The residential sector accounts for a major share of global electricity demand, and this demand is projected to rise sharply with the spread of automated appliances and heatwave-driven cooling loads. While fuzzy-logic HVAC control, dynamic pricing, and renewable-storage integration have each been studied individually, few works jointly quantify how a fuzzy-logic energy management controller interacts with real-time and time-of-use pricing and with renewable generation, storage, and load shifting within a single, reproducible residential simulation framework, leaving the combined saving potential and its attribution across mechanisms unclear. This study addresses that gap by developing a fuzzy-logic-based energy management controller for an interruptible HVAC load and a smart load simulator (SLS)–based model of critical and noncritical residential loads, evaluated under real-time pricing (RTP) and time-of-use (TOU) tariffs with and without local solar PV, wind, and battery storage. A MATLAB/Simulink GUIDE-based platform was used to run the simulations over a representative one-month operating horizon, with a 72-rule fuzzy inference base built on four input variables: outdoor temperature, price, demand, and occupancy. Simulation results show that the fuzzy HVAC controller reduces HVAC energy consumption by approximately 28% relative to manual thermostat operation under RTP and that combining renewable generation with noncritical load shifting reduces total household energy consumption and cost by 42.28% and 38.37%, respectively. The contribution of this work is a unified, reproducible residential energy management framework that jointly evaluates fuzzy HVAC control, dynamic-pricing response, and renewable-storage-shifting strategies within one consistent simulation environment, providing quantitative guidance for residential demand-side management design.