Energy and operation management of a microgrid using particle swarm optimization

Radosavljević, Jordan; Jevtić, Miroljub; Klimenta, Dardan

doi:10.1080/0305215x.2015.1057135

Cited by 102 publications

(37 citation statements)

References 29 publications

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“…In Reference , a novel opposition‐based tuned‐chaotic differential evolution technique is proposed for optimal energy management of MGs to reduce the active power losses, improve the voltage profile, and thereby maximize the techno‐economic benefits. In Reference , an efficient algorithm is presented based on PSO algorithm for energy and operation management of an MG, including different DG units and energy storage systems (ESSs). The proposed approach minimizes the total energy and operating cost of MGs.…”

Section: Introductionmentioning

confidence: 99%

A new optimal operation framework for balanced microgrids considering reconfiguration and generation scheduling simultaneously

Sedighizadeh

Shaghaghi‐shahr

Aghamohammadi

et al. 2020

Int Trans Electr Energ Syst

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Summary In this article, the optimal distribution feeder reconfiguration (DFR) and optimal generation scheduling (OGS) are simultaneously taken into account to enhance operational flexibility and efficiency in microgrids (MGs). The proposed model considers different features of MGs, including distributed generation (DG) units, plug in hybrid electric vehicles (PHEVs), and demand response (DR) programs. Three DR programs are modeled including, load shifting, load curtailing, and time of use (TOU) programs. Uncertainties of renewable generations and loads are modeled using a computationally competitive approach. Both controlled and uncontrolled charging/discharging patterns of PHEVs are investigated. To enhance tractability, the proposed model is formulated as a mixed‐integer second‐order cone programing (MISOCP) problem to minimize operational cost subject to technical and financial constraints. To evaluate the performance of the proposed model, it is examined on the balanced 33‐bus test systems in different case studies of DFR and OGS. The optimization problem is solved using the GAMS software package. It is found that PHEVs and DR not only enhances MG operation flexibility, but they also reduce the total operation cost and smooth load profile.

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Section: Introductionmentioning

confidence: 99%

A new optimal operation framework for balanced microgrids considering reconfiguration and generation scheduling simultaneously

Sedighizadeh

Shaghaghi‐shahr

Aghamohammadi

et al. 2020

Int Trans Electr Energ Syst

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“…Considering the features of the MOOD problem, many intelligent methods are used to handle the optimization dispatch of a microgrid. For instance, the genetic algorithm (GA) [22,23], particle swarm optimization (PSO) [24,25], the strength Pareto evolutionary algorithm (SPEA) [26] and so on have been increasingly proposed for solving the optimization dispatch problem because of their non-linear mapping, simplicity and powerful search capability. However, the above intelligent methods present the following shortcomings: many parameters are required to set before the optimization and most test cases are parameters sensitive, and a single intelligent optimization method is usually easy to fall into local optimum.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Optimization Dispatch Method for Microgrid Energy Management Considering the Power Loss of Converters

Cao

Wang

et al. 2019

Energies

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With the spreading and applying of microgrids, the economic and environment friendly microgrid operations are required eagerly. For the dispatch of practical microgrids, power loss from energy conversion devices should be considered to improve the efficiency. This paper presents a two-stage dispatch (TSD) model based on the day-ahead scheduling and the real-time scheduling to optimize dispatch of microgrids. The power loss cost of conversion devices is considered as one of the optimization objectives in order to reduce the total cost of microgrid operations and improve the utility efficiency of renewable energy. A hybrid particle swarm optimization and opposition-based learning gravitational search algorithm (PSO-OGSA) is proposed to solve the optimization problem considering various constraints. Some improvements of PSO-OGSA, such as the distribution optimization of initial populations, the improved inertial mass update rule, and the acceleration mechanism combining the memory and community of PSO, have been integrated into the proposed approach to obtain the best solution for the optimization dispatch problem. The simulation results for several benchmark test functions and an actual test microgrid are employed to show the effectiveness and validity of the proposed model and algorithm.

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“…Likewise, there are always some errors in the forecasted values of market prices and load demand. Probabilistic optimal operation management of MG to minimize total operating cost is presented in previous studies . The uncertainties are handled using 2 m point estimate method (PEM), 2 m + 1 PEM, and scenario‐based stochastic programming, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Probabilistic optimal operation management of MG to minimize total operating cost is presented in previous studies . The uncertainties are handled using 2 m point estimate method (PEM), 2 m + 1 PEM, and scenario‐based stochastic programming, respectively. Aforementioned work from the literature has considered only grid‐connected MG operation.…”

Section: Introductionmentioning

confidence: 99%

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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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Energy and operation management of a microgrid using particle swarm optimization

Cited by 102 publications

References 29 publications

A new optimal operation framework for balanced microgrids considering reconfiguration and generation scheduling simultaneously

A new optimal operation framework for balanced microgrids considering reconfiguration and generation scheduling simultaneously

A Multi-Objective Optimization Dispatch Method for Microgrid Energy Management Considering the Power Loss of Converters

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

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