Optimization and energy management of a standalone hybrid microgrid in the presence of battery storage system

Moradi, Hadis; Esfahanian, Mahdi; Abtahi, Amir-Reza; Zilouchian, Ali

doi:10.1016/j.energy.2018.01.016

Cited by 132 publications

(55 citation statements)

References 33 publications

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“…However, in this work, all customers are considered the same type and also the need for local storage units like ESS has been neglected. In [17], optimal energy management of an autonomous microgrid has been accomplished to increase network stability during a power outage. In this study, the role of ESSs in improving network performance and reducing costs and pollutants has been investigated.…”

Section: Introductionmentioning

confidence: 99%

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

et al. 2020

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The energy management system is executed in microgrids for optimal integration of distributed energy resources (DERs) into the power distribution grids. To this end, various strategies have been more focused on cost reduction, whereas effectively both economic and technical indices/factors have to be considered simultaneously. Therefore, in this paper, a two-layer optimization model is proposed to minimize the operation costs, voltage fluctuations, and power losses of smart microgrids. In the outer-layer, the size and capacity of DERs including renewable energy sources (RES), electric vehicles (EV) charging stations and energy storage systems (ESS), are obtained simultaneously. The inner-layer corresponds to the scheduled operation of EVs and ESSs using an integrated coordination model (ICM). The ICM is a fuzzy interface that has been adopted to address the multi-objectivity of the cost function developed based on hourly demand response, state of charges of EVs and ESS, and electricity price. Demand response is implemented in the ICM to investigate the effect of time-of-use electricity prices on optimal energy management. To solve the optimization problem and load-flow equations, hybrid genetic algorithm (GA)-particle swarm optimization (PSO) and backward-forward sweep algorithms are deployed, respectively. One-day simulation results confirm that the proposed model can reduce the power loss, voltage fluctuations and electricity supply cost by 51%, 40.77%, and 55.21%, respectively, which can considerably improve power system stability and energy efficiency.Energies 2020, 13, 1706 2 of 25 microgrids is evolving quickly [1]. The advanced structure of the microgrids allows the distribution network operators (DSOs) to consider the EV battery, either as a load or distributed generator [2]. However, deployment of RESs and EVs creates some challenges, e.g., poor reliability and power quality problems. One of the most effective ways to mitigate these challenges is the deployment of energy storage systems (ESSs) in distribution grids. ESSs can alleviate the negative effects of uncertainties in RESs production [3]. On the other hand, despite the remarkable advantages of these units, there are some challenges in terms of adequate DERs capacity requirement, system configuration, and energy management and control. One of the most challenging issues is the optimal coordination of both supply and demand sides in microgrids with the main grid, while satisfying system constraints.Currently, extensive studies have been done on the impacts of the RESs, EVs, and ESSs on the network operation . In [4], a smart home energy management strategy is proposed to realize cost-effective energy systems for customers and provide reactive power compensation for home appliances using EV and ESS. In this paper, network-level studies are limited to investigating the power factor indicator, while the potential role of EVs and ESSs in distribution networks has not been considered. Researchers in [5] presented the centralized control strategy to manag...

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Section: Introductionmentioning

confidence: 99%

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

et al. 2020

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“…Simultaneous optimization of operating cost and emission based on a multiobjective framework is considered in Rezvani et al To optimize power and emission cost, a constrained single objective for a standalone MG is presented in Moradi et al A mixed integer quadratic programming is adopted in Kumtepeli and Volkan to attain effective utilization of resources in an islanded MG. Performance of a standalone MG under various operational strategies while considering uncertainties of RES and load demand is modeled in Moradi et al Accessibility of MG to main grid is not discussed in previous studies …”

Section: Introductionmentioning

confidence: 99%

Impact assessment of electric vehicle charging/discharging strategies on the operation management of grid accessible and remote microgrids

