Energy Development Plan Research Articles

Model and Feature Analysis of Energy Storage Participating in Interaction

The “the 13th Five-Year Plan for Energy Development” pointed out that it is necessary to actively carry out the construction of energy storage demonstration projects and promote coordinated operation of energy storage systems with new energy sources and power systems. The future prospects for energy storage development are considerable. Therefore, how to make full use of energy storage advantages to promote power system development is an important research topic. Based on the flexible and interactive characteristics of energy storage, this paper first studied the principle of energy storage equipment participating in the grid interaction. Then, taking the micro-grid system composed of “source-load-storage” under the condition of not being connected to the grid as an example, the specific modes and interactive features of the energy storage involved in the interaction under the system are studied.

Material dependence of national energy development plans: The case for Turkey and United Kingdom

Due to growing production and consumption worldwide, the energy demand is increasing rapidly, which puts additional burdens on the world's scarce natural resources. Therefore, there is a need for efficient use of scarce materials of the earth to meet the increasing energy demand. With this motivation, material footprints of Turkey's and UK's national energy development plans are investigated by applying a global, multiregional input-output (GMRIO) model. A spatial material footprint analysis is conducted for 10 metallic and 9 nonmetallic minerals to reveal the regional and global material dependence of Turkey and UK related to electricity production from 11 different sources. As a high-resolution GMRIO database, the EXIOBASE v.2 is extended by material extraction data that enabled us to calculate the material footprints tracing the complex global supply chains of electric power generation sectors. The Autoregressive Integrated Moving Average (ARIMA) model is also developed to forecast the material footprints of electricity production until 2050. Three energy development plans such as Business-as-Usual (BAU), Official Plan (OP), and Renewable Energy development (RED) plan are investigated to compare the materials dependence of different national energy development policies. Current research concluded that environmental policies applied for national energy development should consider the different levels of complexities of regional and global supply chains for material footprint analysis.

Review of Strategy Toward DEMO in Japan and Required Innovations

A path and strategy toward demonstration plant (DEMO) to achieve net electric power generation and to establish various technologies for successive commercial plants will be shown based on the Japan’s action plan for fusion energy development. It is clear that the fusion power plants may not be realized without a lot of technological innovations. Required innovations to attain the commercial plant as a final target of the DEMO plant are considered and variety of novel innovations introduced in previous reactor designs in Japan have been reviewed in order to prove a possible path to the fusion power plants.

Solar Energy Resource Potential Assessment for Industrial Rooftop Area: A Case Study of Huangpu District

The major energy strategy of China has made a specific goal for the development of renewable energy. For the city of Guangzhou, the government has made a target of the installation of solar PV development: up to 2000MW by 2020. Huangpu district is one of the national economic development zone (EDZ) in Guangzhou, the district government has planned to install 302MW solar PV capacity in any form across all building sectors by 2020. This paper contributes to current renewable energy development planning by combining spatial analysis tools and urban planning data together to evaluate the solar energy resources and technical potential for the solar PV installation on industrial rooftop in Huangpu district in a more accurate method. Results shows that the technical potential for industrial rooftop solar PV in the Huangpu EDZ was estimated to be 649MW with a total effective solar PV panel area of 3,956,402 m2. It shows that the technical potential is enough to reach the solar PV target set by the government if we only consider the industrial buildings.

Life cycle environmental and economic analysis of regional-scale food-waste biogas production with digestate nutrient management for fig fertilisation

Abstract Small-scale biomethane production is a promising option to meet a domestic energy supply in Thailand. As part of domestic energy policies in Thailand (new Alternative Energy Development Plan), a biogas plant with 400 kg-wet/d capacity manufactured by Japanese companies was installed at a university in Thailand in 2013. For sustainable operation of the plant, the primary energy demand (PED) and life-cycle greenhouse gas (GHG) emission associated with well-to-tank, WTT (the food waste transport, conversion to biomethane and biomethane storage) are assessed. GHGs emission associated with well-to-wheel, WTW (the food waste transport, conversion to biomethane, biomethane storage and driving fuelled by the biomethane) are revealed. Economic analysis is also implemented based on the actual operating data including digestate price derived from N-P-K nutrient management (0.2689 THB/kg), and payback time sensitivity analysis. Primary energy demand associated with WTT processes was 0.68 MJ/MJ-CH4. GHG emission from WTW processes is 132.0 kg-CO2eq/km, which is less than one third that of the other biofuels (biohydrogen, biomethanol, bio-dimethylether and Fischer-Tropsch diesel). The economic analysis showed that food waste disposal income and electricity sales are hot spot in the revenue and interest payment and maintenance cost has high impact in expenditure. The most sensitive parameter is biomethane yield, which should be kept higher for profitable operation of the biogas plant in Thailand.

