Summary The ceaseless efforts by power system industries to promote sustainable and competitive electric power market structure in the deregulated environment have given rise to enormous research in the area of transfer capability of transmission networks. Due to high demand for electricity, transmission components are stressed to operate close to their operating limits, and this leads to a decrease in transmission efficiency. To address this issue, efficient evaluation of available transfer capability (ATC) is crucial for system planning, operation, and control. Several approaches have been proposed for ATC computation. Surprisingly, a comprehensive literature review on ATC computation is yet to be efficiently presented. Researchers have been able to come up with fast algorithms, but most of these algorithms are not accurate, and the presented accurate techniques are not fast enough for online applications. This paper presents a comprehensive review of the different approaches for ATC determination. It provides the concepts, methods, and the features of the ATC. For each technique, the state of the art of the several contributions made by researchers has been highlighted. This review reveals that there are issues regarding ATC calculation methods that need attention: the development of fast and accurate algorithm incorporating system dynamics and system uncertainties in ATC determination. Additionally, efforts on the incorporation of renewable energy generation in the ATC evaluation need to be intensified. This review will serve as one in all for researchers as well as a guide for the entrants in this field.

Available transfer capability is an index to measure the security and economic viability of an interconnected system. However, to accurately determine this index, other associated parameters need to be accurately evaluated. One of these parameters is the capacity benefit margin (CBM). For efficient power generation reliability and sustainability, a certain amount of supply capacity is commonly reserved by utilities, which in most cases remain unused, to reduce the effect of generation outage. To minimize this unused reserve, utilities usually reserve a predetermined amount of tie-line capacity between interconnected areas to have access to external supply. This tie-line reserved for this purpose is termed as capacity benefit margin (CBM). In this paper a technique for computing CBM is used, the sensitivity of CBM support from other areas to the increase in load in one of the areas is investigated, and conclusively, demand side management is proposed to improve the quantification of CBM. The contribution of this work is the assessment of the CBMs support from other areas during a critical condition, using the flexibility of DSM technique. The modified 24-bus IEEE reliability test system is employed for the verification of the approach.

In the generation of operating system planning, saving utility cost (SUC) is customarily implemented to attain the forecasted optimal economic benefits in a generating system associated with renewable energy integration. In this paper, an improved approach for the probabilistic peak-shaving technique (PPS) based on computational intelligence is proposed to increase the SUC value. Contrary to the dispatch processing of the PPS technique, which mainly relies on the dispatching of each limited energy unit in sequential order, a modified artificial bee colony with a new searching mechanism (MABC-NSM) is proposed. The SUC is originated from the summation of the Saving Energy Cost and Saving Expected Cycling Cost of the generating system. In addition, further investigation for obtaining the optimal value of the SUC is performed between the SUC determined directly and indirectly estimated by referring to the energy reduction of thermal units (ERTU). Comparisons were made using MABC-NSM and a standard artificial bee colony and verified on the modified IEEE RTS-79 with different peak load demands. A compendium of the results has shown that the proposed method is constituted with robustness to determine the global optimal values of the SUC either obtained directly or by referring to the ERTU. Furthermore, SUC increments of 7.26% and 5% are achieved for 2850 and 3000 MW, respectively.

Modern solar-powered pumping systems are already being utilized in various fields and industrial applications especially in agricultural water irrigation systems where pumping water at a remote location is required with fewer electricity accessibility from the grid is not feasible. PV solely depends on solar hours per day and it is not reliable when a location exhibits variable weather conditions, such as in Malaysia, having 12 solar hours daily but maximum 5-6 peak performance hours are as usually achieved due to the cloudy and rainy nature of the weather. In this case, PV-grid is not able to generate power as per daily demand and would create voltage sags, swell, and power outage when it is directly connected with the water pumping system for irrigation. To overcome this problem, a PV-DVR solar water pumping system (PV-DVRWP) is proposed in this paper that can mitigate power quality issues arising from PV-grid side such as voltage sags, voltage swells, and power outage by injecting the required amount of voltage into the line to maintain constant load voltage supply. The DVR controller strategy uses dq-abc frame mechanism to extract fault signals by using Park’s transformation method, PID, and hysteresis band PWM signal generator to drive the inverter of DVR connected with a battery storage system. The proposed methodology of the PV-DVRWP system mitigates power quality issues occurring from the PV-grid side by injecting compensating voltage as well as being able to support water pumping systems in case of power outage for a three-phase system.

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