et al. 2019

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This article deals with impact analysis of different electric vehicle (EV) charging/discharging strategies (CDS) on the operation and pollutant treatment cost of both grid accessible and remote microgrid (MG) modes. In this regard, EV demand is developed under four different scenarios, namely, uncoordinated charging model (UCM), load leveling model (LLM), maximum renewable model (MRM), and charging discharging model (CDM). A comprehensive study is performed to see the effect of these different EV charging/discharging behaviors in optimizing MG's operation. A 2m scheme of Hong's point estimate method (PEM) is applied to examine the effect of uncertainties linked with the forecasted errors in load demand, solar energy, wind energy, and grid price respectively on MG operation problem. Finally, a sensitivity analysis is performed to investigate the effect of variations in battery parameters on economics of remote MG. The study results indicate that controlled charging of EVs can substantially improve operation of MG. KEYWORDS distributed generators, electric vehicle, microgrid, point estimate methodNomenclature: L avg , average load; L base , base load of MG (without EV); BC b , battery capacity; BC EV,n , battery capacity of EV n; η bc , battery charging efficiency; P bc , battery charging power; η bd , battery discharging efficiency; P bd , battery discharging power; SOC b , battery SOC; v, binary variable, 1 when battery is charging; w, binary variable, 1 when battery is discharging; cf, capacity factor; t cd,n , charging duration of EV n; η EVc,n , charging efficiency of EV n; P EVc,n , charging power of EV n; C grid , cost of power integration between main grid and MG; C DC,i , depreciation cost of DG i; ADCC i , depreciation cost per kwh of DG i; T EVde,n , discharging end time of EV n; T EVds,n , discharging start time of EV n; d n , distance travelled by an EV n; EP GRID , electricity price of grid; e 100,n , energy consumption per 100 km of EV n; k fc,i , fuel consumption coefficient of DG i; C fuel,i , fuel cost of DG i; Incost i , installation cost per capacity of DG i; d, interest rate; l i , lifetime of DG i; P LC , load curtailed; C LC , load curtailment cost; μ LC , load curtailment cost; r down,i , lower ramp rate limit of DG i; SOC b,max , maximum battery SOC; P bc,max , maximum charging limit of battery; P EVcmax,n , maximum charging rate of EV n; P bd,max , maximum discharging limit of battery; P EVdmax,n , maximum discharging power of EV n; T EVddmax,n , maximum duration for which EV n can discharge; P grid,max , maximum power MG can take from main grid under contractual scheme; P i,max , maximum power of DG i; SOC EVmax,n , maximum SOC of battery of EV n; SOC b,min , minimum battery SOC; P i,min , minimum power of DG i; SOC EVmin,n , minimum SOC of battery of EV n; N DG , number of DGs in MG; k OM,i , operation and maintenance coefficient of DG i; C OM,i , operation and maintenance cost of DG i; P PV , output power from PV; P WT , output power from WT; P i , output power of DG i; P grid , output ...

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“…In this way, the energy community works for the benefit of the whole grid and not just the small scale MG [12]. To achieve this, a real-time controller is required that allows the small-scale MG to accurately follow a reference value for the power drawn from the main electric grid, where this reference is created by a higher level controller which considers both local and system wide factors.Alternatively, large scale energy storage systems (ESS) (>1 MWh) will play a key role in solving problems such as intermittency of supply and loss of inertia which are challenging electricity grid operation [13], and many grid operators are encouraging the use of ESS to address, for example, increasing demand peaks and network congestion [14].Much of the existing research focusing on microgrid energy management (MGEM) is oriented towards determining the best operating scenario for the MG [15][16][17]. In [18], Carlos et al introduce a new iterative algorithm that manages energy flows to obtain the minimum energy cost for the microgrid based on the availability of resources, prices, and the expected demand.…”

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confidence: 99%

“…Alternatively, large scale energy storage systems (ESS) (>1 MWh) will play a key role in solving problems such as intermittency of supply and loss of inertia which are challenging electricity grid operation [13], and many grid operators are encouraging the use of ESS to address, for example, increasing demand peaks and network congestion [14].…”

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confidence: 99%

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

2019

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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.Energies 2019, 12, 2712 2 of 26 these ESS into small-scale MG architectures. The electrical load profiles of small scale MGs, particularly residential communities, can vary considerably with time: periods of house inoccupancy (e.g., during vacation) and the addition of new equipment (e.g., electric vehicle charge) or even new houses can have a strong influence on the loading profile [9]. Additionally, these small-scale systems see sharp changes in load over a short period of time as single loads (shower, cooker) can be significant considering the size of the MG. For these reasons, EMS used for small scale MGs must have a short control sample time to observe and respond to fast changes in the load and generation throughout the day [9,10]. Also, energy forecasting techniques must be adaptive and also have a short sample time if they are to help the EMS achieve good results for this type of grid.Small-scale MGs should preferably operate as a single controllable unit that imports/exports power from/to the main grid following a predictable shape [11]. In this way, the energy community works for the benefit of the whole grid and not just the small scale MG [12]. To achieve this, a real-time controller is required that allows the small-scale MG to accurately follow a reference value for the power drawn from the main electric grid, where this reference is created by a higher level controller which considers both local and system wide factors.Alternatively, large scale energy storage systems (ESS) (>1 MWh) will play a key role in solving problems such as intermittency of supply and loss of inertia which are challenging electricity grid operation [13], and many grid operators are encouraging the use of ESS to address, for example, increasing demand peaks and network congestion [14].Much of the existing research focusing on microgrid energy management (MGEM) is oriented towards determining the best operating scenario for the MG [15][16][17]. In [18], Carlos et al. introduce a new iterative algorithm that mana...

show abstract

Optimization and energy management of a standalone hybrid microgrid in the presence of battery storage system

Cited by 132 publications

References 33 publications

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

Impact assessment of electric vehicle charging/discharging strategies on the operation management of grid accessible and remote microgrids

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

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