Optimal sizing of domestic grid-connected microgrid maximizing self consumption and battery lifespan⋆

Ouramdane, Oussama; Elbouchikhi, Elhoussin; Gooya, Ehsan Sedgh

doi:10.1016/j.ifacol.2022.07.391

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…The role of the hybrid ESS was to compensate for the difference between the generated and the demanded powers at any instant according to the frequency. In [6], the optimal sizing of the MG resources including RESs/BSUs was obtained. The objectives of this study were to minimize the bill of energy consumption via the use of the RESs and maximize the lifetime of the BSUs.…”

Section: B Related Literature Surveymentioning

confidence: 99%

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Sizing of Microgrid System Including Multi-Functional Battery Storage and Considering Uncertainties

et al. 2023

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Battery storage units (BSUs) are usually used to perform a single function in most planning studies related to microgrids (MGs). This paper presents an effective methodology to use the BSUs to perform multi-function including supply/demand matching and energy arbitrage. This is done according to a system policy containing all possible scenarios to fully utilize the BSUs to maximize the benefit. In the proposed work, the optimal sizing of the MG system under study containing wind turbines (WTs), photovoltaic system (PV), BSUs, and diesel units (DUs) is obtained. The main objectives of the proposed methodology are; 1) minimizing the total costs of the MG, 2) minimizing the harmful gas emissions, and 3) minimizing the accumulated power difference between the generation from renewable energy systems (RESs) and the demand. Due to the stochastic behavior of the output from the RESs, the uncertainties of wind speed, solar irradiance, and temperature are considered in the study. Two modes of operation of the MG (grid-connected and islanded) and the demand side management (DSM) are also considered. The problem is formulated as a constrained nonlinear optimization problem and is solved using two metaheuristic optimization algorithms, Moth-Flame Optimization (MFO) and Hybrid Firefly and Particle Swarm Optimization (HFPSO). Moreover, the uncertainties in the different parameters are considered by using the Latin Hypercube Sampling (LHS) method to generate samples of wind speed, solar irradiance, and temperature. To examine the proposed methodology, different case studies are presented and discussed. Moreover, the results of the used two algorithms, MFO and HFPSO, are compared to show their effectiveness in solving the proposed problem and assure the optimal solution. The optimization problem is implemented and solved using MATLAB software.

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Section: B Related Literature Surveymentioning

confidence: 99%

“…The objective function (6) includes two terms. The first term represents the total costs of the MG which are calculated by (7).…”

Section: • Third Objectivementioning

confidence: 99%

Sizing of Microgrid System Including Multi-Functional Battery Storage and Considering Uncertainties

et al. 2023

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“…The horizon of the EMPC is 48 hours ahead to consider the daily PV power fluctuations and avoid depleting the static Liion battery. Since the Li-ion batteries were sized to be completed charged and discharged during a day to avoid wasting energy due to self-discharging [21], the daily optimization of (1) will prevent the BMG from ending the day with static batteries completely discharged.…”

Section: Hierarchical Model Predictive Controllermentioning

confidence: 99%

Hierarchical Model Predictive Control to Coordinate a Vehicle-to-Grid System Coupled to Building Microgrids

Yamashita

Vechiu

Gaubert

et al. 2023

IEEE Trans. on Ind. Applicat.

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Aiming to take full advantage of Electric Vehicles' (EVs) batteries, this paper proposes a two-level hierarchical model predictive controller coupled with an innovative charging-discharging scheduler for EVs in Building Microgrids (BMGs). This paper provides a complete framework for the design of this control structure and analyses its performance regarding the state of charge of the EVs at departure time, the self-consumption rate, and the coverage rate, considering a residential BMG equipped with photovoltaic panels and static Li-ion batteries. The results and performance of the proposed control architecture are compared to two other solutions: a hierarchical predictive controller with no scheduler and a rule-based algorithm. A technological and economical study is also performed considering variables such as the dimension of the EV's park, the price of energy, the cost of maintenance, the possibility to discharge or not into the grid, and the execution time of the control architecture. The simulation results conducted in MATLAB Simulink demonstrated that the proposed control structure ensures the full charging of all vehicles at departure time while also improving the self-consumption rate of the BMG with a relatively low stress on the needed computation capacities, even when considering a large fleet of vehicles.

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Optimal sizing of domestic grid-connected microgrid maximizing self consumption and battery lifespan⋆

Cited by 2 publications

References 10 publications

Sizing of Microgrid System Including Multi-Functional Battery Storage and Considering Uncertainties

Sizing of Microgrid System Including Multi-Functional Battery Storage and Considering Uncertainties

Hierarchical Model Predictive Control to Coordinate a Vehicle-to-Grid System Coupled to Building Microgrids

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