Combined heat and power - optimal power flow based on thermodynamic model with associated petroleum and wet gas utilization constraints

Oil fields produce associated petroleum and wet gas, which can be mixed with commercial natural gas as fuel. Associated petroleum and wet gas are a low cost, low quality fuel, whereas commercial natural gas is the opposite. Two parameters are affected by this mixture: the fuel cost and the power – steam output of gas turbine – heat recovery steam generators. This research develops a Unit Commitment and Optimal Power Flow model based on Mixed Integer Nonlinear Programming to optimize combined heat and power cost by considering the optimal mixture between associated petroleum - wet gas and commercial natural gas. A thermodynamic model is used to represent the performance of gas turbine – heat recovery steam generators when subjected to different fuel mixtures. The results show that the proposed model can optimize cost by determining the most efficient power – steam dispatch and optimal fuel mixture. Furthermore, the optimization model can analyse the trade-off between power system losses, steam demand and associated - wet gas utilization.

Applications of gravitational search algorithm in engineering

Gravitational search algorithm (GSA) is a nature-inspired conceptual framework with roots in gravitational kinematics, a branch of physics that models the motion of masses moving under the influence of gravity. In a recent article the authors reviewed the principles of GSA. This article presents a review of applications of GSA in engineering including combinatorial optimization problems, economic load dispatch problem, economic and emission dispatch problem, optimal power flow problem, optimal reactive power dispatch problem, energy management system problem, clustering and classification problem, feature subset selection problem, parameter identification, training neural networks, traveling salesman problem, filter design and communication systems, unit commitment problem and multiobjective optimization problems

Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithm

Hydro-power plants are able to produce electrical energy in a sustainable way. A known format for producing energy is through generation scheduling, which is a task usually established as a Unit Commitment problem. The challenge in this process is to define the amount of energy that each turbine-generator needs to deliver to the plant, to fulfill the requested electrical dispatch commitment, while coping with the operational restrictions. An optimal generation scheduling for turbine-generators in hydro-power plants can offer a larger amount of energy to be generated with respect to non-optimized schedules, with significantly less water consumption. This work presents an efficient mathematical modelling for generation scheduling in a real hydro-power plant in Brazil. An optimization method based on different versions of the Coral Reefs Optimization algorithm with Substrate Layers (CRO) is proposed as an effective method to tackle this problem.This approach uses different search operators in a single population to refine the search for an optimal scheduling for this problem. We have shown that the solution obtained with the CRO using Gaussian search in exploration is able to produce competitive solutions in terms of energy production. The results obtained show a huge savings of 13.98 billion (liters of water) monthly projected versus the non-optimized scheduling.European CommissionMinisterio de Economía y CompetitividadComunidad de Madri