Tie-line Power Flow Control Method for Grid-connected Microgrids with SMES Based on Optimization and Fuzzy Logic

Abstract

In an active distribution grid, renewable energy sources (RESs) such as photovoltaic (PV) and energy storage systems (e. g., superconducting magnetic energy storage (SMES)) can be combined with consumers to compose a microgrid (MG). The high penetration of PV causes high fluctuations of tie-line power flow and highly affects power system operations. This can lead to several technical problems such as voltage fluctuations and excessive power losses. In this paper, a fuzzy logic control based SMES method (FSM) and an optimized fuzzy logic control based SMES method (OFSM) are proposed for minimizing the tie-line power flow. Consequently, the fluctuations and transmission power losses are decreased. In FSM, SMES is used with a robust fuzzy logic controller (FLC) for controlling the tie-line power flow. An optimization model is employed in OFSM to simultaneously optimize the input parameters of the FLC and the reactive power of the voltage source converter (VSC) of SMES. The objective function of minimizing the tieline power flow is incorporated into the optimization model. Particle swarm optimization (PSO) algorithm is utilized to solve the optimization problem while the constraints of the utility power grid, VSC, and SMES are considered. The simulation results demonstrate the effectiveness and robustness of the proposed methods.

A demand side controller of electrolytic aluminum industrial microgrids considering wind power fluctuations

“…Under the assumption A3), an observer is constructed, driven by the measured error e , to achieve asymptotically tracking (x, d) . The error feedback controller can be designed in the form of: (16) �S = A� + BŴ + P (17) x, d) for tracking x and d asymptotically and the gain matrices (F ,G,H) . Equation ( 19) can be written in transfer function form as: By the detectability of A3), the observer is designed in the following form to estimate (x, d) using only the error signal e , as: with the pair (G 1 , G 2 ) designed such that all the eigenvalues of…”

“…The existing literature on power flow control of tieline mainly focuses on long-term time-scales. An optimized fuzzy logic controller is proposed in [17] to minimize the daily tie-line power flow in a grid-connected microgrid with PV and ESS. In [18], an adaptive robust tie-line scheduling system considering wind power uncertainty is presented to optimize tie-line power flows every 15 min while a coordination control algorithm is developed to smooth tie-line power with a time interval of 10 min [19].…”

“…However, the previously mentioned studies do not cover the detailed dynamic model of the demand side. Thus, the strategies in [17][18][19] may be difficult to apply for dealing with short-term tie-line power fluctuations (5 min or less). In addition, wind power fluctuations appear as time-varying disturbance signals.…”

Optimal Techno-Economic Planning of a Smart Parking Lot—Combined Heat, Hydrogen, and Power (SPL-CHHP)-Based Microgrid in the Active Distribution Network

“…In order to make much fewer simulations, the authors employed S2PEM and utilized deterministic routines to find the statistical moments of the time series [12]. The fuzzy logic model was employed to model load and power flow equations' probabilistic behavior [13], and an intelligent network was used to model the market pricing model [14]. ISO made a decision to choose the best bids among all of the DISCOS proposed prices.…”

Adaptive Coordination Strategy Based on Fuzzy Control for Electric Vehicles and Superconducting Magnetic Energy Storage—Towards Reliably Operating Utility Grids

“…Among several types of ESSs, superconducting magnetic energy storage (SMES) is considered as one of the most crucial storage technologies preferred in the fast charging/discharging processes. SMES has many advantages, such as minimum delay discharging time, high cycle efficiency, quick response in charging/discharging processes, long lifetime, and highpower density [2], [3]. Therefore, SMES can cooperate with the EVs to improve their charging behavior during all loading conditions, as well as to enhance the reliable operation of the grid during loading and off-loading periods.…”