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.
The electrical power generation from fossil fuel releases several contaminants into the air and this become excrescent if the generating unit is fed by Multiple Fuel Sources (MFS).The ever more stringent environmental regulations have forced the power producers to produce electricity not only at the cheapest price but also at the minimum level of pollutant emissions. Inclusion of this issue in the operational task is a welcome perspective. The cost effective and environmental responsive power system operations in the presence of MFS can be recognized as a multi-objective constrained optimization problem with conflicting operational objectives. The modern meta-heuristic algorithm namely, Ant Lion Optimizer (ALO) has been applied for the first time to obtain the feasible solution. The fuzzy decision-making mechanism has been integrated to determine the Best Compromise Solution (BCS) in the multi-objective framework. The intended algorithm is implemented on the standard test systems considering valve-point effects, CO2 emission and tie-line limits.
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