Search results

The purpose of this paper is to focus on life cycle cost analysis (LCCA) of 1 MW roof-top Solar Photovoltaic (PV) panels installed in warm and humid climatic region in Southern India. The effect of actual power generated from solar PV panels on financial indicators is evaluated.

LCCA is done using the actual power generated from solar PV panels for one year. The net present value (NPV), internal rate of return (IRR), simple payback period (SPP) and discounted payback period (DPP) are determined for a base case scenario. The effect of service life and the differences between the ideal power expected and the actual power generated is evaluated.

A base case scenario is evaluated using the actual power generation data, 25-year service life and 6 percent discount rate. The NPV, IRR, SPP and DPP are found to be INR 13m, 8 percent, 10.9 years and 18.8 years respectively. It is found that the actual power generated is about one-third less than the ideal power estimated by consultants prior to project bidding. The payback period increases by 70–120 percent when the actual power generated from solar PV panels is considered.

The return on investment calculated based on ideal power generation data without considering the operation and maintenance related aspects may lead to incorrect financial assessment. Hence, strategies toward solar power generation should also focus on the actual system performance during operation.

In the wake of the December 2015 Paris COP21 (Conference of Parties), and India's announced renewable energy commitments, Reliance Power is reviewing its renewable energy investments to arrive at a long term strategy for the role of renewable energy in its power generation portfolio and the financing of renewable projects. The case reviews the Indian government's policies to promote renewable energy; the evolution of the renewable energy sector; and Reliance Power's financing of renewable energy investments. The case requires identification of alternative long term strategies and their financing implications. This case serves as an introduction to renewable energy from the perspective of Reliance Power, a large private power generator of the country. These projects also provide a learning opportunity for Reliance Power to deal with fast evolving renewable technologies.

The environmental impact of energy supply is growing which has a significant impact on regional and global environmental issues. As a solution for this, both developed and developing nations paying attention to convert their energy productivity by using renewable energy like wind and solar energy. Sri Lankan government also aims to obtain the full amount of electricity required from local renewable sources by the year 2050 under the project called “sooryabala sangramaya” (the battle for solar energy). Currently, Sri Lanka’s power generation sector is heavily dependent on imported fuels, such as petroleum and coal, resulting in growing detrimental impacts on the country’s sustainable socioeconomic development. With the growing market of solar photovoltaic (PV) technology, Sri Lanka is turning its attention towards generating the total amount of electricity required from solar power by promoting the installation of arrays of PV panels on the rooftops of households, religious places, hotels, commercial establishments and industries. It also aims to deploy solar PV for sustainable rural development, mainly focused on uplifting people living in remote areas in the country. This chapter discusses how Sri Lanka has initiated a rooftop solar PV adoption program to lessen imported fuels’ socioeconomic and environmental impacts. Moreover, this case demonstrates that the adoption of rooftop solar PV brings many socioeconomic benefits to its consumers.

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

In order to accurately forecast nonlinear and complex characteristics of solar power generation in China, a novel discrete grey model with time-delayed power term (abbreviated as TDDGM(1,1,tα) is proposed in this paper.

Firstly, the time response function is deduced by using mathematical induction, which overcomes the defects of the traditional grey model. Then, the genetic algorithm is employed to determine the optimal nonlinear parameter to improve the flexibility and adaptability of the model. Finally, two real cases of installed solar capacity forecasting are given to verify the proposed model, showing its remarkable superiority over seven existing grey models.

Given the reliability and superiority of the model, the model TDDGM(1,1,tα) is applied to forecast the development trend of China's solar power generation in the coming years. The results show that the proposed model has higher prediction accuracy than the comparison models.

This paper provides a scientific and efficient method for forecasting solar power generation in China with nonlinear and complex characteristics. The forecast results can provide data support for government departments to formulate solar industry development policies.

The main contribution of this paper is to propose a novel discrete grey model with time-delayed power term, which can handle nonlinear and complex time series more effectively. In addition, the genetic algorithm is employed to search for optimal parameters, which improves the prediction accuracy of the model.

Decentralized solar systems are increasingly being used as alternative source of off-grid electrification in Bangladesh. They offer solutions to provide (clean) electricity to the low-income households that are not currently served by the national grid. The standards of solar systems need to be improved to maximize the benefits they offer for off-grid electrification.

A quantitative research approach was used to explore the power output performance of six solar systems samples. In order to realize a proper load management, daily power production was measured to determine the generation capacity of 50, 60 and 100 Wp monocrystalline and polycrystalline modules when average solar irradiation was 916 W/m². In the testing system, the irradiation was measured by panel analyzer HT instrument I-V 400. The load arrangement comprised of different kinds of appliances (fan, light, TV). The daily consumption of energy by these loads was calculated using daily operational hours to determine system power performance.

The authors found that monocrystalline system performs better than polycrystalline by 0.39 kilowatt-hour (kWh) with capacity of 100 watt-peak (Wp) modules. The carbon dioxide (CO₂) emissions reduction potential of our sample solar systems were also estimated by assuming a scenario. This was derived by using the electricity emission factor for natural gas (CH₄), since CH₄ is the main source of energy for power generation in Bangladesh. Savings in CO₂ of 0.52 kgCO₂/kWh is possible with the adoption of a 100 Wp monocrystalline module.

