scholarly journals Optimal Scheduling of Residential Home Appliances by Considering Energy Storage and Stochastically Modelled Photovoltaics in a Grid Exchange Environment Using Hybrid Grey Wolf Genetic Algorithm Optimizer

2019 ◽  
Vol 9 (23) ◽  
pp. 5226 ◽  
Author(s):  
Iqbal ◽  
Sajjad ◽  
Amin ◽  
Haroon ◽  
Liaqat ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Energy Storage ◽  
Energy Management ◽  
Storage System ◽  
Energy Storage System ◽  
Grey Wolf ◽  
Photovoltaic Generation ◽  
Average Ratio ◽  
Consumption Cost

The transformation of a conventional power system to a smart grid has been underway over the last few decades. A smart grid provides opportunities to integrate smart homes with renewable energy resources (RERs). Moreover, it encourages the residential consumers to regulate their home energy consumption in an effective way that suits their lifestyle and it also helps to preserve the environment. Keeping in mind the techno-economic reasons for household energy management, active participation of consumers in grid operations is necessary for peak reduction, valley filling, strategic load conservation, and growth. In this context, this paper presents an efficient home energy management system (HEMS) for consumer appliance scheduling in the presence of an energy storage system and photovoltaic generation with the intention to reduce the energy consumption cost determined by the service provider. To study the benefits of a home-to-grid (H2G) energy exchange in HEMS, photovoltaic generation is stochastically modelled by considering an energy storage system. The prime consideration of this paper is to propose a hybrid optimization approach based on heuristic techniques, grey wolf optimization, and a genetic algorithm termed a hybrid grey wolf genetic algorithm to model HEMS for residential consumers with the objectives to reduce energy consumption cost and the peak-to-average ratio. The effectiveness of the proposed scheme is validated through simulations performed for a residential consumer with several domestic appliances and their scheduling preferences by considering real-time pricing and critical peak-pricing tariff signals. Results related to the reduction in the peak-to-average ratio and energy cost demonstrate that the proposed hybrid optimization technique performs well in comparison with different meta-heuristic techniques available in the literature. The findings of the proposed methodology can further be used to calculate the impact of different demand response signals on the operation and reliability of a power system.

scholarly journals A Study of Control Methodologies for the Trade-Off between Battery Aging and Energy Consumption on Electric Vehicles with Hybrid Energy Storage Systems

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 600
Author(s):  
Kevin Mallon ◽  
Francis Assadian
Keyword(s):  
Energy Consumption ◽  
Energy Storage ◽  
Electric Vehicle ◽  
Energy Management ◽  
Storage Systems ◽  
Storage System ◽  
Energy Storage System ◽  
Trade Off ◽  
Hybrid Energy ◽  
Hybrid Energy Storage

Hybrid and electric vehicle batteries deteriorate from use due to irreversible internal chemical and mechanical changes, resulting in decreased capacity and efficiency of the energy storage system. This article investigates the modeling and control of a lithium-ion battery and ultracapacitor hybrid energy storage system for an electric vehicle for improved battery lifespan and energy consumption. By developing a control-oriented aging model for the energy storage components and integrating the aging models into an energy management system, the trade-off between battery degradation and energy consumption can be minimized. This article produces an optimal aging-aware energy management strategy that controls both battery and ultracapacitor aging and compares these results to strategies that control only battery aging, strategies that control battery aging factors but not aging itself, and non-optimal benchmark strategies. A case study on an electric bus with variously-sized hybrid energy storage systems shows that a strategy designed to control battery aging, ultracapacitor aging, and energy losses simultaneously can achieve a 28.2% increase to battery lifespan while requiring only a 7.0% decrease in fuel economy.

scholarly journals Privacy-Preserving Energy Management of a Shared Energy Storage System for Smart Buildings: A Federated Deep Reinforcement Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4898
Author(s):  
Sangyoon Lee ◽  
Le Xie ◽  
Dae-Hyun Choi
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Energy Storage ◽  
Energy Management ◽  
Storage System ◽  
Energy Storage System ◽  
Privacy Preserving ◽  
Smart Buildings ◽  
Energy Management Systems ◽  
Building Energy Management

This paper proposes a privacy-preserving energy management of a shared energy storage system (SESS) for multiple smart buildings using federated reinforcement learning (FRL). To preserve the privacy of energy scheduling of buildings connected to the SESS, we present a distributed deep reinforcement learning (DRL) framework using the FRL method, which consists of a global server (GS) and local building energy management systems (LBEMSs). In the framework, the LBEMS DRL agents share only a randomly selected part of their trained neural network for energy consumption models with the GS without consumer’s energy consumption data. Using the shared models, the GS executes two processes: (i) construction and broadcast of a global model of energy consumption to the LBEMS agents for retraining their local models and (ii) training of the SESS DRL agent’s energy charging and discharging from and to the utility and buildings. Simulation studies are conducted using one SESS and three smart buildings with solar photovoltaic systems. The results demonstrate that the proposed approach can schedule the charging and discharging of the SESS and an optimal energy consumption of heating, ventilation, and air conditioning systems in smart buildings under heterogeneous building environments while preserving the privacy of buildings’ energy consumption.

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