Peak shaving algorithm with dynamic minimum voltage tracking for battery storage systems in microgrid applications

Garcia-Plaza, M.; Carrasco, J. Eloy-Garcia; Alonso-Martínez, Jaime; Asensio, Andrés Peña

doi:10.1016/j.est.2018.08.021

Cited by 26 publications

(19 citation statements)

References 21 publications

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“…An interesting approach to bypass these challenges is introduced in [20], were the threshold is defined as 2kW and kept constant, but the charging strategy is based on the State Of Charge (SOC) value. More specifically, the battery is charged or discharged proportionally to the difference between a reference "target" SOC value (set to 50%) and the actual SOC of the battery.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the benefits of decentralised residential batteries for load peak shaving

Jankowiak

Zacharopoulos

Brandoni

et al. 2020

Journal of Energy Storage

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Section: Literature Reviewmentioning

confidence: 99%

Assessing the benefits of decentralised residential batteries for load peak shaving

Jankowiak

Zacharopoulos

Brandoni

et al. 2020

Journal of Energy Storage

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Section: On/off Peak Hours Membership Functionmentioning

confidence: 99%

“…Integrating ESSs into the grid is considered to be the best strategy for peak shaving, which consists in providing or absorbing the power surplus, thereby shaving the total exchanged power [43]. In this way, the coordination of charging and discharging of batteries offers the opportunity to participate in Demand Side Management (DSM) to reduce the peak load demand in the grid and avoid the surplus of PV generation injection into the grid [44].…”

Section: On/off Peak Hours Membership Functionmentioning

confidence: 99%

Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

et al. 2019

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This paper proposes an optimal design for hybrid grid-connected Photovoltaic (PV) Battery Energy Storage Systems (BESSs). A smart grid consisting of PV generation units, stationary Energy Storage Systems (ESSs), and domestic loads develops a multi-objective optimization algorithm. The optimization aims at minimizing the Total Cost of Ownership (TCO) and the Voltage Deviation (VD) while considering the direct and indirect costs for the prosumer, and the system stability with regard to intermittent PV generation. The optimal solution for the optimization of the PV-battery system sizing with regard to economic viability and the stability of operation is found while using the Genetic Algorithm (GA) with the Pareto front. In addition, a fuzzy logic-based controller is developed to schedule the charging and discharging of batteries while considering the technical and economic aspects, such as battery State of Charge (SoC), voltage profile, and on/off-peak times to shave the consumption peaks. Thus, a hybrid approach that combines a Fuzzy Logic Controller (FLC) and the GA is developed for the optimal sizing of the combined Renewable Energy Sources (RESs) and ESSs, resulting in reductions of approximately 4% and 17% for the TCO and the VD, respectively. Furthermore, a sensitivity cost-effectiveness analysis of the complete system is conducted to highlight and assess the profitability and the high dependency of the optimal system configuration on battery prices.

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“…The cost‐benefit analysis showed that the overall revenue from the proposed system is 1.84 times of the capital investment. Garcia‐Plaza et al 29 proposed a new algorithm for peak shaving by categorizing the states of a microgrid power balance and implemented the appropriate response of the BESS. As main drawbacks of the aforementioned approaches, can be considered the high levels of complexity in their implementation, and the heavy computational burdens that are often required.…”

Section: Introductionmentioning

confidence: 99%

Energy management and techno‐economic assessment of a predictive battery storage system applying a load levelling operational strategy in island systems

et al. 2020

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In the present study, a predictive battery energy storage system (BESS) for application in geographical non-interconnected islands with high renewable energy penetration is proposed, capable of performing load levelling. The system under consideration is composed of diesel and heavy oil generators, a photovoltaic farm and a small wind turbine. The proposed solution integrates machine learning (ML) methods for the forecasting of load and intermittent solar and wind power productions, alongside a custom scheduling algorithm, which calculates the necessary BESS setpoints that accomplish the desired levelling effect. An important feature of the scheduling algorithm is that the charge and discharge energy amounts of each day are by design equal and independent of the forecasts' accuracy. This aspect enables economic investigations to identify the appropriate BESS capacity for the particular system, taking also into account the battery's capacity degradation. The overall system is modelled and simulated utilizing the open-source languages Python and Modelica. Simulations presented a 9.8% peak-to-mean ratio (PMR) reduction of the thermal plant's load. Furthermore, economic investigations estimated a marginal BESS cost of 287.1 €/kWh revealing the financial viability of the proposed integrated system, in at least the case of geographical islands.

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Peak shaving algorithm with dynamic minimum voltage tracking for battery storage systems in microgrid applications

Cited by 26 publications

References 21 publications

Assessing the benefits of decentralised residential batteries for load peak shaving

Assessing the benefits of decentralised residential batteries for load peak shaving

Optimal Design of Hybrid PV-Battery System in Residential Buildings: End-User Economics, and PV Penetration

Energy management and techno‐economic assessment of a predictive battery storage system applying a load levelling operational strategy in island systems

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