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Recently, clean solar energy has aroused wide attention due to its excellent potential for electricity production. A highly accurate prediction of photovoltaic power generation (PVPG) is the basis of the production and transmission of electricity. However, the current works neglect the regional correlation characteristics of PVPG and few studies propose an effective framework by incorporating prior knowledge for more physically reasonable results. In this work, a hybrid deep learning framework is proposed for simultaneously capturing the spatial correlations among different regions and temporal dependency patterns with various importance. The scientific theory and domain knowledge are incorporated into the deep learning model to make the predicted results possess physical reasonability. Subsequently, the theory-guided and attention-based CNN-LSTM (TG-A-CNN-LSTM) is constructed for PVPG prediction. In the training process, data mismatch and boundary constraint are incorporated into the loss function, and the positive constraint is utilized to restrict the output of the model. After receiving the parameters of the neural network, a TG-A-CNN-LSTM model, whose predicted results obey the physical law, is constructed. A real energy system in five regions is used to verify the accuracy of the proposed model. The predicted results indicate that TG-A-CNN-LSTM can achieve higher precision of PVPG prediction than other prediction models, with RMSE being 11.07, MAE being 4.98, and R2 being 0.94, respectively. Moreover, the performance of prediction models with sparse data is tested to illustrate the stability and robustness of TG-A-CNN-LSTM. 

Logistics planning is regarded as the most complex part of supply chain management for refined products. A vital knowledge gap still exists in understanding the trade-offs between the economy and the practicability of logistics schemes. Focus on this issue, this paper proposes a model-experience-driven method for the planning of refined product primary logistics. The method couples three sub-modules: (1) use coordinator's preference information and convex function interpolation to construct satisfaction indicator; (2) set up a multi-objective model for logistics coordination and optimization considering supply adjustment and secondary delivery; (3) adopt the augmented ɛ-constraint method to obtain the Pareto solutions and balance the economy and satisfaction indicators. The method is verified by a small-scale system, where the satisfaction degree increases by 77% while the logistics cost remains unchanged. The method is also successfully applied to a large-scale system with 29 refineries and 196 market depots, where Pareto logistics schemes are obtained and the supply–demand imbalance is greatly eased. The proposed method can help provide theoretical guidance for real-world logistics planning.

Offshore wind farms are increasingly becoming the focus of clean sources market because of the huge energy potential and fast-maturing technology. The existing researches normally optimize the wind turbine layout and two-dimensional cable routing independently. This work focuses on the synchronization optimization of site selection of the offshore wind farm, three-dimensional wind turbine layout and three-dimensional cable network routing based on meta-heuristic algorithms and geographic information systems. Several practical issues, i.e., restricted areas, power generation, cable network and energy loss, are taken into consideration. A two-layer model is proposed. The outer layer model is for the site selection and the wind turbine layout optimization. The inner layer model is for the obstacle-avoiding cable routing optimization. In this stage, the seabed terrain is considered for the first time. The proposed integrated model is complex and non-convex. Thus, a hybrid method including an improved ant colony optimization combined with genetic algorithm, dual-simplex method and Kruskal algorithm is proposed to search the solution more efficiently. The initialization stage of the hybrid method is improved from random assignment to directional assignment. The directional solution is obtained by the widely used genetic algorithm. A case study based on a real offshore wind farm is established to prove the effectiveness of the proposed methodology. The results show an over one million dollars increase in annual benefit compared with conventional methods.

The pipeline network integration reform enables unified management of pipelines from different entities. For refined oil logistics, this paper proposes a framework based on the MILP optimization model to quantify its flexibility. Considering the uncertainty, three disturbances occur in the logistics concurrently, and 10,000 simulations are performed to obtain the turnover cost. The ratio of pipeline transportation cost to the calculated average turnover cost is defined as the flexibility indicator. Taking China's largest refined oil pipeline network as an example, the results show that the flexibility rises 8.9% after the reform. The paper also quantifies the impact of the reform on logistics flexibility in South China, which is embodied in achieving lower freights and GHG emissions, lower impact by fluctuations, higher pipeline utilization, more efficient oil product turnover, and the avoiding of depot shortages when facing logistical disturbances. The underlying reasons for the results and 3E analysis are analyzed.

Air pollution reduction is one of the most straightforward co-benefits of PV development, but its mechanism is complex. In a recent One Earth paper, Chen et al. analyzed the respective effects of different factors on solar power performance and show that the co-benefits of air pollution control policies for PV over the past decade would be grossly underestimated. 

