scholarly journals An Economical Energy Management Strategy for Viable Microgrid Modes

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1442 ◽  
Author(s):  
Samia Abid ◽  
Turki Ali Alghamdi ◽  
Abdul Haseeb ◽  
Zahid Wadud ◽  
Abrar Ahmed ◽  
...  
Keyword(s):  
Energy Management ◽  
Greenhouse Gas Emission ◽  
Management Strategies ◽  
Operating Cost ◽  
Substantial Reduction ◽  
Flower Pollination Algorithm ◽  
Energy Management Strategy ◽  
Distributed Generator ◽  
Management Algorithm ◽  
Load Demand

In the last couple of decades, numerous energy management strategies have been devised to mitigate the effects of greenhouse gas emission, hence introducing the concept of microgrids. In a microgrid, distributed energy generators are used. Microgrid enables a point which ameliorates in exchanging power with the main grid during different times of day. Based on the system constraints, in this work, we aim to efficiently minimize the operating cost of the microgrid and shave the power consumption peaks. For this purpose, we introduce an improved binary bat (iBBat) algorithm which helps to schedule the load demand of smart homes and energy generation from distributed generator of microgrid to the load demand and supply. The proposed energy management algorithm is applied to both grid-connected and islanded modes of the microgrid. The constraints imposed on the algorithm ensure that the load of electricity consumer does not escalate during peak hours. The simulation results are compared with BBat and binary flower pollination algorithm, which validate that the iBBat reflects substantial reduction in operating cost of microgrid. Moreover, results also show a phenomenal reduction in the peak-to-average ratio of load demand from main the main grid.

scholarly journals Multicriteria Decision-Making to Determine the Optimal Energy Management Strategy of Hybrid PV–Diesel Battery-Based Desalination System

2021 ◽  
Vol 13 (8) ◽  
pp. 4202
Author(s):  
Hegazy Rezk ◽  
Basem Alamri ◽  
Mokhtar Aly ◽  
Ahmed Fathy ◽  
Abdul G. Olabi ◽  
...  
Keyword(s):  
Reverse Osmosis ◽  
Energy Management ◽  
Management Strategy ◽  
Management Strategies ◽  
Operating Cost ◽  
Water Desalination ◽  
Evaluation Procedure ◽  
Optimal Size ◽  
Energy Management Strategy ◽  
Simulation And Optimization

This paper identifies the best energy management strategy of hybrid photovoltaic–diesel battery-based water desalination systems in isolated regions using technical, economic and techno–economic criteria. The employed procedures include Criteria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as tools for the solution. Twelve alternatives, containing three–four energy management strategies; four energy management strategies, load following (LF), cycle charging (CC), combined LF–CC, and predictive strategy; and three different sizes of brackish water reverse osmosis (BWRO) water desalination units, BWRO-150, BWRO-250, and BWRO-500, are investigated with capacity of 150, 250, and 500 m3/day, respectively. Eight attributes comprising different technical and economic metrics are considered during the evaluation procedure. HOMER Pro® software is utilized to perform the simulation and optimization. The main findings confirmed that the best energy management strategies are predictive strategies and the reverse osmosis (RO) unit’s optimal size is RO-250. For such an option, the annual operating cost and initial costs are $4590 and $78,435, respectively, whereas the cost of energy is $0.156/kWh. The excess energy and unmet loads are 27,532 kWh and 20.3 kWh, respectively. The breakeven grid extension distance and the amount of CO2 are 6.02 km and 14,289 kg per year, respectively. Compared with CC–RO-150, the amount of CO2 has been sharply decreased by 61.2%.

scholarly journals Optimum Energy Flow Management of a Grid-Tied Photovoltaic-Wind-Battery System considering Cost, Reliability, and CO2 Emission

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Meryeme Azaroual ◽  
Mohammed Ouassaid ◽  
Mohamed Maaroufi
Keyword(s):  
Greenhouse Gas Emission ◽  
Storage System ◽  
Operating Cost ◽  
Cost Savings ◽  
Substantial Effect ◽  
Energy Management Strategy ◽  
Optimum Energy ◽  
Ghg Reduction ◽  
Reliable Model ◽  
Cover Ratio

The main goal of this paper is to explore the performance of a residential grid-tied hybrid (GTH) system which relies on economic and environmental aspects. A photovoltaic- (PV-) wind turbine- (WT-) battery storage system with maximizing self-consumption and time-of-use (ToU) pricing is conducted to examine the system efficiency. In so doing, technical optimization criteria with taking into consideration renewable energy benefits including feed-in-tariff (FIT) and greenhouse gas emission (GHG) reduction are analyzed. As the battery has a substantial effect on the operational cost of the system, the energy management strategy (EMS) will incorporate the daily operating cost of the battery and the effect of the degradation. The model can give the opportunity to the network to sell or purchase energy from the system. The simulation results demonstrate the effectiveness of the proposed approach in which the new objective function achieves the maximum cost-saving (99.81%) and income (5.16 $/day) compared to other existing strategies as well as the lowest GHG emission. Furthermore, the battery enhances the best daily self-consumption and load cover ratio. Then, as the model is nonlinear, a comparison with other existing algorithms is performed to select the feasible, robust, and reliable model for the residential application. A hybrid algorithm (HGAFMINCON) is developed to demonstrate the superiority of the algorithm over FMINCON and GA shown in terms of cost savings and income.

