Search

The motivation of this paper is to investigate the resistance of countries' energy research and development (RD&D) expenditures to random shocks. The analysis includes the five countries (France, Germany, Japan, the United States, and the United Kingdom) that are the biggest investors in RD&D in fossil fuels, renewables, energy efficiency, and nuclear energy. Thus, economies will be able to take precautions against global shocks in producing sustainable energy policies. To achieve this aim, this paper uses the artificial neural network approach, which uses a distributed computing model that can store and generalize data after a learning period. Empirical results widely depend on countries and distinct energy technology policy fields. The key findings provide evidence that RD&D expenditures, except for renewables, do not tend to mean revert. The economies of Japan, Germany, and the United States should make more renewable investments to reduce the resistance of renewable energy sources to shocks and thus produce policies for both environmental sustainability and energy security. The economies of France and the United Kingdom can ensure energy security by continuing their sustainable energy policies. • Applied SDG policies focused on energy technology RD&D are presented. • The stochastic characteristics of energy technology investments are examined. • Carbon-reducing policies are recommended for the top five economies. • Energy technologies are perused through artificial intelligence approach. [ABSTRACT FROM AUTHOR]

Recently, with the development of new energy technologies, all-electric ships (AESs) with hybrid energy storage system (HESS) are becoming a promising solution to reduce fuel consumption and emissions. However, the high maneuverability of ships during the actual navigation places higher performance requirements on the HESS, which presents a nonlinear and multi-objective challenge for the HESS design. Therefore, it is necessary to consider the coupling between HESS sizing and energy management strategy (EMS). In this paper, a self-adaptive joint optimization framework (SJOF) for marine HESS design considering load fluctuation characteristics is proposed, which can find the optimal decision solution with excellent system economic and battery life performance for AES HESS design. Based on the rain flow counting (RFC) method, a multi-objective joint optimization method considering life cycle cost (LCC) and battery degradation index (BDI) is introduced into SJOF. Besides, a novel EMS including the self-adaptive segmentation mechanism (SSM) and power allocation is proposed, which can achieve the most efficient energy scheduling. • A self-adaptive working condition classification approach is proposed. • A power allocation strategy with optimal filter matching is proposed. • A reconstruction method for obtaining optimal solution set is proposed. [ABSTRACT FROM AUTHOR]

The rapid development of renewable energy sources (RESs) facilitates the coordinated operation of different energy sources to hedge against the uncertain and non-dispatchable nature of RESs. In this paper, we propose an effective approach for ultra-short-term optimal operation of a photovoltaic-energy storage hybrid generation system (PV-ES HGS) under forecast uncertainty. First, a generic approach for modelling forecast uncertainty is designed to capture PV output characteristics in the form of scenarios. Then, stochastic model predictive control (SMPC) is utilized in the coordinated operation of PV-ES HGS for load shifting under forecast uncertainty. Finally, a PV-ES power station located in Northeast China is selected as a case study to validate the feasibility and effectiveness of the proposed method in detail. The results show that: (1) The forecast uncertainty modelling method, which incorporates adaptive kernel density estimation (KDE) and two-layer nested copula, could capture the characteristics of PV output effectively. (2) The proposed sampling procedure ensures the representativeness and temporal dependence of scenarios extracted from the joint predictive density, leading to stability and efficiency in solving the stochastic optimization problems. (3) The SMPC-based coordinated operation strategy exhibits significant superiority in load shifting performance and resistance to risks. The standard deviation of residual load (SDORL) is reduced by 17.5% on average compared to the conventional scheme. Thus, this work provides operators with guidance in the ultra-short-term optimal operation of a PV-ES HGS under forecast uncertainty. [Display omitted] • Integration of adaptive KDE and two-layer copula to model PV forecast uncertainty. • Proposal of a representative scenario generation method incorporating LH and CD. • Utilization of scenario-based SMPC to optimize ultra-short-term PV-ES HGS operation. • Identification of trade-off between load shifting performance and operational risks. [ABSTRACT FROM AUTHOR]

With the increasing penetration of electric vehicles into the existing power networks, the development of proper energy management tools become essential to mitigate the damaging effects of emerging high-power charging modes. To facilitate the management of vehicles, they can be gathered into clusters in order to aggregate their charging profiles. This paradigm is called cluster of vehicles and can be forward developed by gathering a group of parking, thus forming a parking cluster where the characteristics of different parking lots are aggregated. In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The new proposal is casted as a game-based framework which seeks for the equilibrium between the cluster coordinator and parking lots, instead of conventional tools where the pricing strategy is decided by a central entity disregarding the interests of users. The developed model is established as a bi-level optimization problem, which is further linearized to be tractable and solvable by off-the-shelf solvers. The implications of vehicle-to-grid are also studied by fully modelling bi-directional power flows in chargers. In addition, two uncertainty-aware strategies are proposed to cope with uncertain energy margins in parking lots. A case study is conducted and various results are analysed and discussed. It is shown that enabling vehicle-to-grid characteristics discloses significant economic benefits for users and the cluster coordinator. In addition, vehicle-to-grid impacts notably on the risk-averse character of the uncertainty-aware strategies proposed. The developed pricing mechanism is also compared with the conventional pricing policies based on key of repartitions, showing that the new proposal is able to reduce the cost for users, avoiding to directly translate the energy cost to charging points. • A new game-based pricing mechanism for parking clusters is developed. • Two uncertainty-aware strategies are proposed and compared. • Vehicle-to-grid helps to improve the economy of the cluster notably. • The impact of uncertainties strongly depends on vehicle-to-grid availability. • The new proposal reduces the cost of users compared with other approaches. [ABSTRACT FROM AUTHOR]