scholarly journals Modified PSO Algorithm for Real-Time Energy Management in Grid-Connected Microgrids

2018 ◽  
Author(s):  
Md Alamgir Hossain ◽  
Hemanshu Roy Pota ◽  
Stefano Squartini ◽  
Ahmed Fathi Abdou
Keyword(s):  
Cost Function ◽  
Real Time ◽  
Energy Management ◽  
Storage System ◽  
Pso Algorithm ◽  
Operational Cost ◽  
Electricity Pricing ◽  
Optimal Operating ◽  
The Cost ◽  
Operating Points

Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating a cost function, it is suitably analysed and then a dynamic penalty function in order to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management.

scholarly journals Modified PSO Algorithm for Real-Time Energy Management in Grid-Connected Microgrids

2018 ◽  
Author(s):  
Md Alamgir Hossain ◽  
Hemanshu Roy Pota ◽  
Stefano Squartini ◽  
Ahmed Fathi Abdou
Keyword(s):  
Cost Function ◽  
Real Time ◽  
Energy Management ◽  
Storage System ◽  
Pso Algorithm ◽  
Operational Cost ◽  
Electricity Pricing ◽  
Optimal Operating ◽  
The Cost ◽  
Operating Points

Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating an cost function, it is suitably analysed and then a dynamic penalty function to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for the real-time energy management.

scholarly journals Modified PSO Algorithm for Real-Time Energy Management in Grid-Connected Microgrids

2019 ◽  
Author(s):  
Md Alamgir Hossain ◽  
Hemanshu Roy Pota ◽  
Stefano Squartini ◽  
Ahmed Fathi Abdou
Keyword(s):  
Cost Function ◽  
Real Time ◽  
Energy Management ◽  
Storage System ◽  
Pso Algorithm ◽  
Operational Cost ◽  
Electricity Pricing ◽  
Optimal Operating ◽  
The Cost ◽  
Operating Points

Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating a cost function, it is suitably analysed and then a dynamic penalty function in order to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management.

Study on Power Delivering in Energy Management for Hybrid Electric Vehicle

2013 ◽  
Vol 676 ◽  
pp. 235-241
Author(s):  
Ping Sun ◽  
Xiu Min Yu ◽  
Wei Dong
Keyword(s):  
Cost Function ◽  
Real Time ◽  
Electric Vehicle ◽  
Energy Management ◽  
Minimization Problem ◽  
Hybrid Electric Vehicle ◽  
Driving Cycle ◽  
Global Minimization ◽  
Optimal Value ◽  
The Cost

The equivalent consumption minimization strategy (ECMS) is a method to reduce the global minimization problem to an instantaneous minimization problem to be solved at each instant. The adaptive ECMS is a development of ECMS in which the equivalence factors are not pre-coded, but rather calculated online. The equivalence factors, their optimal value, which minimizes the cost function while maintaining the vehicle substantially charge sustaining, depends on the specific driving cycle. The method proposed in this paper is one of the most important simplifications for actual real time implementation of A-ECMS and power delivering in energy management for HEV. The charging factor can be calculated if the discharging factor is calculated in the experiment for real time. And only a subset of (charging and discharging factors) generates a trend close to zero which indicates charge-sustainability.

scholarly journals Home Energy Management for Community Microgrids Using Optimal Power Sharing Algorithm

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1060
Author(s):  
Md Mamun Ur Rashid ◽  
Majed A. Alotaibi ◽  
Abdul Hasib Chowdhury ◽  
Muaz Rahman ◽  
Md. Shafiul Alam ◽  
...  
Keyword(s):  
Energy Storage ◽  
Real Time ◽  
Energy Management ◽  
Cost Minimization ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Power Sharing ◽  
Optimal Power ◽  
Time Operation ◽  
Home Energy Management

From a residential point of view, home energy management (HEM) is an essential requirement in order to diminish peak demand and utility tariffs. The integration of renewable energy sources (RESs) together with battery energy storage systems (BESSs) and central battery storage system (CBSS) may promote energy and cost minimization. However, proper home appliance scheduling along with energy storage options is essential to significantly decrease the energy consumption profile and overall expenditure in real-time operation. This paper proposes a cost-effective HEM scheme in the microgrid framework to promote curtailing of energy usage and relevant utility tariff considering both energy storage and renewable sources integration. Usually, the household appliances have different runtime preferences and duration of operation based on user demand. This work considers a simulator designed in the C++ platform to address the domestic customer’s HEM issue based on usages priorities. The positive aspects of merging RESs, BESSs, and CBSSs with the proposed optimal power sharing algorithm (OPSA) are evaluated by considering three distinct case scenarios. Comprehensive analysis of each scenario considering the real-time scheduling of home appliances is conducted to substantiate the efficacy of the outlined energy and cost mitigation schemes. The results obtained demonstrate the effectiveness of the proposed algorithm to enable energy and cost savings up to 37.5% and 45% in comparison to the prevailing methodology.

Operational Cost of Running Real-Time Mobile Cloud Applications

2015 ◽  
pp. 294-321 ◽  
Author(s):  
Ovunc Kocabas ◽  
Regina Gyampoh-Vidogah ◽  
Tolga Soyata
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Service Level ◽  
Cloud Services ◽  
Mobile Cloud ◽  
Cost Models ◽  
Operational Cost ◽  
Resource Requirements ◽  
The Cost ◽  
Cloud Applications

This chapter describes the concepts and cost models used for determining the cost of providing cloud services to mobile applications using different pricing models. Two recently implemented mobile-cloud applications are studied in terms of both the cost of providing such services by the cloud operator, and the cost of operating them by the cloud user. Computing resource requirements of both applications are identified and worksheets are presented to demonstrate how businesses can estimate the operational cost of implementing such real-time mobile cloud applications at a large scale, as well as how much cloud operators can profit from providing resources for these applications. In addition, the nature of available service level agreements (SLA) and the importance of quality of service (QoS) specifications within these SLAs are emphasized and explained for mobile cloud application deployment.

scholarly journals Real-Time Energy Management for a Small Scale PV-Battery Microgrid: Modeling, Design, and Experimental Verification

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2712 ◽  
Author(s):  
Mahmoud Elkazaz ◽  
Mark Sumner ◽  
David Thomas
Keyword(s):  
Energy Storage ◽  
Real Time ◽  
Energy Management ◽  
Storage System ◽  
Reference Value ◽  
Energy Storage System ◽  
Small Scale ◽  
Battery Energy Storage System ◽  
Battery Energy Storage ◽  
Battery Energy

A new energy management system (EMS) is presented for small scale microgrids (MGs). The proposed EMS focuses on minimizing the daily cost of the energy drawn by the MG from the main electrical grid and increasing the self-consumption of local renewable energy resources (RES). This is achieved by determining the appropriate reference value for the power drawn from the main grid and forcing the MG to accurately follow this value by controlling a battery energy storage system. A mixed integer linear programming algorithm determines this reference value considering a time-of-use tariff and short-term forecasting of generation and consumption. A real-time predictive controller is used to control the battery energy storage system to follow this reference value. The results obtained show the capability of the proposed EMS to lower the daily operating costs for the MG customers. Experimental studies on a laboratory-based MG have been implemented to demonstrate that the proposed EMS can be implemented in a realistic environment.

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