Optimal energy management of a fuel cell-battery-supercapacitor-powered hybrid tramway using a multi-objective approach

2019 ◽  
Vol 234 (5) ◽  
pp. 511-523 ◽  
Author(s):  
Han Zhang ◽  
Jibin Yang ◽  
Jiye Zhang ◽  
Pengyun Song ◽  
Ming Li
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Management Strategy ◽  
Operating Cost ◽  
Cell System ◽  
Energy Management Strategy ◽  
Multi Objective ◽  
Optimal Energy ◽  
Optimal Energy Management ◽  
Fuel Costs

Achieving an optimal operating cost is a challenge for the development of hybrid tramways. In the past few years, in addition to fuel costs, the lifespan of the power source is being increasingly considered as an important factor that influences the operating cost of a tramway. In this work, an optimal energy management strategy based on a multi-mode strategy and optimisation algorithm is described for a high-power fuel cell hybrid tramway. The objective of optimisation is to decrease the operating costs under the conditions of guaranteeing tramway performance. Besides the fuel costs, the replacement cost and initial investment of all power units are also considered in the cost model, which is expressed in economic terms. Using two optimisation algorithms, a multi-population genetic algorithm and an artificial fish swarm algorithm, the hybrid system's power targets for the energy management strategy were acquired using the multi-objective optimisation. The selected case study includes a low-floor light rail vehicle, and experimental validations were performed using a hardware-in-the-loop workbench. The results testify that an optimised energy management strategy can fulfil the operational requirements, reduce the daily operation costs and improve the efficiency of the fuel cell system for a hybrid tramway.

scholarly journals Development of Global Optimization Algorithm for Series-Parallel PHEV Energy Management Strategy Based on Radau Pseudospectral Knotting Method

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3268
Author(s):  
Kegang Zhao ◽  
Jinghao Bei ◽  
Yanwei Liu ◽  
Zhihao Liang
Keyword(s):  
Global Optimization ◽  
Energy Management ◽  
Computational Efficiency ◽  
Management Strategy ◽  
Hybrid Electric Vehicles ◽  
Minimum Principle ◽  
Management Strategies ◽  
Energy Management Strategy ◽  
Pontryagin Minimum Principle ◽  
Component Sizing

The powertrain model of the series-parallel plug-in hybrid electric vehicles (PHEVs) is more complicated, compared with series PHEVs and parallel PHEVs. Using the traditional dynamic programming (DP) algorithm or Pontryagin minimum principle (PMP) algorithm to solve the global-optimization-based energy management strategies of the series-parallel PHEVs is not ideal, as the solution time is too long or even impossible to solve. Chief engineers of hybrid system urgently require a handy tool to quickly solve global-optimization-based energy management strategies. Therefore, this paper proposed to use the Radau pseudospectral knotting method (RPKM) to solve the global-optimization-based energy management strategy of the series-parallel PHEVs to improve computational efficiency. Simulation results showed that compared with the DP algorithm, the global-optimization-based energy management strategy based on the RPKM improves the computational efficiency by 1806 times with a relative error of only 0.12%. On this basis, a bi-level nested component-sizing method combining the genetic algorithm and RPKM was developed. By applying the global-optimization-based energy management strategy based on RPKM to the actual development, the feasibility and superiority of RPKM applied to the global-optimization-based energy management strategy of the series-parallel PHEVs were further verified.

scholarly journals Research on Energy Management Strategies of Extended-Range Electric Vehicles Based on Driving Characteristics

2020 ◽  
Vol 11 (3) ◽  
pp. 54 ◽  
Author(s):  
Yuanbin Yu ◽  
Junyu Jiang ◽  
Zhaoxiang Min ◽  
Pengyu Wang ◽  
Wangsheng Shen
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Management Strategy ◽  
Control Strategies ◽  
Management Strategies ◽  
Principal Component ◽  
Driving Cycle ◽  
Control Parameters ◽  
Energy Management Strategy ◽  
Extended Range

