Fuzzy Control Algorithm for Battery Storage and Demand Side Power Management for Economic Operation of the Smart Grid System at Naresuan University, Thailand

Mansiri, Kongrit; Sukchai, Sukruedee; Sirisamphanwong, Chatchai

doi:10.1109/access.2018.2838581

Cited by 43 publications

(89 citation statements)

References 33 publications

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“…This allowed the 2kWh energy storage system to represent a 90 kWh system within the experiments. The SoC of the actual ESS was estimated using the open circuit voltage, V OC and the operating voltage range, V max and V min as shown in ( 28) from [44], [45]. where S, s and e are the total number of samples, simulation data and experimental data for time t respectively.…”

Section: B Experimental Resultsmentioning

confidence: 99%

Optimization of Fuzzy Energy-Management System for Grid-Connected Microgrid Using NSGA-II

Teo

Logenthiran

Woo

et al. 2021

IEEE Trans. Cybern.

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This paper proposes a fuzzy logic based energy management system (FEMS) for a grid-connected microgrid with renewable energy sources (RES) and energy storage system (ESS). The objectives of the FEMS are reducing the average peak load (APL) and operating cost through arbitrage operation of the ESS. These objectives are achieved by controlling the charge and discharge rate of the ESS based on the state-of-charge of ESS, the power difference between load and RES, and electricity market price. The effectiveness of the fuzzy logic greatly depends on the membership functions. The fuzzy membership functions of the FEMS are optimized offline using a Pareto based multiobjective evolutionary algorithm, nondominated sorting genetic algorithm (NSGA-II). The best compromise solution is selected as the final solution and implemented in the fuzzy logic controller. A comparison with other control strategies with similar objectives are carried out at a simulation level. The proposed FEMS is experimentally validated on a real microgrid in the energy storage test bed at

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Section: B Experimental Resultsmentioning

confidence: 99%

Optimization of Fuzzy Energy-Management System for Grid-Connected Microgrid Using NSGA-II

Teo

Logenthiran

Woo

et al. 2021

IEEE Trans. Cybern.

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“…Recently, the potential of energy storage systems (ESSs) has been investigated to overcome power system problems [30], [31], such as voltage problems [32]- [34], wind farm fluctuation [35], [36], load balancing and transmission congestion [37], [38]. Furthermore, the effectiveness of Large-scale ESSs in providing ancillary services and increasing grid reliability is proved in [39].…”

Section: Related Workmentioning

confidence: 99%

Efficient Allocation Strategy of Energy Storage Systems in Power Grids Considering Contingencies

et al. 2019

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This paper addresses the allocation of Energy Storage Systems (ESSs) in power grids by finding the optimal number of ESSs and their locations and sizes with the goal of improving reliability in contingency states. We propose a contingency-sensitivity-based heuristic to decide the optimal number of ESSs and the most effective locations for ESSs support, while circumventing the combinatorial nature of the siting problem. A contingency sensitivity index (CSI) is proposed which represents the impacts of contingencies on the network buses. The CSI ranks the buses, such that those with higher impacts have the privilege for installing ESSs. For the ESSs being fixed, the sizing is formulated as a multi-period AC optimal power flow (OPF) problem and solved by Self-Organizing Hierarchical Particle Swarm Optimization with Time Varying Acceleration Coefficients (HPSO TVAC). The optimal ESSs sizes are selected by minimizing a total cost, which includes investment cost of storage devices, bus voltage deviation cost and average network losses cost. Uncertainties of the renewable generation are accounted by considering different realizations of the generation profiles, then, ESSs sizes are selected by taking the worst case approach. The proposed methodology has been demonstrated on the modified IEEE 30-bus system and Tunisian Grid. The obtained results show the effectiveness of the proposed methodology and the related reliability merits.INDEX TERMS Contingency sensitivity matrix, energy storage systems, multi-period OPF, power grid, siting and sizing.

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“…However, EESs are expensive and amount to the economic viability of the overall system [9]. Super inductors, ultracapacitors, flywheels, and fuel cells are various commonly accessible ESSs [10], [11]. Amongst the stated storage systems, Battery Energy Storage System (BESS) is less costly and delivers capable results when incorporated with RE systems [12].…”

Section: Introductionmentioning

confidence: 99%

Moving Regression Filtering With Battery State of Charge Feedback Control for Solar PV Firming and Ramp Rate Curtailment

Syed

Khalid

2021

IEEE Access

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The surge in energy demand needs to be met with environmentally pleasant resources to reduce the production of greenhouse gases. Solar Photovoltaic (PV) power is a widespread choice as it is accessible in plenty and is comparatively inexpensive. However, the large-scale penetration of intermittent PV power causes multiple variabilities in the grid such as frequency issues and voltage deviations. To counteract these instabilities, Battery Energy Storage System (BESS) is integrated into the grid as it reduces the PV fluctuations and promotes optimal operation. Nevertheless, storage systems are expensive, and thus smoothing filters are coupled with the BESS for cost reduction and power smoothing. Formerly, traditional filters such as Low Pass Filters (LPF), Moving Average (MA), and Moving Median (MM) filters have been proposed for power smoothing. However, these filters have inadequate power tracking capabilities particularly with longer window sizes (W.S) and time constants, which subsequently depreciates the storage system performance. To compensate for the delayed power tracking, larger energy storage systems are required which in turn adds to the overall operational costs. This paper proposes the Moving Regression (MR) filter combined with state of charge (SoC) feedback control for solar PV variability reduction, reduced time delay, decreased battery charging/discharging power, and ramp rate. Simulation results attest that the MR filter achieves better solar power smoothing without increasing the BESS capacity. Also, the performance of the MR filter is less affected with the increase in window sizes. Additionally, the execution of the introduced smoothing filter was found to be superior when assessed against the LPF, MA, MM, Savitsky-Golay (SG), and the Gaussian filter. In comparison to the SG (W.S = 53), the MR (W.S = 45) filter reduces the battery charging/discharging power by approximately 30.48% and as opposed to the LPF (T.C = 48), the peak SoC is reduced by 19.1%.INDEX TERMS Battery energy storage system, low pass filter, machine learning, moving average filter, moving regression filter, renewable energy, solar power smoothing.

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Fuzzy Control Algorithm for Battery Storage and Demand Side Power Management for Economic Operation of the Smart Grid System at Naresuan University, Thailand

Cited by 43 publications

References 33 publications

Optimization of Fuzzy Energy-Management System for Grid-Connected Microgrid Using NSGA-II

Optimization of Fuzzy Energy-Management System for Grid-Connected Microgrid Using NSGA-II

Efficient Allocation Strategy of Energy Storage Systems in Power Grids Considering Contingencies

Moving Regression Filtering With Battery State of Charge Feedback Control for Solar PV Firming and Ramp Rate Curtailment

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