A research team in China has developed an optimized energy management strategy for hybrid wind–PV heat pump (HP) systems.
Their approach combines thermal and electrical energy storage with a seasonal energy-interaction framework that includes spring ground-source precooling, summer cooling, autumn ground-source preheating, and winter heating.
“The novelty of the methodology developed in this work is threefold,” the researchers said. “First, we propose a new energy interaction management strategy. Second, we introduce new indicators to evaluate the interaction capacity of power-to-load and the characteristics of a disconnected grid. Third, we conduct two-stage optimizations to determine the optimal system configuration and weekly dynamic interaction control state of power-to-load for both PV–HP and wind/PV–HP systems, considering technology, economy, and environment.”
The team based its analysis on a low-energy residential building (LERB) in Shenyang, northeastern China. The two-storey, 334.8 m² building was modeled using TRNSYS and SketchUp. Of roughly 160 m² of roof surface, 130 m² was suitable for PV installation. Hourly solar radiation ranged from 0 to 0.3 kWh/m², while ambient temperatures varied between –26.54 C and 32.18 C over the year.
The hybrid wind/PV–HP system was also modeled in TRNSYS. It includes 550 W PV modules, 3 kW wind turbines, a ground-source heat pump (GSHP), ground-source exchangers (GSEs), an air-source heat pump (ASHP), 40 kWh of batteries, and a water tank with phase-change materials (PCM). Renewable electricity powers the heat pumps, with surplus energy stored in batteries or exported to the grid. The GSHP serves as the primary supply unit, while the ASHP provides secondary heating or cooling.
The researchers evaluated four scenarios: a baseline system without advanced interaction strategies (Case 1); Case 1 plus the interaction strategy and ASHP (Case 2); Case 2 plus two-stage optimization and PV (Case 3); Case 3 plus wind generation (Case 4).
The first optimization stage used the NSGA-II algorithm for system sizing; the second applied particle swarm optimization to manage weekly battery state of charge.
Seasonal interaction strategies were designed to maximize renewable use. In spring and autumn, ground-source precooling and preheating help regulate soil temperature. In summer, renewable electricity drives cooling and thermal storage. In winter, day-ahead load forecasting and battery management maintain heating while reducing grid reliance.
The scientists found that adopting the interaction strategy improved the system’s power-to-load interaction and enabled zero-energy performance. The power load factor reached 1.45 and 1.34, while the system independence factor fell by 75.15% and 69.82% in the PV–HP and wind/PV–HP cases, respectively. Levelized cost of energy dropped by at least 54.70%, and system performance rose by at least 4%. Soil temperature decreased by only 0.42 C over ten years, mitigating long-term ground imbalance.
The optimal configuration for the wind/PV–HP system includes 13.12 kW of PV, two wind turbines, 25.46 kWh of batteries, a 6.17 kW GSHP, and a 2.76 m³ water tank. PV capacity in the PV–HP case is 18.14% higher than in the hybrid wind/PV–HP case.
“The second-stage optimization determines the weekly dynamic interaction control state of power-to-load based on the optimized system configuration,” the researchers stressed. Compared to the first stage, the second-stage optimization reduced the SIF by 15.00% and 16.00%, lowered LCOE by 4.70% and 4.62%, increased the self-consumption ratio by 5.88% and 4.76%, and raised carbon emissions by 4.70% and 4.62% in the PV–HP and wind/PV–HP cases, respectively.
The system was presented in “An optimized energy management strategy for wind-PV hybrid heat pump systems with dual storage: Enhancing power-to-load interaction,” published in Energy. Scientists from China’s Shenyang Jianzhu University and Shanghai Jiao Tong University have conducted the research.
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