The level of concern regarding the total energy consumption in new building clusters and urban districts (BCDs) has increased recently. Rising living standards have led to a significant increase in building energy consumption over the past few decades. A great potential for energy savings exists through energy quality management (EQM) for new BCDs. Quality of energy measures the useful work potential of certain energy. EQM in this thesis is defined as reducing energy demand, applying distributed renewable energy sources, and utilizing energy technology in sustainable way. According to this definition, tasks of EQM include energy supply system optimization and energy demand prediction.

Based on EQM, the optimization of BCDs’ energy supply systems aims to search for the most appropriate scenario, which is a trade-off between various aspects, such as energy performance and environmental impacts as well as system reliability. A novel multi-objective optimization approach for new BCDs is established in this thesis. Optimization algorithm is known as Genetic Algorithm (GA), which is used to address non-linear optimization problems. Two case studies are included in this thesis: the U.K. eco-town residential BCDs case and the Norway office BCDs case.

The U.K. case examines the application possibility of the approach in practical design. Optimization objectives involved in this case are the life-cycle global warming potential of the system and the system exergy efficiency. The total life-cycle global warming potential is minimized while the exergy efficiency is maximized. Different types of energy supply system scenarios are recommended with different optimization objective combinations (equal-importance, slightly exergy efficiency-oriented and slightly environment-oriented). The results show that the proposed approach can feasibly be an optimal design tool in practical use.

To provide deeper insights into the problem, the Norway case checks the expansibility of inserting additional objectives into the approach. Loss of Power Supply Probability (LPSP), which is one of the system reliability indicators, is additionally included in the optimization objectives. For this case, the approach guarantees the optimal scenarios that cannot exceed the desired LPSP with minimum life-cycle global warming potential and maximum exergy efficiency. Optimal scenarios with different desired LPSP values (0, 1%, and 5%) are compared. Comparison results demonstrate that optimal scenarios change significantly along with variations of the desired LPSP values. Therefore, system reliability is proven as one of the most important objectives for renewable energy system optimization. In the future, this approach can be applied to complex problems with more objectives.

Besides energy supply system optimization, an effective and precise BCDs energy demand model is needed. This model should be capable of providing reliable inputs (energy demand and load profiles) for energy supply system optimization and reducing unnecessary energy consumption. In principle, energy demand in BCDs is a complex task because numerous design criteria influence energy performance, which is hard to plan and pre-calculate. Establishing such a model would require a thorough decision base that prioritizes these design criteria and generally distinguishes the more important criteria from the less important ones. The study uses general survey aims to collect and identify the design criteria that affect the BCDs energy demand model and to evaluate the priorities of each criterion using the fuzzy Analytical Hierarchy Process (AHP) method. Four main criteria – location, building characteristics, government, and outdoor surrounding characteristics – are established, along with 13 secondary criteria. The results show that the use of the AHP method can accurately guide the energy demand model and automatically rank significant criteria. The method can provide the weighting value for each criterion as well as the relative ranking for the energy demand model.

This thesis aims to provide a systematic and holistic EQM method for BCDs energy system design at the beginning of the decision-making stage.