Forecasting Energy-Related CO2 Emissions Employing a Novel SSA-LSSVM Model: Considering Structural Factors in China

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.

Comprehensive evaluation of regional clean energy development levels based on principal component analysis and rough set theory

Abstract After the strategic concept of global energy Internet constructed, it has produced a strong response in the world. In recent years, countries around the world are actively promoting energy transformation and have developed clean energy development goals and plans. This paper constructs a comprehensive evaluation index system for the level of clean energy development by considering policies and regulations, energy supply, environmental impact, energy consumption, technology, economy and so on. At the same time, China, Germany, the United States and other countries are selected as evaluation objects. On the basis of searching, processing and analyzing a large number of data, firstly, the research uses the principal component analysis method to carry out the correlation cluster analysis of the index and then uses the rough set method to assign the weight of the extracted principal components. Finally, the weight of each index in the index system is calculated. The results of the comprehensive evaluation of each country are compared and analyzed, which shows that the weight determination method based on rough set theory and principal component analysis is more reasonable and objective. At the end of the paper, some suggestions are proposed to promote the development of clean energy.

Study on new energy development planning and absorptive capability of Xinjiang in China considering resource characteristics and demand prediction

Xinjiang, as the area where wind energy and solar energy resources are extremely rich, with good resource development characteristics, can provide a support for regional power development and supply protection. This paper systematically analyzes the new energy resource and development characteristics of Xinjiang and carries out the demand prediction and excavation of load characteristics of Xinjiang power market. Combing the development plan of new energy of Xinjiang and considering the construction of transmission channel, it analyzes the absorptive capability of new energy. It provides certain reference for the comprehensive planning of new energy development in Xinjiang and the improvement of absorptive capacity of new energy.

Total-Factor Energy Efficiency in BRI Countries: An Estimation Based on Three-Stage DEA Model

The Belt and Road Initiative (BRI) is showing its great influence and leadership on the international energy cooperation. Based on the three-stage DEA model, total-factor energy efficiency (TFEE) in 35 BRI countries in 2015 was measured in this article. It shows that the three-stage DEA model could eliminate errors of environment variable and random, which made the result better than traditional DEA model. When environment variable errors and random errors were eliminated, the mean value of TFEE was declined. It demonstrated that TFEE of the whole sample group was overestimated because of external environment impacts and random errors. The TFEE indicators of high-income countries like South Korea, Singapore, Israel and Turkey are 1, which is in the efficiency frontier. The TFEE indicators of Russia, Saudi Arabia, Poland and China are over 0.8. And the indicators of Uzbekistan, Ukraine, South Africa and Bulgaria are in a low level. The potential of energy-saving and emissions reduction is great in countries with low TFEE indicators. Because of the gap in energy efficiency, it is necessary to distinguish different countries in the energy technology options, development planning and regulation in BRI countries.

Ranking of Renewable Energy for The National Electricity Plan in Thailand Using an Analytical Hierarchy Process (AHP)

Awareness of environmental burdens and uncertainty about the quantity and price of fossil fuels are challenges to efficient energy supply and use. Therefore, energy planning is an important mechanism in addressing these challenges. The objective of this study was to prioritize renewable energy types as a guideline for targeting Thailand’s power generation plan using an analytical hierarchy process (AHP). Six expert groups with different backgrounds related to the renewable energy field in Thailand were involved through questionnaire surveys for a prioritization exercise. Five main criteria and eight associated sub-criteria were studied. Seven of the renewable resources were selected according to the Alternative Energy Development Plan (AEDP2015). The results showed that solar energy had the highest ranking score, followed by biomass, small hydropower, biogas from wastewater/solid waste, wind energy, biogas from energy crops, and waste to energy. Compared with AEDP2015, the results of this research showed significant differences. Although this research was performed for academic purposes, its result can be useful as a model for stakeholders, policy makers, and decision makers who are involved in the energy policy sector.

