Non-smooth/non-convex economic dispatch by a novel hybrid differential evolution algorithm

Abstract

This paper presents a novel stochastic optimisation approach to determining the feasible optimal solution of the economic dispatch (ED) problem considering various generator constraints. Many practical constraints of generators, such as ramp rate limits, prohibited operating zones and the valve point effect, are considered. These constraints make the ED problem a non-smooth/nonconvex minimisation problem with constraints. The proposed optimisation algorithm is called selftuning hybrid differential evolution (self-tuning HDE). The self-tuning HDE utilises the concept of the 1/5 success rule of evolution strategies (ESs) in the original HDE to accelerate the search for the global optimum. Three test power systems, including 3-, 13-and 40-unit power systems, are applied to compare the performance of the proposed algorithm with genetic algorithms, the differential evolution algorithm and the HDE algorithm. Numerical results indicate that the entire performance of the proposed self-tuning HDE algorithm outperforms the other three algorithms.

Practical multi‐area bi‐objective environmental economic dispatch equipped with a hybrid gradient search method and improved Jaya algorithm

“…In contrast to the traditional approach that the TUs with no DPZs were managed to corporate in up and down spinning reserve requirements [33], [34], constraints (16) and (17) illustrate that all the TUs are able to provide the 10-min up/down reserves for which the up and down ramping rates are rationally modeled as As shown, the 10-min up and down reserve capabilities of each TU shrink to its sub-feasible region to avoid the load regulation from DPZs. The spinning reserve satisfaction with and without considering the DPZs for a typical TU is demonstrated in Fig.…”

An Improved Quantum-Behaved Particle Swarm Optimization Method for Economic Dispatch Problems with Multiple Fuel Options and Valve-Points Effects

“…The stopping criterion is set to 500. The result obtained from SQPSO is compared with some methods in the literature including IFEP [1], GA_PS_SQP [22], PC-PSO [23], SOH_PSO [23], NPSO [24] ,NPSO_LRS [24], PSO-GM [25], CBPSO_RVM [25], ICA-PSO [26], ACO [5], APSO(2) [27], HDE [28], ST-HDE [28] and IQPSO [29]. In addition, in order to compare the performance of the crossover operation in [18] with the proposed selective probability operator.…”

Economic Dispatch Using Hybrid Particle Swarm Optimization with Prohibited Operating Zones and Ramp Rate Limit Constraints

“…The following quoted here are some examples from recent literatures, which have used the combination of two different optimization techniques to solve the non linear economic dispatch problems. Simulated Annealing-Particle Swarm Optimization (SA-PSO) [28], Self Tuning Hybrid Differential Evolution (STH DE) [29], Variable Scaling Hybrid Differential Evolution (VSHDE) [30], Improved Genetic Algorithm with Multiplier Updating (IGAMU) [31], Quantuminspired version of the PSO using the harmonic oscillator (HQPSO) [32], Self-Organizing hierarchical Particle Swarm Optimization (SOH-PSO) [33], and Bacterial Forging with Nelder-Mead Algorithm (BFA-NM) [34].…”