China’s renewable energy power has developed rapidly in recent years. Evaluating the external benefits of renewable energy power can provide a reference for the Chinese government to set diverse development goals and to implement differentiated supporting policies for different renewable energy power types, which can promote their sustainable development. In this paper, a hybrid MCDM method was applied to evaluate the external benefits of China’s renewable energy power. Firstly, the impacts of renewable energy power accessing the power grid for multiple stakeholders in the electric power system were analyzed. Secondly, the external benefit evaluation index system for renewable energy power was built from the economic, social and environmental factors, based on the concept of sustainability. Then, the basic theory of the hybrid MCDM method employed in this paper was introduced in two parts: the superiority linguistic ratings and entropy weighting method for index weight determination and the fuzzy grey relation analysis for ranking alternatives. Finally, the external benefits of wind power, solar PV power and biomass power were evaluated. Taking a regional electric power system as an example, the results show that PV power has the greatest external benefit, followed by wind power and biomass power. Therefore, more policies supporting PV power should be put in place to promote the harmonious and sustainable development of the whole renewable energy power industry.

Electricity market reform is in progress in China, and the operational performance of power grid enterprises are vital for its healthy and sustainable development in the current electricity market environment. In this paper, a hybrid multi-criteria decision-making (MCDM) framework for operational performance evaluation of a power grid enterprise is proposed from the perspective of sustainability. The latest MCDM method, namely the best-worst method (BWM) was employed to determine the weights of all criteria, and the technique for order preference by similarity to an ideal solution (TOPSIS) was applied to rank the operation performance of a power grid enterprise. The evaluation index system was built based on the concept of sustainability, which includes three criteria (namely economy, society, and environment) and seven sub-criteria. Four power grid enterprises were selected to perform the empirical analysis, and the results indicate that power grid enterprise A1 has the best operation performance. The proposed hybrid BWM-TOPSIS-based framework for operation performance evaluation of a power grid enterprise is effective and practical.

Accurate and reliable forecasting on annual electricity consumption will be valuable for social projectors and power grid operators. With the acceleration of electricity market reformation and the development of smart grid and the energy Internet, the modern electric power system is becoming increasingly complex in terms of structure and function. Therefore, electricity consumption forecasting has become a more difficult and challenging task. In this paper, a new hybrid electricity consumption forecasting method, namely grey model (1,1) (GM (1,1)), optimized by moth-flame optimization (MFO) algorithm with rolling mechanism (Rolling-MFO-GM (1,1)), was put forward. The parameters a and b of GM (1,1) were optimized by employing moth-flame optimization algorithm (MFO), which is the latest natured-inspired meta-heuristic algorithm proposed in 2015. Furthermore, the rolling mechanism was also introduced to improve the precision of prediction. The Inner Mongolia case discussion shows the superiority of proposed Rolling-MFO-GM (1,1) for annual electricity consumption prediction when compared with least square regression (LSR), GM (1,1), FOA (fruit fly optimization)-GM (1,1), MFO-GM (1,1), Rolling-LSR, Rolling-GM (1,1) and Rolling-FOA-GM (1,1). The grey forecasting model optimized by MFO with rolling mechanism can improve the forecasting performance of annual electricity consumption significantly.

Carbon dioxide (CO₂) emissions forecasting is becoming more important due to increasing climatic problems, which contributes to developing scientific climate policies and making reasonable energy plans. Considering that the influential factors of CO₂ emissions are multiplex and the relationships between factors and CO₂ emissions are complex and non-linear, a novel CO₂ forecasting model called SSA-LSSVM, which utilizes the Salp Swarm Algorithm (SSA) to optimize the two parameters of the least squares support sector machine (LSSVM) model, is proposed in this paper. The influential factors of CO₂ emissions, including the gross domestic product (GDP), population, energy consumption, economic structure, energy structure, urbanization rate, and energy intensity, are regarded as the input variables of the SSA-LSSVM model. The proposed model is verified to show a better forecasting performance compared with the selected models, including the single LSSVM model, the LSSVM model optimized by the particle swarm optimization algorithm (PSO-LSSVM), and the back propagation (BP) neural network model, on CO₂ emissions in China from 2014 to 2016. The comparative analysis indicates the SSA-LSSVM model is greatly superior and has the potential to improve the accuracy and reliability of CO₂ emissions forecasting. CO₂ emissions in China from 2017 to 2020 are forecast combined with the 13th Five-Year Plan for social, economic and energy development. The comparison of CO₂ emissions of China in 2020 shows that structural factors significantly affect CO₂ emission forecasting results. The average annual growth of CO₂ emissions slows down significantly due to a series of policies and actions taken by the Chinese government, which means China can keep the promise that greenhouse gas emissions will start to drop after 2030.

With the increasing development of renewable resources-based electricity generation and the construction of wind-photovoltaic-energy storage combination exemplary projects, the intermittent and fluctuating nature of renewable resources exert great challenges for the power grid to supply electricity reliably and stably. An energy storage system (ESS) is deemed to be the most valid solution to deal with these challenges. Considering the various types of ESSs, it is necessary to develop a comprehensive assessment framework for selecting appropriate energy storage techniques in establishing exemplary projects combining renewable resources-based electricity generation and an ESS. This paper proposes a multi-criteria decision making (MCDM) model combining a fuzzy-Delphi approach to establish the comprehensive assessment indicator system, the entropy weight determination method, and the best-worst method (BWM) to calculate weights of all sub-criteria, and a Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) comprehensive evaluation model to choose the optimal battery ESS. In accordance with the comprehensive evaluation results, the Li-ion battery is the optimal battery ESS to apply to wind-photovoltaic-energy storage combination exemplary projects. Based on the discussion on the comprehensive evaluation results, policy implications are suggested to improve the applicability of battery ESSs and provide some references for decision makers in related fields.