• Energy Research

As China’s rapid urbanization continues and urban dwellers become more affluent, energy use in buildings is expected to grow. To understand how this growth can be slowed, we explore four scenarios for Chinese buildings, ranging from a high-energy-demand scenario with no new energy policies to lowest energy demand under a techno-economic-potential scenario that assumes full deployment of cost-effective efficient and renewable technologies by 2050. We show that, in the high energy demand scenario, building energy demand has an average annual growth rate of about 2.8%, with slower growth rates in the other three scenarios. In all scenarios, CO2 emissions grow slower than energy, with building CO2 peaking around 2045 in the high energy demand scenario, and as early as 2030 in the techno-economic-potential scenario. We show that although various technological solutions, systems and practices can be very effective in minimizing building energy use, rigorous policies are needed to overcome multiple implementation barriers.

Buildings consume more than one-third of the world’s primary energy. Reducing energy use in buildings with energy efficient technologies is feasible and also driven by energy policies such as energy benchmarking, disclosure, rating, and labeling in both the developed and developing countries. Current energy retrofits focus on the existing building stocks, especially older buildings, but the growing number of new high performance buildings built around the world raises a question that how these buildings perform and whether there are retrofit opportunities to further reduce their energy use. This is a new and unique problem for the building industry. Traditional energy audit or analysis methods are inadequate to look deep into the energy use of the high performance buildings. This study aims to tackle this problem with a new holistic approach powered by building performance data and analytics. First, three types of measured data are introduced, including the time series energy use, building systems operating conditions, and indoor and outdoor environmental parameters. An energy data model based on the ISO Standard 12655 is used to represent the energy use in buildings in a three-level hierarchy. Secondly, a suite of analytics were proposed to analyze energy use and to identify retrofit measures for high performance buildings. The data-driven analytics are based on monitored data at short time intervals, and cover three levels of analysis – energy profiling, benchmarking and diagnostics. Thirdly, the analytics were applied to a high performance building in California to analyze its energy use and identify retrofit opportunities, including: (1) analyzing patterns of major energy end-use categories at various time scales, (2) benchmarking the whole building total energy use as well as major end-uses against its peers, (3) benchmarking the power usage effectiveness for the data center, which is the largest electricity consumer in this building, and (4) diagnosing HVAC equipment using detailed time-series operating data. Finally, a few energy efficiency measures were identified for retrofit, and their energy savings were estimated to be 20% of the whole-building electricity consumption. Based on the analyses, the building manager took a few steps to improve the operation of fans, chillers, and data centers, which will lead to actual energy savings. This study demonstrated that there are energy retrofit opportunities for high performance buildings and detailed measured building performance data and analytics can help identify and estimate energy savings and to inform the decision making during the retrofit process. Challenges of data collection and analytics were also discussed to shape best practice of retrofitting high performance buildings.

The global issue of climate change has accelerated the international commitment to net-zero carbon emission development. Decarbonizing the building sector has been put on several governments’ sustainable development agendas. To provide a reference for decarbonizing the building sector, this paper summarizes the U.S. experience in zero-carbon buildings (ZCBs) from the aspects of policies, codes, and standards at the federal and local levels and those of professional societies. Based on the definition and boundaries of ZCBs, this paper introduces policies on building energy efficiency, electrification, on-site renewable energy deployment, and “buy clean”, illustrating highlights in building phases, energy systems, materials production, and fiscal incentives. The synergic efforts and coordination between federal and local levels and with professional societies are also introduced. Successful experiences in policy and standard implementation are summarized, including the systemic work of multilevel governance, clearly defined goals and stringent policies, constant upgrades of codes and standards, transparency in reporting and information sharing, and increased financial and investment opportunities. This paper provides concrete recommendations for developing zero-carbon building policies.

