A Novel Heap-Based Optimizer for Scheduling of Large-Scale Combined Heat and Power Economic Dispatch

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 selfcontribution. 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 INDEX TERMSCogeneration systems economic dispatch, fuel cost minimization, heap based optimization algorithm, distribution reconfiguration, valve point impacts, transmission losses. Nomenclature: ai, bi, & ci The i th power plant cost coefficients aj; bj, & cj The j th heat plant cost coefficients ak, bk, ck, dk, ek & fk The k th unit cost coefficients C Total production costs ( ) Fuel cost of power unit i ( ℎ ) Fuel cost of j th heat plant ( , ) Operational cost of k th cogeneration unit Hd Heat demand in the system Np , Nh and Nc Number of power-only plants, heatonly plants and cogeneration units P Output of power generation units Pd Electric power demand PLoss Transmission losses & Valve-point cost coefficients H Output of heat generation units

Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems

“…This is elaborated in Table 2, which tabulates the comparative results of GTO. It achieved the minimum RMSE value and standard deviation values of 6.367E−4 and 4.405E−8, respectively, with respect to other recent optimization techniques, which were EO [47,55], FBI [46], HEAP [49,56], jellyfish search (JFS) optimizer [48,57], and EMPA [58,59], and other reported optimization techniques, which were PSO [12], ALO [18], flexible PSO (FPSO) [29], PGJAYA [34], classified perturbation mutation PSO (CPMPSO) [60], Hybrid Firefly and Pattern Search (HFAPS) [61], Lightning Attachment Procedure Optimization (LAPO) [62], Barnacles Mating Optimizer (BMA) [63], neighborhood scheme-based Laplacian MBA (NLBMA) [64], hybrid PSO-GWO algorithm (PSOGWO) [65], Enriched Harris Hawks optimization (EHHO) [66], and multi-verse optimizer (MVO) [67]. As shown in Table 2, the GTO technique for the SDM of KC200GT had the minimum error value compared with the various reported algorithms in the literature.…”

An Innovative Hybrid Heap-Based and Jellyfish Search Algorithm for Combined Heat and Power Economic Dispatch in Electrical Grids

“…This table demonstrates that the proposed HBJSA overwhelmed the standard HBA, the standard JSA, and the reported recent techniques for achieving minimum WFC. In addition, a comparison between the HBA, JSA, and the proposed HBJSA is conducted in Table 2 for the 24-unit system of CHP economic dispatch with respect to reported techniques such as PSO [40], time-varying acceleration coefficients-PSO (TVAC-PSO) [40], group search optimization (GSO) [41], and improved GSO (IGSO) [42], MRFO [28], and supply demand algorithm (SDA) [34]. In this table, ranking order is evaluated in ascending order based on the minimum WFC.…”

“…Additionally, the standard HBA, the standard JSA, and the proposed HBJSA effectively achieve the power and heat balance constraints with 100% accuracy, as illustrated in Table 3. modified group search optimization In addition to this, a comparison between the HBA, JSA, and proposed HBJSA is conducted in Table 4 for the 48-unit system of CHP economic dispatch with respect to other reported techniques such as MRFO [28], SDA [34] TVAC-PSO [40], CPSO [40], GSO [43], modified PSO [44], OTLBO [16], and MGSO [43] and gravitational search algorithm (GSA) [45]. Additionally, crow search algorithm (CSA) [46], grey wolf algorithm (GWA) [47], salp swarm algorithm (SSA) [48], multi-verse algorithm (MVA) [49], DE [50], MPA [51][52][53], civilized swarm optimization CSO [54] and Powell's pattern search PPS [54] are applied to the CHP economic dispatch for this system.…”

“…In addition, a comparison study between the HBA, JSA, and proposed HBJSA is conducted in Table 6 for the 84-unit system of CHP economic dispatch with respect to reported techniques such as WOA [20], MRFO [28], marine predators algorithm (MPA) [42], improved MPA (IMPA) [42], and SDA [34]. In this table, ranking order is evaluated in ascending order based on the minimum WFC.…”

“…Its simplicity and effectiveness enforce the research direction into its promising implementations in solving engineering problems. In [33], the HBA was efficiently utilized for parameter estimation of fuel cells, while it was applied for the CHP economic dispatch in [34] and optimal reactive power dispatch in [35]. Secondly, the standard JSA, inspired from jellyfish movements, was created by J.-S. Chou and D.-N. Truong in January 2021 [36].…”