Bi-level Optimization of Residential Community Energy Systems: Space Heating and DHW Loads under Multi-Energy Pricing
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PUBLIC SUMMARY
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Developed a hierarchical optimization approach for RCES managing space heating and DHW under multi-energy pricing.
Upper-level model optimizes electricity/heat supply and pricing to maximize the RCES operator's economic returns.
Lower-level model controls radiators and electric water heaters to adjust load profiles for cost reduction.
Solved the bi-level problem using KKT conditions and strong duality, transforming it into a tractable MILP model.
Case studies show effective supply-demand flexibility coordination, boosting operator profits and reducing consumer costs.
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ABSTRACT
Focusing on thermal demands including space heating and domestic hot water (DHW), this study develops a hierarchical optimization approach for residential community energy systems (RCES) operating under a multi-energy pricing mechanism. In the upper-level model, the RCES operator utilizes various energy conversion devices to jointly schedule electricity/heat supply and set retail multi-energy prices, aiming to achieve optimal economic returns. Conversely, the lower-level problem explicitly models controllable assets like radiators and electric water heaters, ensuring consumers can satisfy their thermal comfort and water usage needs. Guided by the operator’s price signals, residents act as followers to adjust their load profiles, thereby minimizing expenses and realizing integrated demand response (IDR). To solve this bi-level problem, the lower-level constraints are transformed using Karush-Kuhn-Tucker (KKT) conditions and strong duality theory, converting the original model into a tractable single-level mixed-integer linear programming (MILP) formulation. Case studies on a typical RCES indicate that accurately modeling controllable loads facilitates effective IDR. Furthermore, the framework successfully coordinates flexibility on both the supply and demand sides. The results confirm that this strategy yields a win-win outcome, simultaneously boosting the operator’s profits and reducing the energy expenditures of residential consumers. -
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Liang S., Jin X., Jia H., et al. (2026). Bi-level Optimization of Residential Community Energy Systems: Space Heating and DHW Loads under Multi-Energy Pricing. Energy Use 2:100036. https://doi.org/10.59717/ipj.energy-use.2026.100036
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Liang S., Jin X., Jia H., et al. (2026). Bi-level Optimization of Residential Community Energy Systems: Space Heating and DHW Loads under Multi-Energy Pricing. Energy Use 2:100036. https://doi.org/10.59717/ipj.energy-use.2026.100036
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Figure 1.
Schematic diagram of the RC model of one zone.
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Figure 2.
Case setting for network, weather, DHW demand and multi-energy prices.
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Figure 3.
Case setting for network, weather, DHW demand and multi-energy prices.
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Figure 4.
Results of residential consumers.
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Figure 5.
RCES operator’s profits and residential consumers’ energy cost in scenario I and scenario II.