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In order to verify the feasibility of different techniques, this chapter further studies the adaptability of two massive straw biomass applications in rural areas in China.

The methods of assessing biomass power generation project with Life Cycle Assessment (LCA), survey and field test of one biogas station, and game-theoretic analysis are adopted.

The following conclusions can be drawn: The air pollution costs account for more than 60% of the total environmental cost, followed by depreciation expense and maintenance fee of 18%, compared to that of biomass power generation at 0.01711 CNY/kWh. The adopted greenhouse sunlight technology of Solar Biogas Plant in Xuzhou, China, raises the inside average temperature by 11.0 °C higher than outside and keeps the pool temperature above 16 °C in winter, ensuring a gas productivity of biogas project in winter up to 0.5–0.7 m³/m³ by volume. This chapter also analyzes the information cost incurred by asymmetric information in biomass power generation via game theory method and illustrates the information structure with game results. It provides not only a foundation for the policy research in promoting straw power generation but also theoretical framework to solve the problem of straw collection.

These studies will propose solutions to relevant problems arisen in the running process.

These studies are all based on real cases, field research, and appropriate theoretical analyses, so, they can reduce the relevant costs and promote the application of relevant technologies.

To better understand bioenergy's role in sustainable rural development and cleaner environment, it is necessary to place it in a local regional context. This paper aims to provide a conceptual approach for biomass-based energy self-sufficiency in rural areas of developing and underdeveloped countries having a strong agricultural sector. It further provides a framework for the estimation of surplus biomass and bioenergy potential and the biomass power emissions in a rural area.

A detailed approach is laid out to attain energy self-sufficiency in rural areas encompassing identification of surplus biomass resources in a selected area, suitable conversion technologies, consideration of local end-use priorities, skill development and monitoring of the project.

Following the novel approach proposed in this paper a case study analysis for Thanagazi block (Alwar District, India) is done, and it is observed that locally available biomass in the block can substitute more than 75% of the conventional energy demand and save 78% emissions vis-à-vis equivalent coal power. This indicates that creating local bioenergy production system as a means of substituting/complementing fossil energy can contribute to a cleaner self-sufficient ecosystem.

Biomass is a spatio-temporal resource. Prior works have looked at bioenergy potential for national or state levels; however, granular data to reveal a more realistic outlook in a rural area is the novelty of this work. Furthermore, biomass assessment studies largely focus on crop residual biomass, whereas the present study also includes livestock manure assessment which is a major resource in rural areas. This paper highlights the need and the approach for exploring locally available biomass to meet the local energy demands for clean energy security while considering the involvement of the local population in bioenergy planning and implementation.

Biomass waste can be used as fuel in biomass power plants to generate electricity. It is a type of renewable energy widely available in Malaysia because 12 million tons of the biomass waste is produced every year. At present, only 5 per cent of the total biomass waste in Sabah, one of the states in Malaysia, is used to generate electricity for on-site consumption. The remaining 95 per cent of the biomass waste has not been utilized because the transportation cost for shifting the waste from the plantations to the power plants is substantial, hence making the cost of the biomass generated electricity to be high. Therefore, a methodology is developed and presented in this paper to determine the optimum geographic distribution and capacities of the biomass power plants around a region so that the cost of biomass generated electricity can be minimized. The paper aims to discuss these issues.

The methodology is able to identify the potential locations of biomass power plants on any locations on a region taking into account the operation and capital costs of the power plants as well as the cost of connecting the power plants to the national grid. The methodology is programmed using Fortran.

This methodology is applied to Sabah using the real data. The results generated from the methodology show the best locations and capacities of biomass power plants in Sabah. There are 20 locations suitable for biomass power plants. The total capacity of these biomass power plants is 4,996 MW with an annual generation of 35,013 GWh. This is sufficient to meet all the electricity demand in Sabah up to 2030.

The methodology is an effective tool to determine the best geographic locations and sizes of the biomass power plants around a region.

Ethiopia’s energy sector faces critical challenges to meeting steadily increasing energy demand given limited infrastructure, heavy reliance on hydroelectric power and underdevelopment of alternative energy resources. The purpose of this paper was to identify optimal least cost investment decisions for integrated energy source diversification. The authors seek to contribute to the relevant literature by paying particular attention to the role of public policy for promoting renewable energy investment and to better understand future energy security implications of various sources of uncertainty.

The authors created a dynamic linear programming model using General Algebraic Modelling System software to explore the national energy security implications of uncertainties associated with increasing technological advances and efficiency, and climate change scenarios.

