Urban building energy modelling and multi-objective optimization for PED transition in an existing neighbourhood in Sweden

Artikel i vetenskaplig tidskrift, 2026

Transitioning existing urban neighbourhoods to Positive Energy Districts (PEDs) requires integrated planning to address complex energy, cost, and lifecycle carbon emission challenges. Existing integrated methodologies optimize buildings individually and overlook local energy systems potentials like shared renewables and battery storage. Moreover, existing studies on optimization of on-site renewable energy sources and building retrofitting
often rely on simplified typical-day representations, falling short in capturing critical operational dynamics over time. This paper introduces and applies an integrated urban building energy modelling and bi-objective mixedinteger linear programming framework. The framework co-optimizes building retrofits and heating systems, PhotoVoltaic (PV) systems, and Battery Energy Storage System (BESS) capacities over a 10-year horizon with hourly resolution, targeting minimal lifecycle costs and total carbon emissions (operational and embodied). Applied to a 1950s neighbourhood in Gothenburg, Sweden, the analysis demonstrated that achieving full PED status (net-zero energy import, net-zero carbon, and energy surplus) with the evaluated interventions is highly challenging. While optimized solutions reduced the grid dependency (to ∼63% with PV and BESS under volatile
prices) and overall carbon emissions (by ∼13% compared to baseline), neither complete net-zero energy import, full carbon neutrality including embodied impacts, nor an energy surplus were realized. The study quantitatively identified critical trade-offs between investment costs, embodied carbon, operational performance, and resilience. Optimal solutions were sensitive to local conditions, notably low-carbon district heating and electricity price volatility. The proposed framework provides a decision-support tool for strategic PED planning in existing urban areas, enabling an exploration of complex techno-economic and environmental trade-offs.