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Hybrid natural gas combined cooling, heating, and power (CCHP) systems integrated with solar technologies offer the efficient use of distributed energy resources for reducing the use of fossil fuels and greenhouse gas emissions. This paper proposes a novel CCHP system, using the phosphoric acid fuel cell (PAFC) as the prime mover, integrated with the natural gas reforming with solar energy assistance. Their respective thermodynamic models are constructed for simulation of the system thermodynamic performance. The simulation results demonstrate that the energy efficiencies of the hybrid system with solar energy assistance are 55.12% for the heater mode and 66.77% for the chiller mode, while the exergy efficiencies are 32.87% and 31.59%, respectively. Similarly, when solar energy is not available, its energy efficiencies are 59.24% and 69.18%, while its exergy efficiencies are 40.09% and 39.01%, respectively. In the PAFC–CCHP hybrid system, the exergy efficiencies are key components in the overall efficiency, and the efficiencies with and without solar energy are 29.34% and 36.95%, respectively. Solar energy and exergy share is employed to analyze the solar energy contribution, and they are 36.1% and 35.6%, respectively. Meanwhile, the parametric analysis is also carried out to evaluate the effects of key parameters on the performance. The results show that increasing the reforming temperature, influenced by solar energy, improves the natural gas conversion and boosts the hydrogen concentration of the syngas fed into the PAFC, which increases the overall exergy efficiency. However, the effect of the reforming temperature is reduced when the reforming temperature is higher than 1073.15 K.

The integrated energy system (IES) can effectively utilize the distributed renewable energies, but its performance improvement requires advanced design and optimization methods. This study proposes a new IES based on biomass gasification integrated with an internal combustion engine, photovoltaic and solar collectors, an absorption chiller/heater, a heat pump and a thermal storage unit to utilize renewable energies effectively. Based on the thermodynamic models of components considering off-design conditions, a multi-objective optimization model of biomass IES is established to improve the comprehensive performances of energy, economy, and environment. A decision method is proposed to select the optimal configuration considering the Euclidean distance between the Pareto solution and the ideal solution. The results demonstrate that in the specific case, the capacity of the internal combustion engine with biomass gasification is preferred to be low, and 71.4% of Pareto solutions is less than 200 kW during the set range from 20 to 1,000 kW. The larger capacity of solar photovoltaic panels achieves more benefits than solar heat collectors. Moreover, the optimal IES determined by the minimum Euclidean distance has primary energy utilization efficiency of 43.4% and exergy efficiency of 18.0%, which saves primary energy of 7.1%, the annual total cost of 7.2%, and reduces carbon dioxide emission by 41.5%.

A thermoelectric coupling limitation imposes the combined cooling, heating, and power (CCHP) systems low performance under dynamic uncertainty. Energy storage is an effective way to address the problem. This paper integrates an energy storage device into the CCHP system to decouple the limitation and proposes a multi-objective optimization model considering load uncertainty for the optimal capacity of energy storage. The design space is a set of all feasible configuration schemes that meet the requirements of the system’s confidence level. A chance-constrained method is employed to address the load uncertainty and transform the uncertain optimization into deterministic optimization. Load uncertainty is closely related to the given confidence level. Therefore, the indicator loss of load expectation is used to evaluate the reliability of the CCHP system. The economic and environmental performance of the CCHP system is assessed by using the annual total cost and carbon dioxide emission, respectively. A hotel example validates the feasibility of the proposed methodology. The results show that increasing the confidence level from 0.5 to 0.99 results in the capacity increase of the waste heat boiler from 326.8 kW to 408.2 kW in the heating mode. The increased change reduces the loss of load expectation by 108.4% and increases the annual operation cost by 110.14%.

Combined cooling, heating and power (CCHP) system integrated with multiple renewable energies can improve the environmental performance while increasing the dependency on the national grid due to the multiple uncertainties. This paper proposes a multi-objective stochastic multi-scenario optimization method for the optimal capacity of a CCHP system integrated wind, solar and geothermal energy considering its energy supply independence, environmental impact, economic performance and energy efficiency. The spatiotemporal multiple uncertainties in wind velocity, solar irradiation and multiple loads are characterized by the stochastic multi-scenarios generated by the stochastic hierarchy scenario-generation method. The hybrid system's flexibility is evaluated by both the grid integration level and net interaction level. The environmental performance is assessed by both the carbon emission reduction rate and renewable energy penetration. The economic and energy performances are characterized by the annual cost-saving rate and primary energy-saving rate, respectively. The multi-objective stochastic optimal design method is formulated and solved using non-dominated sorting genetic algorithm II. The case study results show that there are slight deviations of objectives between the stochastic multi-scenario and traditional optimizations, while the former can save 58.69% computation time. Moreover, the installed capacity increase of the electrical energy storage to 870kWh can reduce the system's dependency on the national grid and the net interaction level to 3.74% while deteriorating the economic performance and dropping the annual cost-saving rate to −217.37%. Inversely, the installed capacity increase of renewable energy generators can enhance economic and environmental performances while worsening the net interaction level.

As wind power accounts for an increasing proportion of the electricity market, the wind speed prediction plays a vital role in the stable operation of the power grid. However, owing to the stochastic nature of wind speed, predicting wind speeds accurately is difficult. Aims at this challenge, a new short-term wind speed prediction model based on double decomposition, error correction strategy and deep learning algorithm is proposed. The complete ensemble empirical mode decomposition with adaptive noise and variational mode decomposition are applied to decompose the original wind speed series and error series, respectively. The deep learning algorithm based on long short term memory neural network, is utilized to detect the long-term and short-term memory characteristics and build the suitable prediction model for each sub-series. In the four real forecasting cases, nine models were built to compare the performance of the proposed model. The experimental results show that the proposed model performs better than all other considered models without double decomposition, and the variational mode decomposition for error series can improve the effect of error correction strategy.

The effective planning of energy stations and networks is critical for improving the economic and energy performance of regional integrated energy systems. Based on the principle of minimizing investment cost and network losses, this study proposes a novel methodology involving both kernel density estimation and shortest path methods to optimize the transmission network. The optimal locations of energy supply and storage stations are determined according to the kernel density of the annual energy consumption of building groups and the annual energy output of energy stations in the kernel density estimation method, respectively. The shortest path method using the Astar search algorithm with a screening algorithm is used to optimize the distribution of energy networks. The proposed methodology is verified by a hypothetical region from literature. The modified energy station and network planning methodology based on kernel density hotspots can reduce the total network length by 21.3%. Moreover, the proposed methodology can be applied easily and effectively to select the locations of the energy stations and determine the distribution of the energy networks in a regional integrated energy system.

Solar radiation intensity significantly influences the cooling loads of building, and the two are correlated and accorded to a certain extent. This study proposes a double effect LiBr–H2O absorption cooling system based on the parabolic trough collector (PTC) of solar heat energy. Thermodynamic models including PTC and absorption chiller are constructed, and their accuracy is verified by comparing the simulation results and the experimental data. Subsequently, the impact of variable design parameters on the thermodynamic performance is analyzed and discussed. The analysis of a solar cooling system in a hotel case study is related to its operation in a typical day, the average coefficient of performance of the absorption chiller is approximately 1.195, and the whole solar cooling system achieves 61.98% solar energy utilization efficiency. Furthermore, the performance comparison of a solar cooling system in different types of building indicates that higher matching and a higher correlation coefficient between the transient solar direct normal irradiance and cooling load is helpful in decreasing the heat loss and improving systemic performance. The solar cooling system in the office building exhibits a correlation coefficient of approximately 0.81 and achieves 69.47% systemic thermal efficiency.