To cope with the impacts of drought expected from future climate change on hydroelectric power production, Ethiopia would need to invest in the development of alternative energy resources. Such investment would not only enhance the sustainability and reliability of energy production but also increase costs. Greater rates of technological and efficiency innovations, however, were found to improve electricity diversification and reduce production costs and shadow prices or resource scarcity, and are thus key for enhancing energy security and reducing the risks posed by drought.

The dynamic linear programming model by the authors represents a flexible sector modelling tool for exploring the sustainability and efficiency of energy resource development pathways and evaluating the effects of different sources of uncertainty on the energy sector.

There are thousands of areas excluded from using electrical energy in China. It is mainly because that these places, which are away from towns, have the characteristics of scattered living and low-power consumption and are difficult to construct the power grid. The utilization of energy in remote areas could improve the level of education and quality of life for people living in there, which has great social significance. However, how to choose the optimal power generation model quantitatively according to local energy advantages is a difficult problem.

To carry out a better assessment of the energy benefits of Chinese rural areas to assist the decision-making of energy utilization project, this paper takes Sunan Yugu Autonomous County in Gansu Province as an example. Four feasible energy utilization scenarios are proposed by analyzing its geographical conditions and re-source advantage, respectively, are photovoltaic power generation, biomass power generation, wind power generation and power grid extension. Based on the above scenarios, the evaluation index system of comprehensive utilization of energy in remote areas is constructed, and the comprehensive benefit of each model is evaluated by adopting entropy-based fuzzy comprehensive evaluation model.

Evaluation results show that the comprehensive benefits of photovoltaic power generation is the best, followed by power grid extension. Thus, preference should be given to the two models in the energy utilization in Sunan County. This evaluation model can provide a scientific reference for the selection decision-making of energy utilization project, which is helpful to provide the feasibility and efficiency of the construction of energy utilization project.

The authors construct the comprehensive benefit evaluation index system and evaluate the comprehensive benefits of different scenarios are by using entropy - fuzzy comprehensive evaluation model. Then the qualitative problem can be analyzed quantitatively. The purpose of this study is to support the decision-making of energy investment. Simultaneously, the paper also has some practical significance in improving the credibility of the government and the quality of local people’s life.

Bioenergy is a key component of climate change mitigation strategies aiming at low stabilization. Its versatility and capacity to generate negative emissions when combined with carbon capture and storage add degrees of freedom to the timing of emission reductions. This paper aims to explore the robustness of a bioenergy-based mitigation strategy by addressing several dimensions of uncertainty on biomass potential, bioenergy use and induced land use change emissions.

Different mitigation scenarios were explored by two different energy-economy optimization models coupled to the same land use model, which provides a common basis for the second generation bioenergy dynamics in the two energy-economy models.

Using bioenergy is found to be a robust mitigation strategy as demonstrated by high biomass shares in primary energy demand in both models and in all mitigation scenarios.

A variety of possible storylines about future uses of biomass exist. The comparison of the technology choices preferred by the applied models helps understand how future emission reductions can be achieved under alternative storylines.

The presented comparison-based assessment goes beyond other comparison studies because both energy-economy models are coupled to the same land use model.

Renewable energy sources are likely to play a major role in meeting the future energy requirement of a developing country like India. Among the various renewable energy sources, the bio‐energy plays a key role for the power generation. In this paper, an attempt is made to develop a fuzzy based linear programming optimal electricity allocation model (OEAM) that minimizes the cost and determines the optimum allocation of different energy sources for the centralized and decentralized power generation in India with special emphasis to bio‐energy.

The OEAM model optimizes and selects the appropriate energy options for the power generation on the factors such as cost, potential, demand, efficiency, emission and carbon tax. The objective function of the model is minimizing the cost of power generation. The other factors are used as constraints in the model. The fuzzy linear programming optimization approach is used in the model.

The extents of energy sources distribution for the power generation in the year 2020 would be 15,800 GWh (4 per cent) from the coal based plants, 85,400 GWh (20 per cent) from the nuclear plants, 191,100 GWh (44 per cent) from the hydro plants, 22,400 GWh (5 per cent) from the wind mills, 45,520 GWh (11 per cent) from the biomass gasifier plants, 14,112 GWh (3 per cent) from the biogas plants, 8,400 GWh (2 per cent) from the solid waste, 33,600 GWh (8 per cent) from the cogeneration plants and 11,970 GWh (3 per cent) from the mini hydel plants, respectively.

The OEAM has been developed for the electricity demand allocation for the year 2020. An extensive literature survey revealed that carbon tax and emission constraints were never used in the previous models and they are considered in the present model.