scholarly journals Multi-Household Energy Management in a Smart Neighborhood in the Presence of Uncertainties and Electric Vehicles

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3186
Author(s):  
Luca Serafini ◽  
Emanuele Principi ◽  
Susanna Spinsante ◽  
Stefano Squartini
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Energy Demand ◽  
Renewable Energy Sources ◽  
Energy Usage ◽  
Peak Shaving ◽  
Public And Private ◽  
Household Energy ◽  
Parking Lot ◽  
Decision Policies

The pathway toward the reduction of greenhouse gas emissions is dependent upon increasing Renewable Energy Sources (RESs), demand response, and electrification of public and private transportation. Energy management techniques are necessary to coordinate the operation in this complex scenario, and in recent years several works have appeared in the literature on this topic. This paper presents a study on multi-household energy management for Smart Neighborhoods integrating RESs and electric vehicles participating in Vehicle-to-Home (V2H) and Vehicle-to-Neighborhood (V2N) programs. The Smart Neighborhood comprises multiple households, a parking lot with public charging stations, and an aggregator that coordinates energy transactions using a Multi-Household Energy Manager (MH-EM). The MH-EM jointly maximizes the profits of the aggregator and the households by using the augmented ϵ-constraint approach. The generated Pareto optimal solutions allow for different decision policies to balance the aggregator’s and households’ profits, prioritizing one of them or the RES energy usage within the Smart Neighborhood. The experiments have been conducted over an entire year considering uncertainties related to the energy price, electric vehicles usage, energy production of RESs, and energy demand of the households. The results show that the MH-EM optimizes the Smart Neighborhood operation and that the solution that maximizes the RES energy usage provides the greatest benefits also in terms of peak-shaving and valley-filling capability of the energy demand.

scholarly journals Electric Mobility in Cities: The Case of Vienna

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 217
Author(s):  
Amela Ajanovic ◽  
Marina Siebenhofer ◽  
Reinhard Haas
Keyword(s):  
Electric Vehicles ◽  
Future Development ◽  
Urban Areas ◽  
Energy Demand ◽  
Renewable Energy Sources ◽  
Road Transport ◽  
Electric Mobility ◽  
Policy Framework ◽  
The Future ◽  
Historical Developments

Environmental problems such as air pollution and greenhouse gas emissions are especially challenging in urban areas. Electric mobility in different forms may be a solution. While in recent years a major focus was put on private electric vehicles, e-mobility in public transport is already a very well-established and mature technology with a long history. The core objective of this paper is to analyze the economics of e-mobility in the Austrian capital of Vienna and the corresponding impact on the environment. In this paper, the historical developments, policy framework and scenarios for the future development of mobility in Vienna up to 2030 are presented. A major result shows that in an ambitious scenario for the deployment of battery electric vehicles, the total energy demand in road transport can be reduced by about 60% in 2030 compared to 2018. The major conclusion is that the policies, especially subsidies and emission-free zones will have the largest impact on the future development of private and public e-mobility in Vienna. Regarding the environmental performance, the most important is to ensure that a very high share of electricity used for electric mobility is generated from renewable energy sources.

Energy Management System for Hybrid Microgrids

2021 ◽  
Vol 69 (2) ◽  
pp. 21-30
Author(s):  
Nasreddine ATTOU ◽  
Sid-Ahmed ZIDI ◽  
Mohamed KHATIR ◽  
Samir HADJERI
Keyword(s):  
Energy Management ◽  
Electricity Market ◽  
Management System ◽  
Energy Demand ◽  
Renewable Energy Sources ◽  
Supply And Demand ◽  
Energy Sources ◽  
Variable Load ◽  
Energy Management System ◽  
Price Variation

Energy management in grid-connected Micro-grids (MG) has undergone rapid evolution in recent times due to several factors such as environmental issues, increasing energy demand and the opening of the electricity market. The Energy Management System (EMS) allows the optimal scheduling of energy resources and energy storage systems in MG in order to maintain the balance between supply and demand at low cost. The aim is to minimize peaks and fluctuations in the load and production profile on the one hand, and, on the other hand, to make the most of renewable energy sources and energy exchanges with the utility grid. In this paper, our attention has been focused on a Rule-based energy management system (RB EMS) applied to a residential multi-source grid-connected MG. A Microgrid model has been implemented that combines distributed energy sources (PV, WT, BESS), a number of EVs equipped with the Vehicle to Grid technology (V2G) and variable load. Different operational scenarios were developed to see the behaviour of the implemented management system during the day, including the random demand profile of EV users, the variation in load and production, grid electricity price variation. The simulation results presented in this paper demonstrate the efficacy of the suggested EMS and confirm the strategy's feasibility as well as its ability to properly share power among different sources, loads and vehicles by obeying constraints on each element.

scholarly journals QoE-Aware Smart Home Energy Management Considering Renewables and Electric Vehicles

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2304 ◽  
Author(s):  
Mingfu Li ◽  
Guan-Yi Li ◽  
Hou-Ren Chen ◽  
Cheng-Wei Jiang
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Smart Home ◽  
Control Algorithm ◽  
Renewable Energy Sources ◽  
Peak Load ◽  
Electricity Bill ◽  
Home Energy Management ◽  
Smart Appliance ◽  
User Comfort

