A voltage source inverter (VSI) is the key component of grid-tied AC Microgrid (MG) which requires a fast response, and stable, robust controllers to ensure efficient operation. In this paper, a fuzzy logic controller (FLC)-based direct power control (DPC) method for photovoltaic (PV) VSI was proposed, which was modelled by modulating MG’s point of common coupling (PCC) voltage. This paper also introduces a modified grid synchronization method through the direct power calculation of PCC voltage and current, instead of using a conventional phase-locked loop (PLL) system. FLC is used to minimize the errors between the calculated and reference powers to generate the required control signals for the VSI through sinusoidal pulse width modulation (SPWM). The proposed FLC-based DPC (FLDPC) method has shown better tracking performance with less computational time, compared with the conventional MG power control methods, due to the elimination of PLL and the use of a single power control loop. In addition, due to the use of FLC, the proposed FLDPC exhibited negligible steady-state oscillations in the output power of MG’s PV-VSI. The proposed FLDPC method performance was validated by conducting real-time simulations through real time digital simulator (RTDS). The results have demonstrated that the proposed FLDPC method has a better reference power tracking time of 0.03 s along with reduction in power ripples and less current total harmonic distortion (THD) of 1.59%.

The development of artificial intelligence (AI) based techniques for electricity price forecasting (EPF) provides essential information to electricity market participants and managers because of its greater handling capability of complex input and output relationships. Therefore, this research investigates and analyzes the performance of different optimization methods in the training phase of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for the accuracy enhancement of EPF. In this work, a multi-objective optimization-based feature selection technique with the capability of eliminating non-linear and interacting features is implemented to create an efficient day-ahead price forecasting. In the beginning, the multi-objective binary backtracking search algorithm (MOBBSA)-based feature selection technique is used to examine various combinations of input variables to choose the suitable feature subsets, which minimizes, simultaneously, both the number of features and the estimation error. In the later phase, the selected features are transferred into the machine learning-based techniques to map the input variables to the output in order to forecast the electricity price. Furthermore, to increase the forecasting accuracy, a backtracking search algorithm (BSA) is applied as an efficient evolutionary search algorithm in the learning procedure of the ANFIS approach. The performance of the forecasting methods for the Queensland power market in the year 2018, which is well-known as the most competitive market in the world, is investigated and compared to show the superiority of the proposed methods over other selected methods.

In recent years significant changes in climate have pivoted the distribution system towards renewable energy, particularly through distributed generators (DGs). Although DGs offer many benefits to the distribution system, their integration affects the stability of the system, which could lead to blackout when the grid is disconnected. The system frequency will drop drastically if DG generation capacity is less than the total load demand in the network. In order to sustain the system stability, under-frequency load shedding (UFLS) is inevitable. The common approach of load shedding sheds random loads until the system’s frequency is recovered. Random and sequential selection results in excessive load shedding, which in turn causes frequency overshoot. In this regard, this paper proposes an efficient load shedding technique for islanded distribution systems. This technique utilizes a voltage stability index to rank the unstable loads for load shedding. In the proposed method, the power imbalance is computed using the swing equation incorporating frequency value. Mixed integer linear programming (MILP) optimization produces optimal load shedding strategy based on the priority of the loads (i.e., non-critical, semi-critical, and critical) and the load ranking from the voltage stability index of loads. The effectiveness of the proposed scheme is tested on two test systems, i.e., a 28-bus system that is a part of the Malaysian distribution network and the IEEE 69-bus system, using PSCAD/EMTDC. Results obtained prove the effectiveness of the proposed technique in quickly stabilizing the system’s frequency without frequency overshoot by disconnecting unstable non-critical loads on priority. Furthermore, results show that the proposed technique is superior to other adaptive techniques because it increases the sustainability by reducing the load shed amount and avoiding overshoot in system frequency.

Load flow solutions refer to the steady-state stability of power systems and have a crucial role in the design and planning of slow-changing elements; e.g., in online tab changing actions, automatic generation control, over-excitation limiters and the power recovery characteristics of a load. Therefore, the purpose of this work was to show the connectivity between load flow analysis and long-term voltage stability using a generator model by introducing a novel voltage stability assessment based on the multi-machine dynamic model along with the load flow study for a power grid. The Euclidean distance (ED) was used to introduce a new voltage stability index based on the voltage phasor profile for real-time monitoring purposes. The effects of reactive power compensation, in addition to load-generation patterns and network topology changes in the system behavior, could be seen clearly on the voltage profiles of the buses. Thus, the increased values for the EDs of the buses’ voltage amplitudes—from 0 to around 1.5 (p.u.)—implied that the system was approaching the voltage collapse point, corresponding to the Jacobian matrix singularity of the load flow equation. Moreover, the weakest load bus with respect to any system change was also identified. Indeed, the criticality of any network interruption was in direct proportion to this voltage stability index. The proposed method was validated using the IEEE 118-bus test system.

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