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Hydropower is the largest renewable energy power generation source with the largest construction scale and power generation capacity. A reasonable prediction of hydropower generation is conducive to achieving the carbon peak and neutrality goals. Hydropower generation is affected by seasons and precipitation, which have significant randomness and uncertainty. To realize a reasonable prediction of hydropower generation in China, this paper constructs a novel grey combination optimization model using different parameter combination optimizations based on the three-parameter discrete grey model TDGM(1,1). Further research shows that a better model performance can not necessarily achieved by a higher number of parameter combination optimizations, mainly due to the different effects and influence of various parameters on the model. Subsequently, the TDGM(1,1, r , ξ , Csz) model is applied to forecast China's hydropower generation. The results show that China's hydropower generation can reach 1687.738 hundred million kWh in 2025, an increase of 24.5% compared with 2020. Finally, the rationality of the prediction results is analyzed, and relevant countermeasures and suggestions are proposed. • The highlights of this paper are as follows: • A new grey model is proposed to forecast the hydropower generation in China. • This paper studies the optimization algorithm and implementation process of the new model. • It solves the problem of low accuracy of traditional model in China's hydropower prediction. • Finds can help government of China formulate reasonable power policies. [ABSTRACT FROM AUTHOR]

This paper construct a CCHP and renewable energy based hybrid energy system driven by distributed energy resources (DERs CCHP). Then, the paper constructs performance indexes from energy, economic and environment. Thirdly, the paper proposes a multi-objective optimization model for DERs CCHP system under four optimization of energy rate (ER), total operation cost (TOC), carbon dioxide emission reductions (CER) and joint optimization. Finally, Guangzhou Higher Education Mega Center (GHEMC) in China is taken as the object for comparatively analyzing the operation performance of DERs CCHP system and CCHP system driven by natural gas (NG CCHP system). Results show: Joint optimization mode could balance the results of different optimization modes. DERs CCHP system shows better operation performance. ER of ER optimization, CER optimization and joint optimization are higher than that of NG CCHP system. NPV of all optimization modes is positive, IRR are bigger than the expected yield rate. DERs CCHP system could reduce CO 2 emission by utilizing wind energy and solar energy to replace NG. The sensitivity analysis indicates the performance of the DERs CCHP system will become better with the increase of chiller COP, decrease of NG price and wind-photovoltaic equipment cost. [ABSTRACT FROM AUTHOR]

In this paper, a tramway's system model is established to evaluate its longitudinal dynamics performance, where the model includes multiple masses point connecting together powered by the catenary and a hybrid energy-storage system which consists of a battery pack and ultracapacitor pack. To explore an optimal or appropriate real-time power distribution among these energy sources, a dual fuzzy logic control-based energy management strategy combined with wavelet transform and multi-objective optimization is proposed. The control parameters of the dual fuzzy logic controller are adjusted using a multi-objective particle swarm optimization algorithm while considering the daily operating cost of the tramway. To verify the effectiveness and validity of the proposed model and energy management strategy, its simulation results are further compared and verified with the field experimental results of a real tramway and a real driving cycle in China. • A improved simulation model is presented for a multi-sources hybrid tramway. • A optimal strategy combined with fuzzy logic, wavelet transform and PSO is proposed. • The dual fuzzy logic controller is optimized by multi-objective PSO algorithm. • The strategy shows lower tramway daily operating cost and better energy efficiency. [ABSTRACT FROM AUTHOR]

To improve projection efficiency of future energy demand, this paper proposes a hybrid optimization method, which is Bat Algorithm, Gaussian Perturbations and Simulated Annealing Energy Demand Estimation (BAG-SA EDE) model. The proposed BAG-SA algorithm not only inherits the simplicity and efficiency of the standard BA with a capability of searching for global optimality, but also enhances local search ability and speeds up the global convergence rate. The causality of energy demand and the selected factors is further investigated via the Stationarity, Cointegration and Granger causality tests. Then, BAG-SA algorithm is employed to optimize the coefficients of multiple linear and quadratic equations of energy demand estimation models. Results indicate that the proposed algorithm has higher precision and reliability than other single optimization methods, such as Genetic Algorithm, Particle Swarm Optimization or Bat Algorithm, and the quadratic form of BAG-SA EDE model has better fitting ability compared with the multiple linear form of the model. Therefore, the quadratic form of the model is applied to estimate energy demand of China from 2016 to 2030 in dissimilar scenarios. The forecasting findings show that energy demand in 2030 will be 4.6, 6.1 and 7.9 billion tce in three scenarios. [ABSTRACT FROM AUTHOR]

