Framework for Energy Efficiency Enhancement through Process Parameter Optimization in Power Systems

Rising demand, aging assets, and decarbonization targets compel power systems to extract greater output from existing infrastructure while reducing losses. This paper proposes a unified framework for energy-efficiency enhancement that optimizes process parameters across generation, transmission, and end-use interfaces using physics-based models, data analytics, and operational constraints. The framework integrates three layers: (i) system characterization and uncertainty modeling; (ii) multiobjective optimization of controllable parameters; and (iii) supervisory implementation, measurement, and verification. It targets steady-state and transient regimes, enabling repeatable savings without compromising reliability or safety. In Layer (i), high-fidelity component models boilers, turbines, heat-recovery units, transformers, converters, and HVAC loads are calibrated with plant historians and PMU/SCADA data to establish baselines and parameter bounds. Stochastic elements such as renewable intermittency, load volatility, and ambient conditions are represented through probabilistic scenarios. Layer (ii) formulates a Pareto problem to minimize specific energy consumption and emissions while maximizing equipment life and reliability. Decision variables include excess air and firing rates, turbine inlet temperature, condenser pressure, feedwater setpoints, transformer tap positions, voltage/reactive power targets, and demand-side setpoints. Thermodynamic/exergy analysis identifies avoidable losses; sensitivity analysis ranks parameters; and metaheuristics or gradient-based solvers compute feasible optimums subject to ramp limits, protection settings, and regulatory constraints. Layer (iii) deploys model-predictive control with soft sensors to track optimal trajectories and adapt to disturbances. A measurement and verification protocol CUSUM detection, drift diagnostics, and IPMVP Option C quantifies realized savings and guards against rebound effects. Cybersecurity, change management, and operator training sustain performance over time. A staged roadmap is provided: rapid opportunity screening; pilot optimization on a critical unit; portfolio roll-out with automated KPI dashboards; and periodic re-tuning triggered by asset aging or process changes. Illustrative results indicate 2–5% heat-rate improvements in thermal units, 1–3% network loss reductions via volt/VAR optimization, and 5–10% HVAC fan/pump savings from variable-speed re-scheduling, subject to site constraints. The framework’s modularity supports brownfield retrofits and complements capacity additions by extracting latent efficiency through disciplined parameter tuning. Data governance, high-quality sensing, and digital twins ensure traceable decisions, while multi-criteria dashboards reconcile economics, reliability, emissions, and compliance to maintain stakeholder alignment and credibility.