Material flows analysis of plastic in Thailand

A study of the plastics materials flow analysis in Thailand was aim to show the flow of plastic materials through production, consumption, and waste management based on the year 2013 and projection to the year 2020. This paper finds the plastic waste generation increasing steadily in line with population growth and increasing consumption. In addition, the simulation under waste management three scenarios includes the following: the business as usual, increasing recycled rate set on the National Solid Waste Management Master Plan (2016-2021) and increasing in rate of energy recovery by the Alternative Energy Development Plan 2015-2036. The plastic material flow analysis has shown that the implementation of the National Solid Waste Management Master Plan (2016-2021) or the Alternative Energy Development Plan 2015-2036 can reduce uncollected waste and improper waste disposal. Moreover, there is benefit by adding value from recycled materials and energy recovery.

A copula-based flexible-stochastic programming method for planning regional energy system under multiple uncertainties: A case study of the urban agglomeration of Beijing and Tianjin

In this study, a copula-based flexible-stochastic programming (CFSP) method is developed for planning regional energy system (RES). CFSP can deal with multiple uncertainties expressed as interval values, random variables and fuzzy sets as well as their combinations employed to objective function and soft constraints. It can also reflect uncertain interactions among random variables through using copula functions even having different probability distributions and previously unknown correlations. Then, based on the developed CFSP approach, a CFSP-RES model is formulated for planning RES of the urban agglomeration of Beijing and Tianjin (China). Results disclose that uncertainties existed in the system components have significant effects on the outputs of decision variables and system cost, and the variation of system cost is reached 16.3%. Results also reveal that air pollutant emissions can be mitigated if the urban agglomeration can co-implement renewable energy development plans (REDP) over the planning horizon, with the reductive rates of [3.3, 7.6] % of sulfur dioxide (SO2), [2.7, 4.1] % of nitrogen oxides (NOx) and [7.0, 11.5] % of particulate matter (PM10). Compared to joint-probabilistic chance-constrained programming (JCP), the CFSP method is more effective for handling multiple random parameters associated with different probability distributions in which their correlations are unknown. Thus, it is not limited to some unjustified assumptions and can be applied to a wider range of problems than previous studies. The findings are helpful to explore the influence of interaction among random variables on modeling outputs and provide in-depth analysis for identifying desired decision schemes for planning RES.

Renewables in Development of The Arab East (Case of Solar Energy)

The article analyzes problems of solar energy rise in the Middle East and North Africa. The Arab World possesses huge resources of oil and natural gas and gained great significance as a world producer and exporter of energy. The MENA region home market is also a big consumer of oil and gas. The study stresses the fact, that industrial processing of mass amounts of crude inside the region has already created serious constraints in development of the area. The further steps to overwhelm the aggravations may be positive if secured through minimizing serious threats, concentrating around problems of climate warming and green gases. The perspectives of doing well in this vital sphere are closely connected with the implementation of various kinds of renewables represented in the Arab East in supply, given the fact that the most preferred of them is the sun energy as economically more commercial and so far more effective in its competition with the traditional fuels. Meanwhile the study confirms that the Arab World as a whole is not yet ready for frontal deployment of this method of electric power generation. The real obstacle for the major part of the Arab countries is the inequality of their economic potentials and lack of financial support for industrial use of the new type of energy. There are also social and economic limits doing alongside with the macroeconomic factors. Insufficient experience and lack of competence are followed by more serious factors seeing in bureaucratic hurdles, passivity of national capital, incorrect distribution of functions among administrative bodies, etc. The whole situation is abnormally stressed due to the absence of flexible cost-reflective energy policy and improper billing mechanisms still dominating the region and braking promotion of solar energy industry. The study comes to the conclusion that any delay with building-up of most favorable grounds for the energy market in the MENA region may provoke mal-practicing of the whole business and then the ambitious arab plan for the solar energy development may find itself effective only to a certain degree.

Potential Impact on Freshwater Resources from Agrofuel Feedstock Cultivation in Thailand: Implications of the Alternative Energy Development Plan 2015

The impact of water use in areas with abundant freshwater resources should not be the same as areas with limited resources. This impact is quantified as water scarcity footprint. The monthly water stress index with reference to environmental water requirement is proposed as a characterization factor. The biofuel policies of Thailand—cassava and sugarcane for bioethanol, and oil palm for biodiesel—were selected for the assessment based on land expansion and displacement scenarios. Cultivation was found to be the most water intensive phase in producing both biodiesel and bioethanol. Thus, the proposed index was applied for assessing and selecting areas having low values of the water scarcity footprint. The results showed low values for expanding oil palm plantations on abandoned land and displacing plantation areas with low yields of maize and pineapple with sugarcane and cassava. Additionally, shifting the crop calendar could be considered to reduce the stress situation such as the central region can avoid the water scarcity footprint by 38% from shifting sugarcane cultivation. Consequently mitigating this potential impact and threats to the ecosystem based on specific circumstances and context would be achieved through applying the proposed index in water resource and land suitability planning.

