HVAC Optimization Genetic Algorithm for Industrial Near‐Zero Energy Building Demand Response

Abstract

Demand response offers the possibility of altering the profile of power consumption of individual buildings or building districts, i.e., microgrids, for economic return. There is significant potential of demand response in enabling flexibility via advanced grid management options, allowing higher renewable energy penetration and efficient exploitation of resources. Demand response and distributed energy resource dynamic management are gradually gaining importance as valuable assets for managing peak loads, grid balance, renewable energy source intermittency, and energy losses. In this paper, the potential for operational optimization of a heating, ventilation, and air conditioning (HVAC) system in a smart near-zero-energy industrial building is investigated with the aid of a genetic algorithm. The analysis involves a validated building energy model, a model of energy cost, and an optimization model for establishing HVAC optimum temperature set points. Optimization aims at establishing the trade-off between the minimum daily cost of energy and thermal comfort. Predicted mean vote is integrated in the objective function to ensure thermal comfort requirements are met.

Hierarchical Optimization Scheduling of Active Demand Response for Distribution Networks in 5G Base Stations

“…Simulation Design. The strategy of each microgrid in the process of game bidding is simulated by the IEEE-9 bus system [30] based on the genetic algorithm (GA) [31], and simulation experiments are conducted by Matlab. The architecture of the multimicrogrid system based on IEEE-9 is shown in Figure 8.…”