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Energy storage is becoming increasingly important for isolated power systems having overall low inertia. Among many energy storage devices, superconducting magnetic energy storage (SMES) is most suited for improved frequency control in isolated power systems, due to its outstanding advantages. However, a small rating SMES device has operational constraints, therefore a suitable control strategy is required for its profitable and constrained operation. An adaptive controller which encapsulates on-line identification with model predictive control is proposed in this paper. A recursive least-squares algorithm is used to identify a reduced-order model of wind-diesel power system on-line. Based on the identified model and a simple discrete time model of SMES unit, an adaptive generalized predictive control scheme (AGPC) considering constraints on SMES current level and converter rating is formulated. The scheme yields a control signal which on one hand keeps the system frequency deviations to minimum and on the other hand forces the SMES device to operate within and near its operational constraints, for profitable operation. Simulation studies are performed to illustrate the potency of the proposed strategy in achieving all the control objectives. 0 = 16.17 kJ .

Summary Delivering reliable and adequate power to the consumer is essentially critical. Standard quality of power is measured by its frequency stability and power flow between different control areas. Therefore, power‐system‐control is generally attained with load‐frequency‐control (LFC). This paper presents the LFC of a hybrid power system comprising of conventional‐thermal, solar‐thermal and electric vehicle (EV). The inclusion of EVs into the utility grid, generation‐rate‐constraint of thermal plants and time‐delay in all three control areas makes the proposed power system more realistic and a practical one. This makes the system a bit complex and requires a robust controller to function optimally. An integral‐double‐derivative (IDD) controller is applied for this study and the system responses are compared with those of classical controllers. The controller gains are optimized using the powerful magnetotactic bacteria optimization (MBO) technique, which find its maiden application in power system studies. MBO optimized IDD controller performs better in contrast to other classical controllers. Further, a fuzzy logic control (FLC) is developed to optimize the gains of optimal IDD controller. System dynamic responses comparison of both fuzzy optimized IDD and MBO optimized IDD controller reveals an inclination towards the performance of fuzzy optimized IDD controller. This is validated with the help of demerit index. Robustness analysis is also done to highlight the strength of IDD controller optimized with both MBO and FLC for various system changes, such as load perturbation, system loading and solar irradiance. The critical review of all these analysis infers the effective performance of fuzzy optimized IDD controller.

In this article, a comprehensive model is developed to study the performance of a hybrid wind-diesel energy storage system. Energy storage system exchanges both real and reactive power with the wind-diesel system to improve both frequency and voltage. For obtaining pre-disturbance steady-state scenario of the system, a modified load flow algorithm is proposed which calculates induction machine slip and other initial conditions, in addition to the results obtained with conventional load flow. A compact model is developed by integrating network model, energy storage system and machine equations along with the associated control systems. For frequency estimation concept, centre of inertia is utilised. Energy storage system is modelled as a controllable current source. Complete modelling is carried out in MATLAB/Simulink environment. Simulations are carried out for load disturbance as well as wind perturbations to demonstrate the efficacy of the proposed scheme.

The random nature of wind power along with active and reactive load changes results in both frequency and voltage fluctuations in a wind–diesel power system. In order to improve the dynamic performance by regulating the frequency as well as voltage of the system, an adaptive sliding mode control strategy is proposed on superconducting magnetic energy storage unit interfaced with a wind–diesel power system. Sliding mode control strategy developed with the superconducting magnetic energy storage unit achieves fast and effective exchange of real and reactive power via firing angle control of the converter. With the help of suitable switching surface design and use of adaptive control law, chattering elimination and controller robustness is achieved. This work is carried out in MATLAB/Simulink, and simulation results presented shows a positive impact of proposed scheme.

This work presents power generation control of a two-area hybrid restructured power system, initially integrated with renewable energy source (RES) and electric vehicle (EV). For further analysis, additional inclusion of FACTS and energy storage devices in the two-area hybrid restructured power system is to be analyzed for improved system dynamics. The hybrid restructured power system is a complex nonlinear system that brings light to the major problem of system dynamic control due to insufficient damping under varying loading circumstances. To solve this problem, a robust control approach with rapid acting controllable and storage devices are an immediate need for advanced power system. Motivated from the fact that cascaded fractional order controllers exhibit better performance in comparison to classical controllers, this article proposes an advanced cascaded fractional-order ID-fractional-order PD (cascaded FOID-FOPD) controller for system performance enhancement of hybrid restructured power system. The maiden application of satin bower bird optimizer technique to optimize the secondary controller gains is employed. The proposed controller's superiority is demonstrated by comparing the results to the other existing controllers. The impact of RES's generation and EVs on system dynamics for cascaded FOID-FOPD controller is also explored. Furthermore, the sensitivity analysis is done to reflect that optimized gains of cascaded FOID-FOPD controller are supportive enough for variations in RESs, load perturbations, capacity ratio, and disco participation matrix.

Summary The present paper proposes a novel state‐observer (SO) based integral‐double‐derivative controller for simultaneous frequency‐voltage control operation of a hybrid power system. The hybrid system consists of solar‐thermal, conventional‐thermal, diesel‐plant, and modern day electric vehicle (EV), establishing a concurrent system frequency, voltage, and the corresponding tie‐line power control. In order to exhibit realistic approach, the hybrid system is provided with appropriate system non‐linearities. For the desired performance of the presented hybrid power system, controller gain parameters are optimized by means of a highly effective magnetotactic‐bacteria‐optimization (MBO) technique. The objective function to be minimized is such formulated, resulting in optimized system performance. The reduction of objective function values by 13.6% for the proposed controller is exhibited. Similarly, the demerit index shows a reduction of 25% as compared with other controllers. Sensitivity test pertaining to extensive variations of system parameters from the nominal values confirms the robustness of the optimal controller gains. The proposed control strategy is tested on a more complex three‐area hybrid power system, and the simulation results further validate the effective regulation approach of the optimal controller with a reduction of 12.9% in objective function value. The fact that EVs assist in improving system stability is also demonstrated. An innovative study related to the effects of large penetration of EVs on the system load demand is presented herewith.