A method for evaluating photovoltaic potential in China based on GIS platform

Solar photovoltaic systems are commonly utilized in China. However, the associated research is still lack of its resource potential analysis in all regions in China. Based on the existed data about solar radiation and system conversion efficiency data, a new method for distributed photovoltaic potential assessment has been presented. The experiment of three kinds of solar photovoltaic system has been set up for the purpose of analyzing the relationship between conversion efficiency and environmental parameters. This paper fits the relationship between conversion efficiency and solar radiation intensity. This method takes into account the amount of solar radiation that is effectively generated and drives away the weak values. With the spatial analysis function of geographic information system (GIS) platform, frequency distribution of solar radiation intensity and PV potential in China can be derived. Furthermore, analytical results show that monocrystalline-silicon PV generation in the north-western and northern areas have reached a level of more than 200 kWh/(m2.a), making those areas be suitable for the development of PV system. However, the potential for southwest areas reaches a level of only 130 kWh/(m2.a). This paper can provide the baseline reference for solar energy development planning.

A genetic-algorithm-based remnant grey prediction model for energy demand forecasting.

Energy demand is an important economic index, and demand forecasting has played a significant role in drawing up energy development plans for cities or countries. As the use of large datasets and statistical assumptions is often impractical to forecast energy demand, the GM(1,1) model is commonly used because of its simplicity and ability to characterize an unknown system by using a limited number of data points to construct a time series model. This paper proposes a genetic-algorithm-based remnant GM(1,1) (GARGM(1,1)) with sign estimation to further improve the forecasting accuracy of the original GM(1,1) model. The distinctive feature of GARGM(1,1) is that it simultaneously optimizes the parameter specifications of the original and its residual models by using the GA. The results of experiments pertaining to a real case of energy demand in China showed that the proposed GARGM(1,1) outperforms other remnant GM(1,1) variants.

GHG Mitigation Potentials of Thailand’s Energy Policies to Achieve INDC Target

Climate change is a critical issue causing awareness of greenhouse gases (GHG) emission managements in many other countries around the world. The energy sector is the highest contributor of the global GHG emissions. In 2013, it contributes about 36 percent of total GHG emissions in Thailand. In 2015, Thailand submitted the Intended Nationally Determined Contribution (INDC) to the UNFCCC. The target of GHG reduction is 20 percent from the projected business-as-usual (BAU) level by 2030 in the Energy, Industrial Processes and Product Uses (IPPU), and Waste sectors; however, the level of contribution could increase up to 25 percent with support. In the roadmap of Thailand’s INDC, emission reduction in the energy sector is accounted for about 98 percent of overall reduction. The Energy Efficiency plan (EEP2015) and Alternative Energy Development plan (AEDP2015) are implemented in order to reduce not only energy consumption but also GHG emission in Thailand. Therefore, the objective of this study is to analyze the GHG mitigation potential of those measures in order to identify the effort to achieve the INDC’s target in 2030. In this study, the mitigation potential of the energy plans is divided into four scenarios. In 2030, emissions in the energy sector in BAU will be 425,649 kt-CO2eq. Moreover, in the same year, results indicate that GHG emissions can be mitigated by 47%, 23%, 30% and 29% in the SC1, SC2, SC3 and SC4 scenarios, respectively when compared to the BAU. However, the INDC plan can reduce GHG emissions by 27% in the energy sector. It implies that one of the plans should achieve at least 75% of its target and another must achieve 50% of its target, Thailand will accomplish its INDC target.

