The combined cooling, heating, and power (CCHP) system, which is a sustainable distributed energy system, has attracted increasing attention due to the associated economic, environmental, and energy benefits. Currently, the enforcement of carbon emission regulations has become an increasingly concerning issue globally. In this paper, a multi-objective optimization model is established to evaluate the CCHP system under two different carbon emission regulation policies in terms of economic benefit, environmental sustainability, and energy advantage. A nonlinear programming optimization model is formulated and solved by using the particle swarm optimization (PSO) algorithm. The results from the case studies demonstrate that when considering carbon tax regulation, the cost savings of the optimal CCHP system strategy were on average 10.0%, 9.1%, 17.0%, 22.1%, and 20.9% for the office, supermarket, hotel, school, and hospital in China, respectively, compared with the conventional energy supply system. On the other hand, when considering carbon trading regulation, the optimal CCHP system strategy can lead to a 10.0%, 8.9%, 16.8%, 21.6%, and 20.5% cost-saving for the five different building categories, respectively. Furthermore, the optimal CCHP system strategy for the five buildings, i.e., an average of 39.6% carbon dioxide emission (CDE) reduction and 26.5% primary energy consumption (PEC) saving, can be achieved under carbon emission regulations.

Electric vehicles (EVs) have obtained increasing public interest due to the associated economic and environmental benefits. Recently, studies regarding the economic advantages of adopting EVs as energy storages for commercial/residential buildings are emerging. In fact, according to the U.S. Energy Information Administration, the industrial sector consumes more energy than all of the other sectors combined, which is about 54% of the world’s total delivered energy. The energy consumption pattern in manufacturing facilities is based on production schedules and the heat transfer between machines and the ambient surroundings, thus, differs greatly from commercial/residential buildings. However, little research attention has been given to analyse the synergies of integrating EVs and manufacturing facilities to improve energy efficiency. To fill this research gap, in this study, a comprehensive model is established to evaluate the economic and environmental performance of an energy sharing system that consists of the EVs, power grid, and manufacturing facilities (EPM) under Time-of-Use (TOU) electricity tariff. The model is formulated as a mixed integer nonlinear programming format by considering practical production schedules, heat exchange between machines and ambient surroundings, as well as the heating, ventilation, and air conditioning (HVAC) system. The case study results indicate that the presented EPM energy sharing system has great potential to reduce energy cost and CO2 emissions. In addition, compared to the results from winter scenarios, it is shown that more cost savings can be achieved in summer days.

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Building carbon emission prediction plays an irreplaceable role in low-carbon economy development, public health protection and environmental sustainability. It is significant to identify influential factors mainly contributed to building emission and predict emission accurately in order to harness the growth from the source. In this paper, 11 influencing factors of building carbon emission are identified and a support vector regression (SVR) prediction model is proposed to forecast building carbon emission considering improvement the prediction accuracy, generalization, and robustness. In the SVR model, parameters are optimized by particle swarm optimization (PSO) algorithm with the aim to improve performance. Cases in Shanghai’s building sector are adopted to demonstrate practical applications of the proposed PSO-SVR prediction model. The results indicate that the presented prediction system has an outstanding performance in forecasting building carbon emission under multi-criteria evaluation. Furthermore, compared to the results from other four prediction models (e.g., linear regression, decision tree), it is shown that PSO-SVR model can achieve higher accuracy (e.g., improvement average of 1.01% R2 under training subset), better generalization (e.g., improvement average of 19.89% R2 under testing subset), and better robustness (e.g., improvement average of 18.93% R2 under different levels of noise intensity).

Microgrids allow energy exchange among multiple interconnected microgrids for greater energy efficiency and collective economic interest. However, in some cases, the benefit of some microgrids within the network may not be uncertain. In view of the increasing development of electric vehicles (EVs), a multiobjective model is proposed to improve the performance of microgrids by integrating electric vehicles-to-grid (V2G) and vehicles-to-building (V2B) based on global and individual benefit balance. Two reference models are built to verify the validity of the proposed method, and models are formulated as mixed integer linear programming formats solved by the weighting method. A set of parameters of microgrids are adopted to model the driver behaviors (e.g., available hours of EV), energy transactions (e.g., electricity), performance factor (e.g., emission factor), distributed energy (e.g., solar panel), and energy load of five commercial buildings (e.g., hotel) located in Shanghai. Simulation results demonstrate the effectiveness of the operation decision models in the energy management of microgrids under neutral, proeconomic, proenvironmental, and proenergy weighting scenarios. The case study results specify that the proposed method can achieve operational cost, CO2 emission, and primary energy consumption reductions for each microgrid, with total benefits declining slightly.

Moral hazard have a non-negligible impact on supply chain sustainability, especially from a long-term perspective. This influence is more complicated in a dual-channel supply chain with free riding. Therefore, it is necessary to explore how manufacturers design multi-period incentive strategies in a dual-channel supply chain to deal with moral hazard problems from retailers. In this study, we built a game theory model that contains a retailer (she) who is delegated by a manufacturer (he) to sell products in her offline and online channels and to provide experience services in a physical store. The retailer has the option of exerting effort when providing experience services to boost demand. We explored and compared the manufacturer’s strategies that cover a time horizon of multiple periods under two circumstances: full information and repeated moral hazard. The following conclusions were drawn from this study. In the repeated moral hazard game, the incentive constraints of the retailer are only related to her current and the next-period profits and independent from the profits in other periods. Moreover, the incentive strategies in each period are affected by the historical information in the previous period, while the strategies under information symmetry are not affected by history. Specially, the manufacturer can induce effort by charging an up-front payment from the retailer in the previous period and then returning a utility based on the achieved demand. Therefore, the manufacturer can postpone the payment of incentive costs and shift the risk to the next period. Furthermore, the manufacturer’s incentive strategies are also affected by the free-riding effect between channels. That is, compared with the low-state transfer payment, the high-state transfer payment was found to be more sensitive to free riding.

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