Lockdown measures have been a “panacea” for pandemic control but also a violent “poison” for economies.Lockdown policies strongly restrict human mobility but mobility reduce does harm to economics. Governmentsmeet a thorny problem in balancing the pros and cons of lockdown policies, but lack comprehensive andquantified guides. Based on millions of financial transaction records, and billions of mobility data, we trackedspatio-temporal business networks and human daily mobility, then proposed a high-resolution two-sidedframework to assess the epidemiological performance and economic damage of different lockdown policies. Wefound that the pandemic duration under the strictest lockdown is less about two months than that under thelightest lockdown, which makes the strictest lockdown characterize both epidemiologically and economicallyefficient. Moreover, based on the two-sided model, we explored the spatial lockdown strategy. We argue thatcutting off intercity commuting is significant in both epidemiological and economical aspects, and finally helpedgovernments figure out the Pareto optimal solution set of lockdown strategy.

As a newly-emerging option of shared transportation, Internet-enabled dockless bicycle sharing is well accepted by the public. The implementation of electric fences has great potential to tackle the problem of random parking in bicycle sharing services. However, the deployment of electric fences would have a negative impact on the convenience of bicycle sharing services, which might lead to an increase in energy consumption among customers who switch their methods of transportation. This paper proposes a dynamic electric fence planning method with an assessment of resource-saving and potential energy consumption increasement. An agent-based model is proposed to simulate the trips and evaluated the performance of static and dynamic electric fences. The results show that dynamic electric fences require significantly shorter walking distances than static electric fences. The implementation of electric fences in the city center can significantly avoid random parking and improve the parking tidiness of bicycles. The implementation of dynamic and static electric fences can averagely save 25.31% and 27.76% bicycle resources. By estimating travel mode shifting, dynamic electric fence can reduce energy consumption by 5.79% per day compared to the static electric fence situation. 

The logistics management of refined oil under a separation of production and transportation leads to high logistics costs and a mismatch between the supply and demand sides. This paper intends to develop a general framework to assess the impact of the integration of the production and transportation in terms of economic, environmental, and energy benefits. Firstly, this paper proposes a tactical-level mathematical model for optimizing the integration of production and transportation of refined oil to minimize the total cost. In the model, several factors, such as level of market demand, production capacity limits, transportation modes, and transportation capacity, are taken into consideration. Then, the energy, economy, and environment analysis method are applied to assess the impact of the integration on the field of refined oil logistics. Four scenarios are set up and a comparative analysis is carried out in detail in China. The optimal resource allocation scheme and production adjustment scheme for each scenario are obtained. The results show that after the integration, the logistics cost is reduced by 6.8 %− 11 %, the greenhouse gas emission is reduced by 7.3 %− 17.7 %, and the energy consumption per unit turnover is reduced by 4.4 %− 7.4 %. This proves that the integration of production and transportation guided by the proposed method performs positive economic, environmental, and energy benefits. Finally, policy implications are provided.

The integrated energy system which coordinates natural gas, renewable energy, and other energy subsystems is an effective way to promote a low-carbon economy. An effective framework for system assessment and optimisation is a critical issue. This paper takes a natural gas-wind-photovoltaic integrated energy system as the research object and uses the simulation software to analyse its techno-enviro-economic feasibility. Firstly, a mathematical model is customised to optimise the system installation and operation plans. Renewable electricity replaces some natural gas, resulting in pipeline pressure fluctuation. Here, the Stoner Pipeline Simulator software is used to simulate pipeline network operation to quantify the aforementioned pressure fluctuations. The proportion of renewable energy is gradually reduced until the network pressure fluctuation is less than 20% to ensure the stability of pipeline operation. Then, the optimal operation scheme can be determined. Taking three cities in Shandong, China, as cases, the results show that the proposed system is beneficial for urban energy internet development: (i) the total net present cost is reduced by 19.7%, 19.8%, and 20.8%, (ii) annual CO2 emission is reduced by 23.7%, 18.4%, and 12.2%; (iii) the levelised cost of energy is 0.142 $/kWh, 0.143$/kWh, and 0.153$/kWh. 

Electrolysis hydrogen generation technology is one of the feasible ways to alleviate the problem of wind electricity curtailment. One promising hydrogen value-added application is to blend hydrogen into the natural gas grid and sell it as the heat energy carrier. This paper aims to discuss the feasibility of a roadmap to urban energy internet with wind electricity-natural gas nexus. Firstly, a framework is raised to integrate wind electricity generation, electrolysis hydrogen generation, and hydrogen-natural gas blending systems. Secondly, a series of reasonable hydrogen supply profiles are provided based on annual electricity curtailment and realistic natural gas scheduling. Then, an energy optimisation model and a techno-economic model are applied to simulate the generation of electricity and hydrogen, as well as determine the most economical hydrogen supply scheme. Finally, a case study in the Beijing-Tianjin-Hebei region of China is taken to validate the benefits of the proposed roadmap. The preferred scheme is worked out with the net present value of 88.8 M$, including the economy configurations of the electricity-hydrogen hybrid generation system, as well as the hydrogen-natural gas blending plan. The results also indicate that annual electricity curtailment and annual carbon emission are decreased by 204 GWh (48.8%) and 40.2 kt (49.9%).