Development of an Appropriate Model for Forecasting Municipal Solid Waste Generation in Bangkok

Municipal solid waste (MSW) is a result of human activities. Accurate forecasting of MSW generation is crucial for sustainable management systems and planning. MSW is considered as an important resource for renewable energy development plans of cities. Due to the uncertainties and unavailability of sufficient MSW generation information in developing countries, including the difference of local conditions, various modeling methods were developed to predict MSW generation. The objectives of this paper are to identify influential variables that affect the amount of MSW generation and to predict the future MSW in Bangkok by employing linear and nonlinear models. The major factors of MSW in these two models are accounted by number of residents, people aged 15-59 years, number of households, income per household, and number of tourists. In the linear model, principal component analysis is capable to reduce multi-collinearity factors. This leads to the improvement of the performance of regression by a stepwise algorithm with R2=0.86. In the nonlinear model, artificial neural network (ANN) is conducted by designing an appropriate network architecture in the Matlab tool. This approach with one neuron demand in hidden layer exhibits the fitting value of R2=0.96, which is better than linear regression model. In these regards, the designed network in ANN is possibly stored for further analysis under the same conditions for high percentage of accuracy. All the results in this research can be utilized as part of solid plans for renewable energy development and eco-environmental recycle industry which require MSW as raw material.

Assessment of renewable energy and energy efficiency plans in Thailand’s industrial sector

The industrial sector is one of the main energy consuming sector in Thailand, and accounted for 36.2% of total energy consumption in 2013. The increase of population and economic growth affected energy demand and greenhouse gas (GHG) emissions. Thailand has the long-term climate plans related to energy saving and GHG mitigation in the industrial sector namely; Energy Efficiency Plan 2015 (EEP2015) and Alternative Energy Development Plan 2015 (AEDP2015). Both plans have the same periods and end at 2036. This paper analyzes the changes in GHG emissions during 2005-2036. Energy demand depends on population, gross domestic product, and fuel prices. The GHG mitigation can be evaluated by using Long-range Energy Alternative Planning system (LEAP) under various scenarios. The business-as-usual (BAU) scenario can be expressed as a frozen-scenario where new technology deployment and energy efficiency improvement are excluded. However, mitigation (MIT) scenarios are introduced by using the existing measures in the EEP2015 (MIT_EE scenario) and AEDP2015 (MIT_RE scenario). In this study, Thailand’s industries are categorized into eleven categories: food and beverage, textile, wood and furniture, paper and pulp, chemical, nonmetallic, basic metal, fabricated metal, construction, mining, and other industries. Fossil fuels are the main source of GHG emission accounted for 86% of total GHG emission in this industry. GHG emission will increase from 77.6 Mt-CO2eq in 2005 to 222.5 Mt-CO2eq in 2036. This study found that nonmetallic industry is the major GHG emitters. Full implementation of the EEP2015 and AEDP2015 can reduce GHG emissions by 36% and 9% in 2036 under MIT_EE and MIT_RE scenarios, respectively. Such GHG reductions can be achieved from the implementation of EEP2015, due to the financial incentives for energy performance achievement and enforcement of energy standards in the designated factories.

Long-term CO2 Emission Reduction from Renewable Energy in Power Sector: The case of Thailand in 2050

This paper presents CO2 mitigation potential in the power sector of Thailand regarding to the Power Development Plan (PDP 2015) and Alternative Energy Development Plan (AEDP 2015). Renewable energy and advanced technology have the potential to play an important role in providing clean energy. The Long-range Energy Alternative Planning (LEAP) model is used to analyze future electricity demand, fuel consumption and CO2 emissions reduction in the long term planning during 2010-2050. Regarding to those plans, implementations of renewable energy and advanced technology are proposed for mitigation options. The mitigation scenarios offer sustainable potential of renewable technologies and advanced technologies such as solar, wind, biomass, nuclear, hydropower, hydrogen fuel cell, tidal, geothermal and fossil-fuel power plants with carbon capture storage (CCS). Four mitigation scenarios are modeled along with the BAU scenario. The Mitigation 1 (MIT01) scenario is the base-case mitigation scenario based on the target of PDP2015, which target of renewable energy is 19,634 MW in 2036. In the Mitigation 2 (MIT02) scenario, the technologies in the MIT01 plus new technologies such as hydrogen fuel cell, tidal and geothermal are implemented. Clean fossil fuel technologies with CCS, for example natural gas and coal technologies, are applied in the Mitigation 3 (MIT03) scenario. In the Mitigation 4 (MIT04) scenario as the peak CO2 scenario. Results show that, in the BAU scenario, electricity demand and CO2 emissions from the power sector will increase to 52,770 ktoe and 345,959 kt-CO2eq, respectively in 2050. By adopting mitigation options, total CO2 emissions in 2050 can be mitigated by 69.66% in MIT04, following by 50.83% in MIT02, 47.22% in MIT03, and 41.71% in MIT01 when compared to the BAU scenario. Moreover, regarding to the MIT04, Thailand can meet its peak CO2 emission by 2036.