Abstract
Cogeneration systems economic dispatch (CSED) provides an optimal scheduling of heat/ power generating units. The CSED aims to minimize the whole fuel cost (WFC) of the cogeneration units taking into consideration their technical and operational limits. Then, the current paper examines the first implementation of dominant bio-inspired metaheuristic called heap-based optimization algorithm (HBOA). The HBOA is powered by an adaptive penalty functions for getting the optimal operating points. The HBOA is inspired from the organization hierarchy, where the mechanism consists of the interaction among the subordinates and their immediate boss, the interaction among the colleagues, and the employee's self-contribution. Based on the infeasible solutions' remoteness from the nearest feasible point, HBOA penalizes them with various degrees. Four case studies of the CSED are implemented and analyzed, which comprise of 4, 24, 84 and 96 generating units. The HBOA is proposed to solve CSED problem with consideration of transmission losses and the valve point impacts. An investigation with the recent optimization algorithms, which are supply demand optimization (SDO), jellyfish search optimization algorithm (JFSOA), and marine predators' optimization algorithm (MPOA), the improved MPOA (IMPOA) and manta ray foraging (MRF), is developed and elaborated. From the obtained results, it is clearly observed that the optimal solutions gained, in terms of WFC, reveal the feasibility, capability, and efficiency of HBOA compared with other optimizers especially for large-scale systems. case.
Highlights
The importance of cogeneration systems economic dispatch (CSED) is evident in achieving the minimum operating costs of cogeneration units with optimum scheduling of heat and power units as well with keeping of operational constraints, which are heat and power balance constraint, valve-point effect, and generation capacity limits which take into consideration combined heat and power (CHP) units’ nonconvex feasible operating areas
From the economic perspective, the yearly savings with the application of the proposed heap-based optimization algorithm (HBOA) as compared with the whole fuel cost (WFC) obtained by other conventional methods, lagrangian relaxation (LR) [3], sequential quadratic programming (SQP) [2], LR with surrogate subgradient (LRSS) [5] and benders decomposition (BD) [4], is about 268.056 $/year
It is observed that the obtained optimal solution achieved by HBOA is lower than the reported techniques which are whale optimization algorithm (WOA) [7] and multi-player harmony search (MPHS) [18] as well as the recent techniques applied in this article which are marine predators’ optimization algorithm (MPOA), improved MPOA (IMPOA), manta ray foraging (MRF), supply demand optimization (SDO) and jellyfish search optimization algorithm (JFSOA)
Summary
LITERATURE REVIEW A plethora of conventional and mathematical approaches have been developed to solve CSED optimization problem such as sequential quadratic programming (SQP) [2], lagrangian relaxation (LR) [3], benders decomposition (BD) [4] and LR with surrogate subgradient (LRSS) multiplier updates [5] These optimization techniques may converge to a local optimum, which is highly dependent on the initial starting points. Hybrid non-dominated sorting genetic algorithm with multi-objective PSO [30], multi-verse optimization (MVO) [24], and an enhanced shuffle frog leaping optimizer [31] have been efficiently applied for the same purpose but their validations were restricted to just small-scale applications of 5-units and 7-units systems. C. CONTRIBUTION AND PAPER ORGANIZATION The paper presents a solution to the combined heat and power economic dispatch problem using a heap-based optimizer.
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