ALBERT

Description: With the rapid development of China’s economy, the environmental problems are becoming increasingly prominent, especially the PM2.5 (particulate matter with diameter smaller than 2.5 μm) concentrations that have exerted adverse influences on human health. Considering the fact that PM2.5 concentrations are mainly caused by anthropogenic activities, this paper selected economic growth, economic structure, urbanization, and the number of civil vehicles as the primary factors and then explored the nexus between those variables and PM2.5 concentrations by employing a panel data model for 31 Chinese provinces. The estimated model showed that: (1) the coefficients of the variables for provinces located in North, Central, and East China were larger than that of other provinces; (2) GDP per capita made the largest contribution to PM2.5 concentrations, while the number of civil vehicles made the least contribution; and (3) the higher the development level of a factor, the greater the contribution it makes to PM2.5 concentrations. It was also found that a bi-directional Granger causal nexus exists between PM2.5 concentrations and economic progress as well as between PM2.5 concentrations and the urbanization process for all provinces. Policy recommendations were finally obtained through empirical discussions, which include that provincial governments should adjust the economic and industrial development patterns, restrict immigration to intensive urban areas, decrease the successful proportion of vehicle licenses, and promote electric vehicles as a substitute to petrol vehicles.

Description: 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.

Description: Analysis of urban saturated power loads is helpful to coordinate urban power grid construction and economic social development. There are two different kinds of forecasting models: the logistic curve model focuses on the growth law of the data itself, while the multi-dimensional forecasting model considers several influencing factors as the input variables. To improve forecasting performance, a novel combined forecasting model for saturated power load analysis was proposed in this paper, which combined the above two models. Meanwhile, the weights of these two models in the combined forecasting model were optimized by employing a fruit fly optimization algorithm. Using Hubei Province as the example, the effectiveness of the proposed combined forecasting model was verified, demonstrating a higher forecasting accuracy. The analysis result shows that the power load of Hubei Province will reach saturation in 2039, and the annual maximum power load will reach about 78,630 MW. The results obtained from this proposed hybrid urban saturated power load analysis model can serve as a reference for sustainable development for urban power grids, regional economies, and society at large.

Description: Electric universal service aims to improve the individual living quality in remote and underdeveloped areas, the implementation of which can promote the sustainable development of people’s lives and the local economy to some extent. A social welfare evaluation model of electric universal service was proposed, which was divided into economic and non-economic welfare evaluation models. After the factors influencing the social welfare sense of electric universal service were identified from the perspective of sustainability, the economic welfare evaluation model was built based on Atkinson’s social welfare function, and the non-economic welfare evaluation model was constructed based on the ideological connotation of prospect theory. Taking Yunnan province in China as the example, a social welfare evaluation of electric universal service was performed. The result shows that the social welfare of electric universal service for Yunnan rural areas has increased by 9.79% from 2006 to 2011, and the “electric service realization degree” is the most sensitive indicator.

Description: Sustainability, Vol. 10, Pages 2130: Comprehensive Performance Evaluation of Electricity Grid Corporations Employing a Novel MCDM Model Sustainability doi: 10.3390/su10072130 Authors: Haoran Zhao Huiru Zhao Sen Guo Under the new round reform of electricity market in China, a large amount of electricity sales companies has emerged in some provinces, and the reform of transmission and distribution tariffs is also in progress. Electricity grid corporations are required to update their operational strategies and improve comprehensive performance to adapt to the fierce competition in the electricity market. Considering this, a novel MCDM (multi-criteria decision making) model integrating Fuzzy-Delphi, the best-worst method (BWM), the entropy weight calculation approach, and the VIKOR method is established in this investigation to assess the comprehensive performances of five selected provincial electricity grid corporations. The comprehensive performance assessment indicator system is constructed in accordance with Fuzzy-Delphi approach, composed of 21 significant sub-criteria from the aspects of profitability capacity, development capacity, safety production capacity, electricity supply reliability, outstanding service provision, energy conservation, and environmental protection. The sub-criteria weights are computed by combining subjective weights determined by BWM and objective weights computed by the entropy weight calculation approach. The comprehensive performance evaluation model is established based on VIKOR. As the electricity grid corporation A is superior in profitability capacity (especially in electricity sales amount) and safety production capacity criterion, it is superior over other four electricity grid corporations. The established novel MCDM is practical and rational, which is applicable for electricity grid corporations’ comprehensive performance evaluation.

Description: Sustainability, Vol. 10, Pages 3168: Operational Efficiency of Chinese Provincial Electricity Grid Enterprises: An Evaluation Employing a Three-Stage Data Envelopment Analysis (DEA) Model Sustainability doi: 10.3390/su10093168 Authors: Haoran Zhao Huiru Zhao Sen Guo With the implementation of new round electricity system reform in China, the provincial electricity grid enterprises (EGEs) of China should focus on improving their operational efficiency to adapt to the increasingly fierce market competition and satisfy the requirements of the electricity industry reform. Therefore, it is essential to conduct operational efficiency evaluation on provincial EGEs. While considering the influences of exterior environmental variables on the operational efficiency of provincial EGEs, a three-stage data envelopment analysis (DEA) methodology is first utilized to accurately assess the real operational efficiency of provincial EGEs excluding the exterior environmental values and statistical noise. The three-stage DEA model takes the amount of employees, the fixed assets investment, the 110 kV and below distribution line length, and the 110 kV and below transformer capacity as input variables and the electricity sales amount, the amount of consumers, and the line loss rate as output variables. The regression results of the stochastic frontier analysis model indicate that the operational efficiencies of provincial EGEs are significantly affected by exterior environmental variables. Results of the three-stage DEA model imply that the exterior environmental values and statistical noise result in the overestimation of operational efficiency of provincial EGEs, and the exclusion of exterior environmental values and statistical noise has provincial-EGE-specific influences. Furthermore, 26 provincial EGEs are divided into four categories to better understand the differences of operational efficiencies before and after the exclusion of exterior environmental values and statistical noise.

