An Operations Model for Home Energy Management System Considering an Energy Storage System and Consumer Utility in a Smart Grid

2017 ◽  
Vol 27 (2) ◽  
pp. 99-125 ◽  
Author(s):  
Juhyeon Kang ◽  
◽  
Yongma Moon
Keyword(s):  
Energy Storage ◽  
Smart Grid ◽  
Energy Management ◽  
Management System ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Management System ◽  
Home Energy Management ◽  
Home Energy Management System

scholarly journals Multi-Objective Fuzzy Logic-Based Energy Management System for Microgrids with Battery and Hydrogen Energy Storage System

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1074 ◽  
Author(s):  
Francisco José Vivas ◽  
Francisca Segura ◽  
José Manuel Andújar ◽  
Adriana Palacio ◽  
Jaime Luis Saenz ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Energy Storage ◽  
Energy Management ◽  
Management System ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Management System ◽  
Hydrogen Energy ◽  
Economic Point ◽  
Load Demand

This paper proposes a fuzzy logic-based energy management system (EMS) for microgrids with a combined battery and hydrogen energy storage system (ESS), which ensures the power balance according to the load demand at the time that it takes into account the improvement of the microgrid performance from a technical and economic point of view. As is known, renewable energy-based microgrids are receiving increasing interest in the research community, since they play a key role in the challenge of designing the next energy transition model. The integration of ESSs allows the absorption of the energy surplus in the microgrid to ensure power supply if the renewable resource is insufficient and the microgrid is isolated. If the microgrid can be connected to the main power grid, the freedom degrees increase and this allows, among other things, diminishment of the ESS size. Planning the operation of renewable sources-based microgrids requires both an efficient dispatching management between the available and the demanded energy and a reliable forecasting tool. The developed EMS is based on a fuzzy logic controller (FLC), which presents different advantages regarding other controllers: It is not necessary to know the model of the plant, and the linguistic rules that make up its inference engine are easily interpretable. These rules can incorporate expert knowledge, which simplifies the microgrid management, generally complex. The developed EMS has been subjected to a stress test that has demonstrated its excellent behavior. For that, a residential-type profile in an actual microgrid has been used. The developed fuzzy logic-based EMS, in addition to responding to the required load demand, can meet both technical (to prolong the devices’ lifespan) and economic (seeking the highest profitability and efficiency) established criteria, which can be introduced by the expert depending on the microgrid characteristic and profile demand to accomplish.

scholarly journals Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2010 ◽  
Author(s):  
Sunyong Kim ◽  
Hyuk Lim
Keyword(s):  
Reinforcement Learning ◽  
Smart Grid ◽  
Energy Management ◽  
Management System ◽  
Transition Probability ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Costs ◽  
Energy Management System ◽  
Management Algorithm

A smart grid facilitates more effective energy management of an electrical grid system. Because both energy consumption and associated building operation costs are increasing rapidly around the world, the need for flexible and cost-effective management of the energy used by buildings in a smart grid environment is increasing. In this paper, we consider an energy management system for a smart energy building connected to an external grid (utility) as well as distributed energy resources including a renewable energy source, energy storage system, and vehicle-to-grid station. First, the energy management system is modeled using a Markov decision process that completely describes the state, action, transition probability, and rewards of the system. Subsequently, a reinforcement-learning-based energy management algorithm is proposed to reduce the operation energy costs of the target smart energy building under unknown future information. The results of numerical simulation based on the data measured in real environments show that the proposed energy management algorithm gradually reduces energy costs via learning processes compared to other random and non-learning-based algorithms.

scholarly journals Integration of Electric Vehicles and Energy Storage System in Home Energy Management System with Home to Grid Capability

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8557
Author(s):  
Arshad Mohammad ◽  
Mohd Zuhaib ◽  
Imtiaz Ashraf ◽  
Marwan Alsultan ◽  
Shafiq Ahmad ◽  
...  
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Energy Cost ◽  
Management System ◽  
Economic Benefits ◽  
Binary Particle Swarm Optimization ◽  
Energy Management System ◽  
Multi Objective ◽  
Home Energy Management ◽  
Home Energy Management System

In this paper, we proposed a home energy management system (HEMS) that includes photovoltaic (PV), electric vehicle (EV), and energy storage systems (ESS). The proposed HEMS fully utilizes the PV power in operating domestic appliances and charging EV/ESS. The surplus power is fed back to the grid to achieve economic benefits. A novel charging and discharging scheme of EV/ESS is presented to minimize the energy cost, control the maximum load demand, increase the battery life, and satisfy the user’s-traveling needs. The EV/ESS charges during low pricing periods and discharges in high pricing periods. In the proposed method, a multi-objective problem is formulated, which simultaneously minimizes the energy cost, peak to average ratio (PAR), and customer dissatisfaction. The multi-objective optimization is solved using binary particle swarm optimization (BPSO). The results clearly show that it minimizes the operating cost from 402.89 cents to 191.46 cents, so that a reduction of 52.47% is obtained. Moreover, it reduces the PAR and discomfort index by 15.11% and 16.67%, respectively, in a 24 h time span. Furthermore, the home has home to grid (H2G) capability as it sells the surplus energy, and the total cost is further reduced by 29.41%.

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