Solution of Combined Heat and Power Economic Dispatch Problem Using Genetic Algorithm

This research proposes a synergistic meta-heuristic algorithm for solving the extreme operational complications of combined heat and power economic dispatch problem towards the advantageous economic outcomes on the cost of generation. The combined heat and power (CHP) is a system that provides electricity and thermal energy concurrently. For its extraordinary efficiency and significant emission reduction, it is considered a promising energy prospect. The broad application of combined heat and power units requires the joint dispatch of power and heating systems, in which the modelling of combined heat and power units plays a vital role. The present research employs the genetic optimization algorithm to evaluate the cost function, heat and power dispatch values encountered in a system with simple cycle cogeneration unit and quadratic cost function. The system was first modeled to determine the various parameters of combined heat and power units towards solving its economic dispatch problem directly. In order for modelling to be done, a general structure of combined heat and power must be defined. The test system considered consists of four units: two conventional power units, one combined heat and power unit and one heat-only unit. The algorithm was applied to test system while taking into account the power and heat units, bounds of the units and feasible operation region of cogeneration unit. Output decision variables of 4-unit test systems plus cost function from Genetic Algorithm (GA), was determined using appropriate codes. The proposed algorithm produced a well spread and diverse optimal solution and also converged reasonably to the actual optimal solution in 51 iterations. The result obtained compared favourably with that obtained with the direct solution algorithm discussed in a previous paper. We conclude that the genetic algorithm is quite efficient in dealing with non-convex and constrained combined heat and power economic dispatch problem.

Operation Optimization of Liquid Cooling Systems in Data Centers by the Heat Current Method and Artificial Neural Network

Liquid cooling systems in data centers have been attracting more attentions due to its better cooling capability and less energy consumption. In order to propose an effective optimization method for the operation of indirect liquid cooling systems, this paper first constructs an experiment platform and applies the heat current method to build the global heat transfer constraints of the whole system. Particularly, the thermal conductance of each heat exchanger under different working conditions is predicted by the Artificial Neural Networks(ANN) trained by the historical data. On this basis, combining the heat transfer and fluid flow constraints together with the Lagrange multiplier method builds the optimization model with the objective of minimum pumping power consumption(PPC), solving which by the solution strategy designed obtains the optimal frequencies of the variable frequency pumps(VFPs). Operating with the optimal and other feasible operating conditions validates the optimization model. Meanwhile, the experiments with variable heat loads and flow resistances provide some guidelines for the optimal system operation. For instance, to address heat load increase of a branch, it needs to increase the frequencies of the VFPs, not only the corresponding hot loop but also the whole cold loop.