An official website of the United States government
Reducing buildings’ carbon emissions is an important sustainability challenge. While scheduling flexible building loads has been previously used for a variety of grid and energy optimizations, carbon footprint reduction using such flexible loads poses new challenges since such methods need to balance both energy and carbon costs while also reducing user inconvenience from delaying such loads. This article highlights the potential conflict between electricity prices and carbon emissions and the resulting tradeoffs in carbon-aware and cost-aware load scheduling. To address this tradeoff, we propose GreenThrift, a home automation system that leverages the scheduling capabilities of smart appliances and knowledge of future carbon intensity and cost to reduce both the carbon emissions and costs of flexible energy loads. At the heart of GreenThrift is an optimization technique that automatically computes schedules based on user configurations and preferences. We evaluate the effectiveness of GreenThrift using real-world carbon intensity data, electricity prices, and load traces from multiple locations and across different scenarios and objectives. Our results show that GreenThrift can replicate the offline optimal and retains 97% of the savings when optimizing the carbon emissions. Moreover, we show how GreenThrift can balance the conflict between carbon and cost and retain 95.3% and 85.5% of the potential carbon and cost savings, respectively.
more »
« less
A Deep Reinforcement Learning Based Approach for Home Energy Management System
Home energy management system (HEMS) enables residents to actively participate in demand response (DR) programs. It can autonomously optimize the electricity usage of home appliances to reduce the electricity cost based on time-varying electricity prices. However, due to the existence of randomness in the pricing process of the utility and resident's activities, developing an efficient HEMS is challenging. To address this issue, we propose a novel home energy management method for optimal scheduling of different kinds of home appliances based on deep reinforcement learning (DRL). Specifically, we formulate the home energy management problem as an MDP considering the randomness of real-time electricity prices and resident's activities. A DRL approach based on proximal policy optimization (PPO) is developed to determine the optimal DR scheduling strategy. The proposed approach does not need any information on the appliances' models and distribution knowledge of the randomness. Simulation results verify the effectiveness of our proposed approach.
more »
« less
- Award ID(s):
- 1917275
- PAR ID:
- 10158893
- Date Published:
- Journal Name:
- IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT 2020)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Those who rely on electricity-dependent durable medical equipment (DME) often struggle to use their medical devices during prolonged power outages. With the increasing frequency of natural disasters and the growing use of electricity-dependent home medical devices, in addition to the continued integration of home-level renewable energy and mobile storage systems such as electric vehicles with vehicle-to-home (V2H) capabilities, home energy management systems (HEMS) must prioritize life-essential medical loads during extended power outages. This work integrates electricity-dependent DME into home energy management optimization. An oxygen concentrator and a hemodialysis machine are used as examples of medical devices with high power demands and distinct usage patterns. The HEMS model is formulated as a mixed-integer linear program (MILP) to minimize the total weighted load curtailment and thermal discomfort during extended outage scenarios. The results demonstrate that the HEMS is effective in sustaining DME operation.more » « less
-
This paper proposes a home energy management system (HEMS) while considering the residential occupant’s clothing integrated thermal comfort and electrical vehicles (EV) state-of-charge (SOC) concern. An adaptive dynamic program- ming (ADP) based HEMS model is proposed to optimally determine the setpoints of heating, ventilation, air conditioning (HVAC), the donning/doffing decisions for the clothing conditions and charging/discharging of EV while taking into account the uncertainties in outside temperature and EV arrival SOC. We use model predictive control (MPC) to simulate a multi-day energy management of a residential house equipped with the proposed HEMS. The proposed HEMS is compared with a baseline case without the HEMS. The simulation results show that a 47.5% of energy cost saving can be achieved by the proposed HEMS while maintaining satisfactory occupant thermal comfort and negligible EV SOC concerns.more » « less
-
With increasing energy prices, low income households are known to forego or minimize the use of electricity to save on energy costs. If a household is on a prepaid electricity program, it can be automatically and immediately disconnected from service if there is no balance in its prepaid account. Such households need to actively ration the amount of energy they use by deciding which appliances to use and for how long. We present a tool that helps households extend the availability of their critical appliances by limiting the use of discretionary ones, and prevent disconnections. The proposed method is based on a linear optimization problem that only uses average power demand as an input and can be solved to optimality using a simple greedy approach. We compare the model with two mixed-integer linear programming models that require more detailed demand forecasts and optimization solvers for implementation. In a numerical case study based on real household data, we assess the performance of the different models under different accuracy and granularity of demand forecasts. Our results show that our proposed linear model is much simpler to implement, while providing similar performance under realistic circumstancesmore » « less
-
Rooftop photovoltaics (PV) and electrical vehicles (EV) have become more economically viable to residential customers. Most existing home energy management systems (HEMS) only focus on the residential occupants’ thermal comfort in terms of indoor temperature and humidity while neglecting their other behaviors or concerns. This paper aims to integrate residential PV and EVs into the HEMS in an occupant-centric manner while taking into account the occupants’ thermal comfort, clothing behaviors, and concerns on the state-of-charge (SOC) of EVs. A stochastic adaptive dynamic programming (ADP) model was proposed to optimally determine the setpoints of heating, ventilation, air conditioning (HVAC), occupant’s clothing decisions, and the EV’s charge/discharge schedule while considering uncertainties in the outside temperature, PV generation, and EV’s arrival SOC. The nonlinear and nonconvex thermal comfort model, EV SOC concern model, and clothing behavior model were holistically embedded in the ADP-HEMS model. A model predictive control framework was further proposed to simulate a residential house under the time of use tariff, such that it continually updates with optimal appliance schedules decisions passed to the house model. Cosimulations were carried out to compare the proposed HEMS with a baseline model that represents the current operational practice. The result shows that the proposed HEMS can reduce the energy cost by 68.5% while retaining the most comfortable thermal level and negligible EV SOC concerns considering the occupant’s behaviors.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISGT45199.2020.9087647