An objective and accurate forecast of carbon emissions can provide the government with an important baseline for the implementation of the Green Economic Development Strategy. This paper considers the time-lag effect and the interaction effect of the influencing factors on carbon emissions simultaneously and establishes a new grey multivariate coupled model (CTGM(1,N)) for carbon emission projection by introducing the Choquet fuzzy integral and grey multivariate delay model. To further promote the prediction performance, the time-lag number of each influencing factor is determined by time-delay grey correlation analysis, and the whale optimization algorithm is designed to acquire the optimal parameters and accuracy of the model. The new model is designed to fitting carbon emissions data in three countries and compare it to six reference models. The performance test shows that the CTGM(1,N) model has high stability. The results of the forecasts show that China's carbon emissions are expected to rise by 1.17% by 2025 from 2020 levels. Meanwhile, emissions will decrease by 5.08% (US) and 0.88% (Japan). The prediction results were consistent with the development status of the three countries. According to the results, we can grasp the development trend of carbon emissions and formulate targeted strategies to achieve sustainable development. [Display omitted] • A new prediction mechanism is developed by complex effects. • A grey multivariate coupled time-delay model is proposed. • The model parameters are optimized by the whale optimization algorithm. • Comparative study shows that new model is superior to other various models. [ABSTRACT FROM AUTHOR]

Abstract: In China, the energy pricing mechanism has an insufficient linkage with other energy prices. As a result of the unreasonable price level, it is impossible to exploit fully the substitution elasticity among energy resources and there is a negative impact on achieving energy conservation and energy efficiency. This paper proposes an optimized mechanism for residential energy prices in China, which maximizes the total social surplus subject to some related constraints. Three types of energy pricing mechanisms are designed based on China's low-carbon targets and the optimization of residential energy price policies through the dynamic CGE model. Compared with the energy price linkage method, the results show that the market netback value mechanism has a greater impact on the total social surplus. In order to achieve further low-carbon targets, the proportion of second and third tier residents can be expanded, while the energy prices could be deregulated to some degree. In addition, considering residential affordability, the government may take into account different electricity pricing mechanisms for different tiers of residents. Electricity pricing for the first tier, the second tier and the third tier should be based respectively on cost, the integration of energy price linkage and the market netback value mechanism. [Copyright &y& Elsevier]

To improve the economics and scheduling flexibility of regionally integrated energy system (RIES) with large-scale centralized renewable energy, a bi-level robust game model is proposed. The model relies on the CCHP units and virtual energy-plant, uses their complementary of various energy to stabilize the uncertainty of power generation and consumption. The revenue and loss risk for coupling multiple uncertain is considered, and the uncertain set is constructed by robust theory, which can be flexibly adjusted its boundaries. Aim at the above-mentioned complex model, such as multi-objective, nonconvex, strong constraint, large-scale decision variables, and irregular Pareto front shape, a multi-objective whale optimization algorithm is proposed. Finally, a modified IEEE-33 bus distribution system and the Hami power grid (in Western China) are used for cases. The sensitivity for scheduling results is quantitatively analyzed, under the influence of power generation and consumption uncertainty. The results show that the strategy can balance the economy and robustness, improve the profit benefit of RIES by 15.67% with the traditional robustness, virtual energy-plant can increase the profit by 1.9%. This paper provides theoretical guidance for the optimal scheduling of RIES with large-scale renewable energy. • Bi-level robust game scheduling model for RIES with large-scale renewable-energy sources is proposed. • Improve the capacity of grid-connected for large-scale renewable energy. • Balance economic and robustness of system scientifically. • The proposed algorithm can solve the large-scale irregular-frontier multi-objective problems. [ABSTRACT FROM AUTHOR]