Due to reducing the reliance of buildings on fossil fuels, Passive House (PH) is receiving more and more attention. It is important that integrated optimization of passive performance by considering energy demand, cost and thermal comfort. This paper proposed a set three-stage multi-objective optimization method that combines redundancy analysis (RDA), Gradient Boosted Decision Trees (GBDT) and Non-dominated sorting genetic algorithm (NSGA-II) for PH design. The method has strong engineering applicability, by reducing the model complexity and improving efficiency. Among then, the GBDT algorithm was first applied to the passive performance optimization of buildings, which is used to build meta-models of building performance. Compared with the commonly used meta-model, the proposed models demonstrate superior robustness with the standard deviation at 0.048. The optimization results show that the energy-saving rate is about 88.2% and the improvement of thermal comfort is about 37.8% as compared to the base-case building. The economic analysis, the payback period were used to integrate initial investment and operating costs, the minimum payback period and uncomfortable level of Pareto frontier solution are 0.48 years and 13.1%, respectively. This study provides the architects rich and valuable information about the effects of the parameters on the different building performance.

Abstract Independent operation of single microgrids (MGs) faces problems such as low self-consumption of local renewable energy, high operation cost and frequent power exchange with the grid. Interconnecting multiple MGs as a multi-microgrid (MMG) is an effective way to improve operational and economic performance. However, ensuring the optimal collaborative operation of a MMG is a challenging problem, especially under disturbances of intermittent renewable energy. In this paper, the economic and collaborative operation of MMGs is formulated as a unit commitment problem to describe the discrete characteristics of energy transaction combinations among MGs. A two-stage adaptive robust optimization based collaborative operation approach for a residential MMG is constructed to derive the scheduling scheme which minimizes the MMG operating cost under the worst realization of uncertain PV output. Transformed by its KKT optimality conditions, the reformulated model is efficiently solved by a column-and-constraint generation (C&CG) method. Case studies verify the effectiveness of the proposed model and evaluate the benefits of energy transactions in MMGs. The results show that the developed MMG operation approach is able to minimize the daily MMG operating cost while mitigating the disturbances of uncertainty in renewable energy sources. Compared to the non-interactive model, the proposed model can not only reduce the MMG operating cost but also mitigate the frequent energy interaction between the MMG and the grid.

Abstract This paper proposes a risk-constrained decision-making approach for a wind power producer participating in the day-ahead market. In the developed model, a flexible demand response trading scheme between the wind power producer and different customers is employed. Through the proposed demand response mechanism, the wind power producer is able to trade demand response resource internally with different customers, and then trade energy externally with the market to increase the expected profit and the wind energy utilization. The uncertainties in the wind power and demand response are modeled by using the information gap decision theory approach from risk averse (robust) and risk-seeking (opportunistic) perspectives. The objective of the robust model is to maximize the robust level while satisfying the desired profit, whereas the opportunistic model aims to evaluate the possibility of achieving windfall profits with favorable uncertainties. The overall offering strategy problem is modeled as a bi-objective mixed integer nonlinear programming, which is linearized by proper techniques and solved efficiently by using the normal boundary intersection technique. Simulation results show that utilizing demand response resource to mitigate wind power deviations can increase a wind power producer’s profit and reduce potential risks. In addition, the results demonstrate that the proposed bi-objective optimization approach enables the wind power producer to select appropriate offering decisions with respect to uncertainties.

The energy crisis in Europe requires cost-effective evaluations of residential heating strategies to reduce costs and mitigate greenhouse gas emissions. This research studied different heating systems in China and Europe. Based on heating energy surveys, simulation models were developed and further expanded for European cities. Monte Carlo analyses were conducted to understand the heating demand and utility costs in Rome, Madrid, and Athens. The sensitivity analysis found that electrifying heating systems with heat pumps can reduce household heating costs and mitigate European cities' dependence on natural gas. However, the high upfront investment may hinder the cost-effective deployment of high-performance heat pump systems. Building envelope retrofits can also provide plausible energy savings despite relatively long payback periods. Financial incentive analyses were conducted to quantify how fiscal measures can improve technologies' techno-economic performance. Finally, the paper provided policy recommendations on future building cost-effective retrofits and heating electrification in Europe.