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In this paper, an integrated approach to optimize electrical, natural gas, and district heating networks simultaneously is studied. Several interdependencies between these infrastructures are considered in details including a nonlinear part-load performance for boilers and CHPs besides the valve-point effect for generators. A novel approach based on selecting an appropriate set of state-variables for the problem is proposed that eliminates the addition of any new variable to convert irregular equations into a regular set while the optimization problem is still solvable. As a large optimization problem, the optimal solution cannot be achieved by conventional mathematical techniques. Hence, it is better to use evolutionary algorithms instead. In this paper, the well-known modified teaching–learning based optimization algorithm is utilized to solve the multi-period optimal power flow problem of multi-carrier energy networks. The proposed scheme is implemented and applied to a typical multi-carrier energy network. Results are compared with some other conventional heuristic algorithms and the applicability and superiority of the proposed methodology is verified. [ABSTRACT FROM AUTHOR]

Highlights: [•] In this paper an expert energy management system (EEMS) is presented. [•] A power forecasting module for wind generation capacity is presented. [•] The objective functions that must be minimized are operating cost and net emission. [•] A smart energy storage system (EES) for electrochemical batteries is presented. [•] A new modified Bacterial Foraging Optimization (MBFO) algorithm is presented. [Copyright &y& Elsevier]

Highlights: [•] This paper focused on the expansion planning optimization of energy systems. [•] Employing two form of energy: the expansion of electrical and thermal energies. [•] The main objective is to minimize the costs. [•] A new Modified Honey Bee Mating Optimization (MHBMO) algorithm is applied. [Copyright &y& Elsevier]

Highlights: [•] A new modified teaching–learning based algorithm is proposed. [•] A self-adaptive wavelet mutation strategy is used to enhance the performance. [•] To avoid reaching a large repository size, a fuzzy clustering technique is used. [•] An efficiently smart population selection is utilized. [•] Simulations show the superiority of this algorithm compared with other ones. [Copyright &y& Elsevier]

Highlights: [•] A new approach to the problem of optimal reactive power control variables planning is proposed. [•] The energy loss minimization problem has been formulated by modeling the load of system as a Load Duration Curve. [•] To solving the energy loss problem, the classic methods and the evolutionary methods are used. [•] A new proposed fuzzy teaching–learning based algorithm is applied to energy loss problem. [•] Simulations are done to show the effectiveness and superiority of the proposed algorithm compared with other methods. [Copyright &y& Elsevier]