Comparative Assessment of Rule-Based Peak Shaving, Load Shifting, and Valley Filling Strategies in PV–Battery Residential Systems

Residential photovoltaic (PV) and battery systems operating under time-of-use tariffs create genuine opportunities for demand-side management, yet comparative evidence across strategies under realistic operating constraints remains scarce, and most reported gains rely on forecasting or optimization frameworks that are difficult to deploy on existing residential hardware. Three deterministic, rule-based strategies, namely peak shaving, load shifting, and valley filling, were evaluated using a verified, energy-conserving MATLAB (Version: R2024b), hourly simulation for a typical European prosumer (6 kWp PV, 6 kWh battery, approximately 6000 kWh annual consumption), incorporating realistic battery constraints and actual three-tier tariff structures across 8760 operational hours. Baseline operation without demand management achieved 31.7% self-consumption, 85.4% grid dependence, and an annual cost of €872. Load shifting produced the strongest economic outcome, reducing costs to €720 (€152 savings), with 36.1% self-consumption and 70.1% grid dependency. Peak shaving saved €126 through automated battery control alone, reaching 35.2% self-consumption without requiring behavioral change. Valley filling prioritized grid stability, lowering power fluctuations by 31%, while achieving 33.6% self-consumption and €112 in savings. No single strategy optimized all objectives simultaneously, and strategy selection should depend on prosumer priorities; the rule-based framework further offers a deployable performance reference against which future forecast- and optimization-based controllers can be assessed.