Description: 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.

Description: Sustainability, Vol. 10, Pages 1568: A Multi-Stage Intelligent Model for Electricity Price Prediction Based on the Beveridge–Nelson Disintegration Approach Sustainability doi: 10.3390/su10051568 Authors: Haoran Zhao Sen Guo Huiru Zhao Accurate electricity price prediction is key to the orderly operation of the electricity market. However, the uncertain, stochastic and fluctuant characteristics of electricity pricees make prediction difficult. With the aim of solving this issue, this investigation proposed a multi-stage intelligent model integrating the Beveridge–Nelson decomposition (B-N-D) model, the least square support vector machine (LSSVM), and a nature-inspired optimization model named the whale optimization algorithm (WOA). Firstly, the B-N-D model was utilized to decompose the hourly electricity price time series into determinacy component, periodic trend, and stochastic item. Secondly, the WOA–LSSVM model was proposed to forecast the future hourly data of three components respectively, of which the optimal parameters of LSSVM were determined by using WOA. Finally, the future hourly electricity price data were computed by multiplying the forecasted data of those terms. To verify the validity of the proposed electricity price prediction model in this paper, five comparison approaches based on the B-N-D approach were selected, which are auto-regressive integrated moving average (ARIMA), single LSSVM, LSSVM optimized by the fruit-fly optimization algorithm (FOA), LSSVM optimized by particle swarm optimization (PSO) models, and WOA–LSSVM without B-N-D. By comparatively analyzing the error criteria values of the above models through testing on the objective data of the Pennsylvania–New Jersey–Maryland (PJM) electricity market collected from 11 December 2017 to 18 December 2017, from 15 January 2018 to 22 January 2018, and from 1 February 2018 to 25 February 2018, we conclude that the constructed intelligent model in this paper can greatly enhance the prediction precision of electricity prices.

Description: Accurate and reliable forecasting on energy-related carbon dioxide (CO2) emissions is of great significance for climate policy decision making and energy planning. Due to the complicated nonlinear relationships of CO2 emissions with its driving forces, the accurate forecasting for CO2 emissions is a tedious work, which is an important issue worth studying. In this study, a novel CO2 emissions prediction method is proposed which employs the latest nature-enlightened optimization method, named the Whale optimization algorithm (WOA), to search the optimized values of two parameters of LSSVM (least squares support vector machine), namely the WOA-LSSVM model. Meanwhile, the driving forces of CO2 emissions including GDP (gross domestic product), energy consumption and population are chosen to be the import variables of the proposed WOA-LSSVM method. Taking China’s CO2 emissions as an instance, the effectiveness of WOA-LSSVM-based CO2 emissions forecasting is verified. The comparative analysis results indicate that the WOA-LSSVM model is significantly superior to other selected models, namely FOA (fruit fly optimization algorithm)-LSSVM, LSSVM, and OLS (ordinary least square) models in terms of CO2 emissions forecasting. The proposed WOA-LSSVM model has the potential to effectively improve the accuracy of CO2 emissions forecasting. Meanwhile, as a new nature-enlightened heuristic optimization algorithm, the WOA has the prospect for wide application.

Description: Sustainability, Vol. 10, Pages 2574: Characterizing the Influences of Economic Development, Energy Consumption, Urbanization, Industrialization, and Vehicles Amount on PM2.5 Concentrations of China Sustainability doi: 10.3390/su10072574 Authors: Haoran Zhao Sen Guo Huiru Zhao The speeding-up of economic development and industrialization processes in China have brought about serious atmospheric pollution issues, especially in terms of particulate matter harmful to health. However, impact mechanisms of socio-economic forces on PM2.5 (the particle matter with diameter less than 2.5 μm) have rarely been further investigated. This paper selected GDP (gross domestic product) per capita, energy consumption, urbanization process, industrialization structure, and the amount of possession of civil vehicles as the significant factors, and researched the relationship between these factors and PM2.5 concentrations from 1998 to 2016, employing auto-regressive distributed lag (ARDL) methodology and environmental Kuznets curve (EKC) theory. Empirical results illustrated that a long-term equilibrium nexus exists among these variables. Granger causality results indicate that bi-directional causality exist between PM2.5 concentrations and GDP per capita, the squared component of GDP per capita, energy consumption and urbanization process. An inverse U-shape nexus exists between PM2.5 concentrations and GDP per capita. When the real GDP per capita reaches 5942.44 dollars, PM2.5 concentrations achieve the peak. Results indicate that Chinese governments should explore a novel pathway to resolve the close relationship between socio-economic factors and PM2.5, such as accelerating the adjustment of economic development mode, converting the critical industrial development driving forces, and adjusting the economic structure.