of labeled data, especially event data. • Generate reasonable data sets based on existing/typical data/operating conditions. • Combine AI with classical power system theories/computations/metrics. 10 Deep Learning Supervised Unsupervised Semi-supervised Reinforcement LearningDeep LearningLearning is part ofLearning the Learning target many unlabeled & reward environment machineerror learningunlabeled family based labeled data few labeled data & state data In Out In Out onIn artificial Out neural networkIn with Out Applicationmany layers. ApplicationDeep learningApplication can Application Classification Clustering Google Photos DeepMind’s AlphaGo Predictbe a target supervised, Visualization unsupervised Webpage classification AlphaZero numeric value Dimensionality reduction AlphaStar and semi Anomaly- detectionsupervised. Fire-extinguish robots Common Algorithms Common Algorithms Common Algorithms Common Algorithms o k-Nearest Neighbors o k-Means o Combination of o Dynamic programming o Linear Regression o Hierarchical Cluster Analysis unsupervised and o Monte Carlo o Logistic Regression o Principal Component supervised learning o Q-Learning o Decision Trees Analysis o SARSA o Naïve Bayes o DBSCAN o Deep Q Network (DQN) o Support Vector Machine o Local Outlier Factor (LOF) o Asynchronous Actor-Critic (SVM) o Autoencoders Agent (A3C) o Neural Networks o Deep Belief Nets o Deep Deterministic Policy DRL=o Ganerative DLAdversarial + RL Gradient (DDPG) Networks 11 The Grid Mind Vision • Grid Mind: A measurement-driven, grid-interactive, self-evolving, and open platform for power system autonomous dispatch and control. In the short term, create EXAMPLES of AlphaZero in power systems. In the mid-term, Grid Mind serves as an assistant to grid operators. In the long term, Grid Mind will be the core of power system operation ROBOT. Goal: To develop a platform and tools that can transform massive amount of measurements into actionable decisions in real time. PMUs Power Systems Synchrophasor Now measurements Situational Awareness Decision Action Perception Comprehension Projection SCADA Power Systems WAMS Image, Reinforcement/ feedback Linear/hybrid Grid States Video, text, SE etc. Goal / Future Grid Eye Grid Mind Action Situational Decision making awareness Execute in sub-second Operator Offline training experience using HPC Autonomous Voltage Control (AVC) on IEEE 14-Bus System Either no violations or one action taken Two actions taken Three actions taken 60%-120% random system load changes Four actions taken Reward Five actions taken Episode Actions – Vset (Episode 8 and 5000) States – Bus Voltage (Episode 8 and 5000) 13 13 AVC: DQN and DDPG Agents for Illinois 200-bus System 60%-120% random load changes are applied to each episode DQN Agent No violation or one Regional voltage control is iteration step considered for DQN agent: 5 adjacent generators with 30 Training Testing interconnected buses in the Two iteration steps neighborhood subsystem Three iteration steps Four iteration steps Episodes Rewards Episodes More than five iteration steps DDPG Agent No violation or one iteration step Training Testing Two iteration steps Three iteration steps Four iteration steps Episodes Rewards Episodes More than five iteration steps Episodes After 10,000 episodes’ learning, the designed DRL agents start to master the voltage control problem in the 200-bus system by making decisions autonomously. *The Illinois 200-bus system model is from https://egriddata.org/dataset/illinois-200-bus-system-activsg200 14 Further Testing-200 Bus System with Random N-1 • Test the DRL agent under different loading conditions: heavily loaded, fully loaded, and lightly loaded. • Consider different topological changes. For example, random line tripping contingency or N-1 conditions. DDPG; 60%-140; Enforcing Q limit Either no violation DQN; 60%-140%; Enforcing Q limit or 1 iteration step 2 iteration steps 3 iteration steps 4 iteration steps More than 5 4 x10 iteration steps x104 Episode Episode Observations: 1. With little human interference, the designed agents work very well under all testing conditions. 2. The results comply with basic power system principles and engineering judgement very well. 15 3. The proposed framework is promising for power system autonomous operation and control. Demo Step 1: Perturb the system Step 2: Check for voltage violations Step 4: See the results Step 3: Run Grid Mind Check the following links for the demo: https://geirina.net/assets/pdf/GridMindDemo_JD4.mp4 https://geirina.net/assets/pdf/JiangsuDemo.mp4
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