The extended-range electric vehicle (E-REV) can solve the problems of short driving range and long charging time of pure electric vehicles, but it is necessary to control the engine working points and allocate the power of the energy sources reasonably. In order to improve the fuel economy of the vehicle, an energy management strategy (EMS) that can adapt to the daily driving characteristics of the driver and adjust the control parameters online is proposed in this paper. Firstly, through principal component analysis (PCA) and iterative self-organizing data analysis techniques algorithm (ISODATA) of historical driving data, a typical driving cycle which can describe driving characteristics of the driver is constructed. Then offline optimization of control parameters by adaptive simulated annealing under each typical driving cycle and online recognition of driving cycles by extreme learning machine (ELM) are applied to the adaptive multi-workpoints energy management strategy (A-MEMS) of E-REV. In the end, compared with traditional rule-based control strategies, A-MEMS achieves good fuel-saving and emission-reduction result by simulation verification, and it explores a new and feasible solution for the continuous upgrade of the EMS.

scholarly journals Two-Stage Energy Management for Energy Storage System by Using Stochastic Model Predictive Control Approach

2021 ◽  
Vol 9 ◽  
Author(s):  
Huimin Zhuang ◽  
Zao Tang ◽  
Jianglin Zhang
Keyword(s):  
Energy Storage ◽  
Stochastic Model ◽  
Energy Management ◽  
Predictive Control ◽  
Management Strategy ◽  
Operation Strategy ◽  
Energy Management Strategy ◽  
Load Demand ◽  
Lower Stage ◽  
Stochastic Model Predictive Control

There is a growing tendency for industrial consumers to invest in both photovoltaic (PV) and energy storage systems (ESSs) to meet their electricity requirements. However, the uncertainty of load demand and PV output brings great challenges for ESS operation. In this paper, a stochastic model predictive control (MPC) approach-based energy management strategy for ESSs is proposed. A non-parametric probabilistic prediction method embedded in time series correlation is adopted to describe the uncertainty of load demand and PV output. Then, a two-stage energy management model is proposed aiming at minimizing the total operation cost. The upper stage can generate an hourly operation strategy for ESSs, while the lower stage focuses on a more detailed minute-level operation strategy. The hourly operation strategy is also used as a basis to guide the ESS operation in the lower stage. Besides, a chance constraint was introduced to achieve a win–win solution between PV power consumption and electricity tariff, while the terminal value constraint of the capacity of ESSs to better cope with the uncertainty beyond the prediction time window. Finally, the numerical results showed that the proposed method can achieve an effective ESS energy management strategy.

scholarly journals Examination on Classification of EVs and Energy Management Strategies of HEV

2019 ◽  
Vol 9 (1S) ◽  
pp. 261-266
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Management Strategies ◽  
Transport Sector ◽  
Energy Management Strategy ◽  
Power Sources ◽  
Power Train ◽  
Range Anxiety

In recent days, the demand for petroleum and emission of pollutant gases continuously increase. This necessitates the electrification power train which replaces Internal Combustion Engine (ICE). Despite pure electric vehicles or Battery Electric Vehicle (EV) reduce the greenhouse gas emissions, there are some major hurdles for EVs to overcome before they totally relieve ICE vehicles form transport sector such as range anxiety, battery storage, economic fall down due to automobile industries, etc. This necessitates Hybrid Electric vehicle (HEV) which combines two different power sources to propel the vehicle. One of the challenges in HEV is how to control the power coming from the two different sources such as battery and ICE. The prime goal of an Energy Management Strategy (EMS) is to manage energy flow such that fuel consumption and emissions are minimized without affecting the vehicle’s performance. In this paper, the different structures of power train and energy management strategies are analysed.

Battery Management Based on Predictive Control and Demand-Side Management

2016 ◽  
pp. 149-180
Author(s):  
Deepranjan Dongol ◽  
Elmar Bollin ◽  
Thomas Feldmann
Keyword(s):  
Energy Management ◽  
Predictive Control ◽  
Management Strategy ◽  
Demand Side Management ◽  
Battery Management ◽  
Demand Side ◽  
Energy Management Strategy ◽  
Battery System ◽  
Surplus Energy ◽  
Load Demand

The chapter is intended to introduce the predictive control based energy management strategy for the grid connected renewable systems in order to achieve an effective demand side management strategy. Grid connected Photovoltaic battery system as being popular and extensively used has been discussed in this chapter .Conventionally, battery storage has been used to store surplus energy produced and meet the load demand with this stored energy. However, such systems do not respond to the grid conditions and violate grid constraints of permissible grid voltage and frequency limits. The operation of the battery depends on the forecast of photovoltaic output and the load demand and as such a predictive control based energy management strategy is needed. A simple optimization problem for such scenarios has also been formulated in great detail to provide readers with an idea for solving such problems. The results of simulations are also discussed.

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