Optimal sizing of energy storage system in islanded microgrid using incremental cost approach

Hesaroor, Kashinath; Das, Debapriya

doi:10.1016/j.est.2019.100768

Cited by 27 publications

(14 citation statements)

References 51 publications

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“…[P i (t)] + P BESS (t) + P grid (t) + P w (t) + −P dump (t) = P D (t) ∀t ∈ T (13) where P D (t) is the power demand at instant of hour t and dump (t) is the dump load. Pdump(t) is chosen such that P dump (t) < P w (t) − P D (t).…”

Section: B System Constraints 1) Balance Constraintmentioning

confidence: 99%

“…In [12], a life cycle planning methodology of BESS in microgrids was presented and the optimal sizes of BESS were obtained under multi-scaled decision parameters to meet the demand growth. The authors in [13] find the optimal values of BESS for an islanded DC microgrid using an incremental cost approach while the authors in [16] used the convex optimization technique for minimization of the unit a unit commitment problem. However, the study in [13] evaluates the feasibility of installing a given battery unit to achieve the minimum running cost.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [13] find the optimal values of BESS for an islanded DC microgrid using an incremental cost approach while the authors in [16] used the convex optimization technique for minimization of the unit a unit commitment problem. However, the study in [13] evaluates the feasibility of installing a given battery unit to achieve the minimum running cost. In [14], the authors consider the efficiency of power supply probability and energy cost to size the BESS using the grasshopper optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Sizing of Battery Energy Storage for Grid-Connected and Isolated Wind-Penetrated Microgrid

2020

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Renewable energy (RE) sources, particularly wind and solar are gaining more popularity due to their inherent benefits, consequently, nations have set ambitious goals to enhance the penetration of RE into their energy-mix. However, the RE sources especially wind and photovoltaic sources are intermittent, uncertain, and unpredictable. Therefore, there is a need to optimize their usage when they are available. Moreover, energy storage system like battery energy storage has much potential to support the RE integration with the power grid. This study, therefore, investigates the sizes of battery energy storage required to support a grid-connected microgrid and a stand-alone microgrid for 12 months considering hourly wind power potential. In this study, we have considered three Scenarios of operations and have determined the BESS sizes and recommend the best based on the cost of operation. Scenarios 1 and 2 are grid-connected configuration while Scenario 3 is a standalone microgrid supported with diesel generators. In each Scenario, the optimization problem is formulated based on the optimal operation cost of the microgrids. The powers consumed from the main grid are reported in Scenarios 1 & 2 and the extra cost spent on the maintenance of diesel generators is reported in Scenario 3. The study evaluates and analyzes the operational environmental effects and costs between the three Scenarios. The formulated problems are solved using the nonlinear optimization method. Simulations results proved the effectiveness of the study. INDEX TERMS Energy capacity, energy storage system, renewable energy, power capacity, optimal sizes, wind power.

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Section: B System Constraints 1) Balance Constraintmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Sizing of Battery Energy Storage for Grid-Connected and Isolated Wind-Penetrated Microgrid

2020

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“… Energy storage (battery) contraints : The charging and discharging efficiency of battery, i.e.

η_{char}

and

η_{dischar}

decides the energy available for subsequent hour of operation based on its initial

E false(batt, h = 0 false)

and allowable state of charge (energy)

E_{batt, \min}

[50, 51] as given below:

E_{batt, s, h} = E_{batt, s, h - 1} + P_{batt, s, h} normalΔ T η_{char} | P_{batt, s, h} false(t false) \geq 0

E_{bat, s, h} = E_{batt, s, h - 1} + \frac{P_{batt, s, h} false(t false) . normalΔ T}{η_{dischar}} | P_{batt, s, h} false(t false) < 0

- P_{batt, R} \leq P_{batt, s, h} \leq P_{batt, R}

E_{batt, \min} \leq E false(batt, s, h false) \leq E_{batt, R}

E false(batt, h = 0 false) \leq E false(batt, h = T false)

Heat demand balance : The heat load at each scenario s at any hour h , must be supplied from either/both CCHP DGs and boiler ( B ), such that the heat power generated must be at least eq...…”

Section: Problem Formulationmentioning

confidence: 99%

Stochastic planning of islanded microgrids with uncertain multi‐energy demands and renewable generations

Jithendranath

Das

2020

IET Renewable Power Generation

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Islanded microgrids (IMGs) are embedded power networks with distributed energy resources (DERs) providing a reliable and flexible energy option for off‐grid customers. This work addresses the planning model of renewable‐based IMGs feeding multi‐energy demands considering investment and emission related objectives. The proposed solution is to determine the optimal mix and sizing of various energy sources in IMG, including renewables; for multiple energy demands. This study also presents a hybrid‐scenario and Monte Carlo approach to gauge the uncertainty involved in multi‐energy demands, i.e. electrical, heating, and cooling loads; together with correlation among wind and solar generations. The spatial interdependence among renewable generations is implemented using copula; that generates a synthetic set of stochastic correlated data. The combined load scenarios for multi‐energy demands and renewable samples are implemented with the proposed hybrid approach in the formulated stochastic planning model. In this work, the formulated problem is proposed to solve using meta‐heuristic multi‐objective ant lion optimiser algorithm, that is validated on the test system. The superiority of the proposed approach is highlighted in comparison with other multi‐objective optimisers. The multi‐energy dispatch between associated sources and loads were simulated to show how the obtained capacity can suffice the seasonal multi‐energy demands of a typical day considered.

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“…The analysis of HESS inclusion in the power systems has been extensively publicized [10][11], and mainly deals with sizing and controlling the ESS system. In general, the sizing problem is solved with optimization techniques to minimize the acquisition and operating costs and improve grid health indexes [12][13][14][15][16]. Each ESS alternative's time scale is treated with filters [17][18][19], Fourier series, Wavelets, and neural networks [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Hybrid Energy Storage Systems Using Wavelet and Stretched-Thread Methods

et al. 2020

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This paper presents a methodology to evaluate hybrid energy storage systems in hybrid energy systems. While Wavelet is used to decompose the net load in temporal segments, the stretchedthread method is used to evaluate the influence of energy storage, instead of conventional optimization techniques, conferring a visual approach. Proper selection of energy storage technologies for each time frame, as long as several sizing of different energy storage technologies is evaluated as well. The use of different methods and their application in a hybrid system are the main contributions of this piece.

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Optimal sizing of energy storage system in islanded microgrid using incremental cost approach

Cited by 27 publications

References 51 publications

Optimal Sizing of Battery Energy Storage for Grid-Connected and Isolated Wind-Penetrated Microgrid

Optimal Sizing of Battery Energy Storage for Grid-Connected and Isolated Wind-Penetrated Microgrid

Stochastic planning of islanded microgrids with uncertain multi‐energy demands and renewable generations

Evaluation of Hybrid Energy Storage Systems Using Wavelet and Stretched-Thread Methods

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