An Unsupervised Deep Learning Approach for Scenario Forecasts

In this paper, we propose a novel scenario forecasts approach which can be applied to a broad range of power system operations (e.g., wind, solar, load) over various forecasts horizons and prediction intervals. This approach is model-free and data-driven, producing a set of scenarios that represent possible future behaviors based only on historical observations and point forecasts. It first applies a newly-developed unsupervised deep learning framework, the generative adversarial networks, to learn the intrinsic patterns in historical renewable generation data. Then by solving an optimization problem, we are able to quickly generate large number of realistic future scenarios. The proposed method has been applied to a wind power generation and forecasting dataset from national renewable energy laboratory. Simulation results indicate our method is able to generate scenarios that capture spatial and temporal correlations. Our code and simulation datasets are freely available online.Comment: Accepted to Power Systems Computation Conference 2018 Code available at https://github.com/chennnnnyize/Scenario-Forecasts-GA

Modeling and Optimization of Complex Building Energy Systems with Deep Neural Networks

Modern buildings encompass complex dynamics of multiple electrical, mechanical, and control systems. One of the biggest hurdles in applying conventional model-based optimization and control methods to building energy management is the huge cost and effort of capturing diverse and temporally correlated dynamics. Here we propose an alternative approach which is model-free and data-driven. By utilizing high volume of data coming from advanced sensors, we train a deep Recurrent Neural Networks (RNN) which could accurately represent the operation's temporal dynamics of building complexes. The trained network is then directly fitted into a constrained optimization problem with finite horizons. By reformulating the constrained optimization as an unconstrained optimization problem, we use iterative gradient descents method with momentum to find optimal control inputs. Simulation results demonstrate proposed method's improved performances over model-based approach on both building system modeling and control

State-of-Charge Aware EV Charging

Recent proliferation in electric vehicles (EVs) are posing profound impacts over the operation of electrical grids. In particular, due to the physical constraints on charging stations' capacity and uncertainty in charging demand, it becomes an emerging challenge to design high performance scheduling algorithms to better serve charging sessions. In this paper, we design a predictive charging controller by actively incorporating each EV's state-of-charge (SOC) information, which has strong effects on the utilization of dispatchable power during peak hours. Simulation results on both synthetic and real-world EV session and charging demand data demonstrate the proposed algorithm's benefits on maximizing charging throughput and achieving higher rate of feasible charging sessions while satisfying battery and station physical constraints at the same time.Comment: Best Paper, 2023 Power and Energy Society General Meeting (PESGM) on Renewables, Storage, and Electric Vehicles. Code available at https://github.com/chennnnnyize/State-Demand_Aware_EV_Chargin

BEAR: Physics-Principled Building Environment for Control and Reinforcement Learning

Recent advancements in reinforcement learning algorithms have opened doors for researchers to operate and optimize building energy management systems autonomously. However, the lack of an easily configurable building dynamical model and energy management task simulation and evaluation platform has arguably slowed the progress in developing advanced and dedicated reinforcement learning (RL) and control algorithms for building operation tasks. Here we propose "BEAR", a physics-principled Building Environment for Control And Reinforcement Learning. The platform allows researchers to benchmark both model-based and model-free controllers using a broad collection of standard building models in Python without co-simulation using external building simulators. In this paper, we discuss the design of this platform and compare it with other existing building simulation frameworks. We demonstrate the compatibility and performance of BEAR with different controllers, including both model predictive control (MPC) and several state-of-the-art RL methods with two case studies.Comment: Accepted at ACM e-Energy 2023; Code available at https://github.com/chz056/BEA