To reduce the peak load and electricity bill while preserving the user comfort, a quality of experience (QoE)-aware smart appliance control algorithm for the smart home energy management system (sHEMS) with renewable energy sources (RES) and electric vehicles (EV) was proposed. The proposed algorithm decreases the peak load and electricity bill by deferring starting times of delay-tolerant appliances from peak to off-peak hours, controlling the temperature setting of heating, ventilation, and air conditioning (HVAC), and properly scheduling the discharging and charging periods of an EV. In this paper, the user comfort is evaluated by means of QoE functions. To preserve the user’s QoE, the delay of the starting time of a home appliance and the temperature setting of HVAC are constrained by a QoE threshold. Additionally, to solve the trade-off problem between the peak load/electricity bill reduction and user’s QoE, a fuzzy logic controller for dynamically adjusting the QoE threshold to optimize the user’s QoE was also designed. Simulation results demonstrate that the proposed smart appliance control algorithm with a fuzzy-controlled QoE threshold significantly reduces the peak load and electricity bill while optimally preserving the user’s QoE. Compared with the baseline case, the proposed scheme reduces the electricity bill by 65% under the scenario with RES and EV. Additionally, compared with the method of optimal scheduling of appliances in the literature, the proposed scheme achieves much better peak load reduction performance and user’s QoE.

scholarly journals Optimum Resilient Operation and Control DC Microgrid Based Electric Vehicles Charging Station Powered by Renewable Energy Sources

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4240 ◽  
Author(s):  
Khairy Sayed ◽  
Ahmed G. Abo-Khalil ◽  
Ali S. Alghamdi
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Solar Pv ◽  
Dc Microgrid ◽  
Charging Station ◽  
And Control ◽  
Ev Charging

This paper introduces an energy management and control method for DC microgrid supplying electric vehicles (EV) charging station. An Energy Management System (EMS) is developed to manage and control power flow from renewable energy sources to EVs through DC microgrid. An integrated approach for controlling DC microgrid based charging station powered by intermittent renewable energies. A wind turbine (WT) and solar photovoltaic (PV) arrays are integrated into the studied DC microgrid to replace energy from fossil fuel and decrease pollution from carbon emissions. Due to the intermittency of solar and wind generation, the output powers of PV and WT are not guaranteed. For this reason, the capacities of WT, solar PV panels, and the battery system are considered decision parameters to be optimized. The optimized design of the renewable energy system is done to ensure sufficient electricity supply to the EV charging station. Moreover, various renewable energy technologies for supplying EV charging stations to improve their performance are investigated. To evaluate the performance of the used control strategies, simulation is carried out in MATLAB/SIMULINK.

scholarly journals Hybrid Microgrid Energy Management and Control Based on Metaheuristic-Driven Vector-Decoupled Algorithm Considering Intermittent Renewable Sources and Electric Vehicles Charging Lot

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3423 ◽  
Author(s):  
Tawfiq M. Aljohani ◽  
Ahmed F. Ebrahim ◽  
Osama Mohammed
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Reactive Power ◽  
Renewable Energy Sources ◽  
Synchronous Generator ◽  
Photovoltaic System ◽  
Severe Condition ◽  
Electric Vehicle Charging ◽  
Bidirectional Converter ◽  
And Control

Energy management and control of hybrid microgrids is a challenging task due to the varying nature of operation between AC and DC components which leads to voltage and frequency issues. This work utilizes a metaheuristic-based vector-decoupled algorithm to balance the control and operation of hybrid microgrids in the presence of stochastic renewable energy sources and electric vehicles charging structure. The AC and DC parts of the microgrid are coupled via a bidirectional interlinking converter, with the AC side connected to a synchronous generator and portable AC loads, while the DC side is connected to a photovoltaic system and an electric vehicle charging system. To properly ensure safe and efficient exchange of power within allowable voltage and frequency levels, the vector-decoupled control parameters of the bidirectional converter are tuned via hybridization of particle swarm optimization and artificial physics optimization. The proposed control algorithm ensures the stability of both voltage and frequency levels during the severe condition of islanding operation and high pulsed demands conditions as well as the variability of renewable source production. The proposed methodology is verified in a state-of-the-art hardware-in-the-loop testbed. The results show robustness and effectiveness of the proposed algorithm in managing the real and reactive power exchange between the AC and DC parts of the microgrid within safe and acceptable voltage and frequency levels.

scholarly journals An Innovative Stochastic Multi-Agent-Based Energy Management Approach for Microgrids Considering Uncertainties

Inventions ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 37 ◽  
Author(s):  
Sajad Ghorbani ◽  
Rainer Unland ◽  
Hassan Shokouhandeh ◽  
Ryszard Kowalczyk
Keyword(s):  
Energy Management ◽  
Demand Response ◽  
Energy Demand ◽  
Search Algorithm ◽  
Renewable Energy Sources ◽  
Management Approach ◽  
Agent Based ◽  
Multi Agent ◽  
Power Outputs ◽  
Microgrid Energy Management

In microgrids a major share of the energy production comes from renewable energy sources such as photovoltaic panels or wind turbines. The intermittent nature of these types of producers along with the fluctuation in energy demand can destabilize the grid if not dealt with properly. This paper presents a multi-agent-based energy management approach for a non-isolated microgrid with solar and wind units and in the presence of demand response, considering uncertainty in generation and load. More specifically, a modified version of the lightning search algorithm, along with the weighted objective function of the current microgrid cost, based on different scenarios for the energy management of the microgrid, is proposed. The probability density functions of the solar and wind power outputs, as well as the demand of the households, have been used to determine the amount of uncertainty and to plan various scenarios. We also used a particle swarm optimization algorithm for the microgrid energy management and compared the optimization results obtained from the two algorithms. The simulation results show that uncertainty in the microgrid normally has a significant effect on the outcomes, and failure to consider it would lead to inaccurate management methods. Moreover, the results confirm the excellent performance of the proposed approach.

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