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Purpose – The combined heat and power dispatch (CHPD) aims to optimize the outputs of online units in a power plant consisting thermal generators, co-generators and heat-only units. Identifying the operating point of a co-generator within its feasible operating region (FOR) is difficult. This paper aims to solve the CHPD problem in static and dynamic environments. Design/methodology/approach – The CHPD plant operation is formulated as an optimization problem under static and dynamic load conditions with the objectives of minimizations of cost and emissions subject to various system and operational constraints. A novel bio-inspired search technique, grey wolf optimization (GWO) algorithm is used as an optimization tool. Findings – The GWO-based algorithm has been developed to determine the preeminent power and heat dispatch of operating units within the FOR region. The proposed methodology provides fuel cost savings and lesser pollutant emissions than those in earlier reports. Particularly, the GWO always keeps the co-generator’s operating point within the FOR, whereas most of the existing methods fail. Originality/value – The GWO is applied for the first time to solve the CHPD problems. New dispatch schedules are reported for 7-unit system with the objectives of total fuel cost and emission minimizations, 24-unit system for economic operation and 11-unit system in dynamic environment. The simulation experiments reveal that GWO converges quickly, consistent and the statistical performance clears its applicability to CHPD problems.

This paper deals with a Unit Commitment (UC) problem of a power plant aimed to find the optimal scheduling of the generating units involving cubic cost functions. The problem has non convex generator characteristics, which makes it very hard to handle the corresponding mathematical models. However, Teaching Learning Based Optimization (TLBO) has reached a high efficiency, in terms of solution accuracy and computing time for such non convex problems. Hence, TLBO is applied for scheduling of generators with higher order cost characteristics, and turns out to be computationally solvable. In particular, we represent a model that takes into account the accurate higher order generator cost functions along with ramp limits, and turns to be more general and efficient than those available in the literature. The behavior of the model is analyzed through proposed technique on modified IEEE-24 bus system.

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Solar PV-connected distributed utility grid often faces several issues due to variable penetration of the generated power. It creates frequent disturbance in load side and increases the voltage instability. It is a great challenge to maintain the stability at distributed low-voltage grid and improve the quality of power. In order to overcome this problem, this paper proposes an adaptive voltage and current regulatory approach to improve the power quality in a solar PV-integrated low-voltage utility grid. It supplies auto-adjustable reactive power during the small and large voltage deviations in the grid. The proposed approach assures that the load bus voltage is maintained at 1 p.u. under variable environmental conditions. In addition, the power quality gets improved by injecting the power with improved quality. Three cases of standalone mode, grid-connected modes with and without STATCOM have been investigated and reported in this paper. To validate the proposed adaptive voltage and current regulatory approach, the dynamic results of regulated grid voltage under poor environmental conditions are analyzed and the measured results are presented in this paper. Furthermore, the obtained results are evaluated with the existing approaches such as BAT, firefly and elephant herding optimization (EHO) algorithms and reported in this paper.