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With the opening of the Chinese electricity market, as a retailer that provides energy services to consumers, the park-integrated energy system (PIES) not only serves as an effective way to earn benefits and reduce carbon emissions but also impacts the energy consumption characteristics of consumers. The PIES implements this function by adjusting the energy selling price in free energy markets. The pricing mechanism model (P-M model) is established to obtain the energy selling price in the planning and design stages. In this model, the impact of the demand response on the energy configuration and the impact of the changes in energy configuration on the energy cost price are both considered. Additionally, the optimal result ensures that both the consumers and the PIES benefit simultaneously. The reactive demand response zone, which represents a consumer trap, is found in numerical studies. The results indicate the following: (1) from the perspective of P-M model optimization, the benefit exclusive point of the PIES is the optimal solution in the short term; (2) from the perspective of the long-term benefit, the ultimate result in the relationship between the PIES and consumers is that the PIES will share its profits with consumers; in other words, benefit sharing point is the optimal solution for the long term. [ABSTRACT FROM AUTHOR]

Methane has enormous potential as a carrier for renewable H 2. The required infrastructure for a methane gas economy is readily available, and it can compete with its energy-intensive liquefied hydrogen (LH 2) counterpart. In this work, we conducted a technoeconomic analysis of the production of liquefied synthetic methane (LSM) from ambient CO 2 and renewable H 2. The process entails (1) capture of CO 2 directly from ambient air, referred to as Direct Air Capture (DAC) using an amine-based system; (2) generation of H 2 from renewable sources, (3) conversion of CO 2 and H 2 to synthetic methane; and (4) liquefaction of synthetic methane. The technoeconomic model used in this study is based on mass/material energy flow analysis and includes the economics of all processes involved. Building on our previous work, we reduced the overall capture cost for the studied scale of 0.291 t/h CO 2 production from US$557 to ~$365/tCO 2. At a scale equivalent to 1 MtCO 2 /year capture, which is the basis for production of LSM in this work, we estimated the cost of DAC to be ~$114/tCO 2. Per GJ of methane produced, the overall cost of LSM was ~$31.5/GJ, comprising around $21.7/GJ ($2.4/kg H 2) for hydrogen production, $5.6/GJ for DAC, $1.4/GJ for methanation and $2.8/GJ for methane liquefaction. The energy conversion efficiency of LSM was lower than for LH 2 , mainly due to the large amount of heat required for DAC. The levelised cost per unit of energy for LSM was lower at small scales, while the costs were the same (~$32/GJ) at a larger scale of 180 kt/year hydrogen production. Our detailed analysis provides insights into the suitability of LSM as a carbon-neutral fuel that can compete economically with LH 2. • Direct air capture (DAC) and methanation processes were integrated. • At a scale of 1 MtCO 2 /year capture, the cost of DAC was estimated ~$114/tCO 2. • Costs of LSM and LH 2 were ~$32/GJ at a scale of 180 kt/year hydrogen production. • Energy efficiency of LH 2 production was higher than that of LSM production. [ABSTRACT FROM AUTHOR]

• Build a co-optimization model to optimize supply and demand sides of IES. • Modify stochastic programming to capture both demand and supply sides' scenarios. • Co-optimization model achieves more cost-efficient design considering carbon tax. • Basic and Standard demand-side upgrading are cost-efficient while Premium is not. • Carbon tax accelerates both renewable penetration and demand-side upgrading. Integrated energy systems (IES) can help achieve greater energy efficiency, and then ultimately promote a climate-neutral economy by utilizing local renewable resources. Demand-side energy-saving measures can reduce operational costs associated with energy usage. Most existing IES models, however, focus on supply-side optimization, while the demand-side energy-saving potential and its impacts on whole-system performance are still not clear. The increasing carbon tax makes it even more important to understand the interactions between supply and demand sides to achieve a sustainable system with a minimal carbon charge. Hence, this study proposes a co-optimization model to simultaneously optimize the supply and demand sides of an IES considering the impact of the carbon tax. A selection tree is developed to describe various demand-side envelope upgrading technologies, and a binary tree is established by generating a set of supply-side scenarios with corresponding probabilities. Based on these results, an improved two-stage stochastic programming model is proposed. The robustness of the modeling results was further validated by a simulation–optimization-based uncertainty analysis addressing price uncertainties. A case study in Shanghai indicates that the proposed co-optimization model achieves more cost-efficient solutions than supply-side-only optimization considering carbon tax. Introducing carbon tax can reduce the installed capacity of fuel-based energy technologies by up to 24% and greatly accelerate the penetration of renewables. The increasing carbon tax also promotes the adoption of more advanced energy-saving technologies. Uncertainty analysis reveals acceptable robustness of the optimal demand-side scheme and supply-side configuration with a deviation of less than 5% and a coefficient of variation of 7%. Overall, the observations of the proposed model and case study provide valuable insights for IES design considering an emerging charge of carbon tax. [ABSTRACT FROM AUTHOR]

• Model community-level energy by combining GIS, simulation, and data-driven approaches. • Propose a GIS-based urban building energy modeling method with low computational cost. • Generate various prototype buildings through two-stage clustering process to describe buildings' diversity. • Integrate urban building energy modeling with global sensitivity analysis to discovery urban planning factors. • Identify FAR and BCR as the most influential factors affecting communities' energy use. The residential sector accounts for an increasing amount of global energy use with continued urbanization. Residential energy-informed urban planning offers an economical and easy-to-operate approach to achieve more efficient urban energy utilization. However, quantifying the interactions between residential energy and urban planning remains an open challenge. This study proposes a holistic approach integrating GIS techniques, building energy modeling, and a global sensitivity analysis to prioritize eight key urban planning factors on the community energy performance based on a building energy dataset. The dataset, including urban planning and building information, was first established using GIS techniques and validated using survey data. The residential energy performance model at the community scale was developed using the clustering tree structure of residential building prototypes and building performance simulations. A combined data-driven and global sensitivity analysis approach was further applied to prioritize the impacts of eight vital urban planning factors on energy use intensity and peak load intensity. A case study of 1963 communities in Shanghai revealed that, for the energy performance of residential communities, the floor area ratio and building coverage ratio are the most influential factors, followed by the maximum height and high-rise proportion having a relatively low impact but higher than other factors. Overall, the proposed holistic approach generates robust insights into urban-scale residential energy performance, which can effectively inform urban planners to achieve more energy-efficient regulatory planning. [ABSTRACT FROM AUTHOR]