The problem of distributing gas through a network of pipelines is formulated as a cost minimization subject to nonlinear flow-pressure relations, material balances, and pressure bounds. The solution method is based on piecewise linear approximations of the nonlinear flow-pressure relations. The approximated problem is solved by an extension of the Simplex method. The solution method is tested on real-world data and compared with alternative solution methods.

The gas transmission problem solved by an extension of the simplex algorithm

A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters

Dynamic evaluation of drilling leakage risk based on fuzzy theory and PSO-SVR algorithm

In this paper, improved single- and multi-objective Harris Hawks Optimization algorithms, called IHHO and MOIHHO, respectively are proposed and applied for determining the optimal placement of distribution generation (DG) in the radial distribution systems. Harris Hawks optimizer (HHO) is a new inspired meta-heuristic optimization technique that is mainly based on the intelligence behavior of the Harris hawks in chasing prey. The IHHO and MOIHHO are applied for determining the optimal size and location of DG at different operating power factors (p.f) with the aim of minimizing the total active power loss, reducing the voltage deviation (VD), and increasing the voltage stability index (VSI) considering the operational constraints of distribution system. In IHHO, the performance of the conventional HHO algorithm is improved using the rabbit location instead of the random location. In MOIHHO, grey relation analysis is applied for identifying the best compromise solution among the non-dominance Pareto solutions. To verify the effectiveness of the proposed algorithms, IEEE 33-bus and IEEE 69-bus radial distribution systems are used, and the obtained results are compared with those obtained by other optimization techniques. The results prove the efficiency of the proposed algorithms in terms of best solutions obtained so far for the single- and multi-objective scenarios.

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Optimal Placement of DGs in Distribution System Using an Improved Harris Hawks Optimizer Based on Single- and Multi-Objective Approaches

In this paper, an Improved version of the Slime Mould Algorithm (ISMA) is proposed and applied to efficiently solve the single-and bi-objective Economic and Emission Dispatch (EED) problems considering valve point effect. ISMA is developed to improve the performance of the conventional Slime Mould Algorithm (SMA). In ISMA, the solution positions are updated depending on two equations borrowed from the sine–cosine algorithm (SCA) to obtain the best solution. Multi-objective SMA (MOSMA) and Multi-objective ISMA (MOISMA) are developed based on the Pareto dominance concept and fuzzy decision-making. In the multi-objective EED problem, MOSMA and MOISMA are applied to minimize the total fuel costs and total emission with the valve point effect simultaneously. The proposed single-and bi-objective economic emission dispatch algorithms are validated using five test systems, 6-units, 10-units, 11-units, 40-units, and 110-units. The performance of the proposed algorithm is compared with Harris Hawk Optimizer (HHO), Jellyfish Search optimizer (JS), Tunicate Swarm Algorithm (TSA), Particle swarm optimization (PSO), and SMA algorithms. The results show that the proposed algorithms are more robust than other well-known algorithms. Feasible solutions using the proposed algorithms are also achieved, which adjust the schedule of generation without violation of the operating generation limits.

Development and application of slime mould algorithm for optimal economic emission dispatch

Risk assessment of integrated electrical, natural gas and district heating systems considering solar thermal CHP plants and electric boilers

Solar cell is one of the important renewable energy resources, and it is considered a promising source for energy challenges in the future. The identification of solar cell model parameters is very important due to the control and the simulation of PV systems. In this paper, an enhanced teaching–learning-based optimization (ETLBO) algorithm is proposed and applied to estimate the photovoltaic cells parameter. The ETLBO is proposed to improve the performance of conventional TLBO and reduce its search space by adjusting the parameters which control the explorative and exploitative phases to achieve the suitable balancing. The proposed algorithm is validated using real dataset of photovoltaic single-diode and double-diode models. In addition, the proposed algorithm is tested on the dataset of two real PV panels (polycrystalline and monocrystalline). The results obtained by the proposed algorithm are compared with those obtained by other well-known optimization algorithms. All results prove the effectiveness and superiority of proposed algorithm compared with other optimization techniques.

Photovoltaic Cells Parameter Estimation Using an Enhanced Teaching–Learning-Based Optimization Algorithm

Solution of optimal power flow (OPF) problem is very important for power system operation, planning and energy management. OPF analysis aims to find the optimal solution of system nonlinear algebraic equations with satisfying operational constraints. In this paper, a new and efficient technique called Jaya optimizer is comprehensively applied to solve the OPF problem in power systems. Jaya optimization algorithm is characterized with the movement toward the best solution and avoiding the trapping into local optima. Different frameworks are developed for solving the single- and multi-objective (two- to five-objective functions) OPF problems. These frameworks are developed to achieve the following objective functions: fuel cost minimization, voltage deviation minimization, voltage stability enhancement, power loss minimization and emission minimization. In the developed multi-objective OPF framework, Pareto concept is combined with Jaya optimization algorithm to obtain a set of non-dominated solutions, and then the best compromise solution is extracted using fuzzy set theory. The developed OPF frameworks are validate using two standard IEEE test systems with 23 studied cases. The results prove the effectiveness and superiority of the developed OPF frameworks compared with other well-known optimization algorithms.

Single- and multi-objective optimal power flow frameworks using Jaya optimization technique

In this article, a combined optimization approach (COA) based on Salp Swarm Algorithm (SSA) and loss sensitivity indices is proposed to determine the optimal allocation of shunt capacitors (SCs) in...

Optimal Capacitor Allocation in Radial Distribution Networks Using a Combined Optimization Approach

The aim of the Optimal Reactive Power Dispatch (ORPD) solution is assigning the most confident operating point for optimal secure and operation state. The required operating point that will be determined by ORPD solution include generators voltage, transformers tap, Var compensators output for minimizing the power losses and improving the voltage profile. It is well-known that the load demand in system changes hourly and seasonally. Thus, the load variation is a source of uncertainty in power system planning. This paper solves the ORPD problem with considering uncertainties in load demand using the enhanced grey wolf optimizer (EGWO). The EGWO technique is developed to enhance the exploration and exploration capabilities of the basic grey wolf optimizer (GWO). The EGWO is based on Levy flight distribution with adaptive operators to avoid the stagnation of the traditional GWO. The EGWO is tested on IEEE 30-bus system with and without considering the uncertainty of load demand and the obtained results are compared with those captured by the traditional GWO. The results show the effectiveness of EGWO for solving the ORPD problem in terms of decreasing power losses and enhancing voltage profile.

Juan Yu

Papers

Risk assessment of integrated electrical, natural gas and district heating systems considering solar thermal CHP plants and electric boilers

Photovoltaic Cells Parameter Estimation Using an Enhanced Teaching–Learning-Based Optimization Algorithm

Single- and multi-objective optimal power flow frameworks using Jaya optimization technique

Optimal Capacitor Allocation in Radial Distribution Networks Using a Combined Optimization Approach

Solving Optimal Reactive Power Dispatch Problem Considering Load Uncertainty