Government actions that promote the use of monocrystalline module will enhance the benefits of the use of solar systems in providing quality and sustainable electricity. This will contribute to government's efforts towards achieving some of the United Nations (UN) sustainable development goals (SDG) and resilience of the most vulnerable population to the effects and impacts of climate change.

Almost all solar modules found in off-grid areas are polycrystalline whose energy generation capacity is much lower compared to monocrystalline types. But use of low efficient polycrystalline solar module hindered the development of country's solar sector and option to save carbon emission. The use of highly efficient monocrystalline solar module will save also the country's land as the country has land scarcity challenges for establishing large-scale solar power plant. The authors also recommend actions that can be implemented at the national level to improve the attractiveness of monocrystalline solar systems in Bangladesh.

The purpose of this paper is to highlight the opportunity for the energy policy in Brazil to tackle the very high cost-effectiveness potencial of solar energy to the power system. Three mechanisms to achieve ambitious reductions in the greenhouse gas emissions from the power sector by 2030 and 2040 are assessed wherein treated as solar targets under ambitious reductions in the greenhouse gas emissions from the power sector. Then, three mechanisms to achieve these selected solar targets are suggested.

This paper reviews current and future incentive mechanisms to promote solar energy. An integrated energy system optimization model shows the most cost-efficient deployment level. Incentive mechanisms can promote renewable sources, aiming to tackle climate change and ensuring energy security, while taking advantage of endogenous energy resources potential. Based on a literature review, as well as on the specific characteristics of the Brazilian power system, under restrictions for the expansion of hydroelectricity and ambitious limitation in the emissions of greenhouse gases from the power sector.

The potential unexploited of solar energy is huge but it needs the appropriate incentive mechanism to be deployed. These mechanisms would be more effective if they have a specific technological and temporal focus. The solar energy deployment in large scale is important to the mitigation of climate change.

The value of the research is twofold: estimations of the cost-effective potential of solar technologies, generated from an integrated optimization energy model, fully calibrated for the Brazilian power system, while tacking the increasing electricity demand, the expected reduction of greenhouse gas emissions and the need to increase the access to clean and affordable energy, up to 2040; proposals of three mechanisms to deploy centralized PV, distributed PV and solar thermal power, taking the best experiences in several countries and the recent Brazilian cases.

Dynamic economic and emission dispatch (DEED) aims to optimally set the active power generation with constraints in a power system, which should target minimum operation cost and at the same time minimize the pollution in terms of emission when the load dynamically changes hour to hour. The purpose of this study is to achieve optimal economic and emission dispatch of an electrical system with a renewable generation mix, consisting of 3-unit thermal, 2-unit wind and 2-unit solar generators for dynamic load variation in a day. An improved version of a simple, easy to understand and popular optimization algorithm particle swarm optimization (PSO) referred to as a constriction factor-based particle swarm optimization (CFBPSO) algorithm is deployed to get optimal solution as compared to PSO, modified PSO and red deer algorithm (RDA).

Different model with and without wind and solar power generating systems; with valve point effect is analyzed. The thermal generating system (TGs) are the major green house gaseous emission producers on earth. To take up this ecological issue in addition to economic operation cost, the wind and solar energy sources are integrated with the thermal system in a phased manner for electrical power generation and optimized for dynamic load variation. This DEED being a multi-objective optimization (MO) has contradictory objectives of fuel cost and emission. To get the finest combination of the two objectives and to get a non-dominated solution the fuzzy decision-making (FDM) method is used herein, the MO problem is solved by a single objective function, including min-max price penalty factor on emission in the total cost to treat as cost. Further, the weight factor accumulation (WFA) technique normalizes the pair of objectives into a single objective by giving each objective a weightage. The weightage is decided by the FDM approach in a systematic manner from a set of non-dominated solutions. Here, the CFBPSO algorithm is applied to lessen the total generation cost and emission of the thermal power meeting the load dynamically.

The efficacy of the contribution of stochastic wind and solar power generation with the TGs in the dropping of net fuel cost and emission in a day for dynamic load vis-à-vis the case with TGs is established.

Cost and emission are conflicting objectives and can be handled carefully by weight factors and penalty factors to find out the best solution.

The proposed methodology and its strategy are very useful for thermal power plants incorporating diverse sources of generations. As the execution time is very less, practical implementation can be possible.

As the cheaper generation schedule is obtained with respect to time, cost and emission are minimized, a huge revenue can be saved over the passage of time, and therefore it has a societal impact.

In this work, the WFA with the FDM method is used to facilitate CFBPSO to decipher this DEED multi-objective problem. The results reveal the competence of the projected proposal to satisfy the dynamic load demand and to diminish the combined cost in contrast to the PSO algorithm, modified PSO algorithm and a newly developed meta-heuristic algorithm RDA in a similar system.

This chapter provides an overview of the current status of renewable energy projects and identifies the key success factors of well-performing public–private partnerships (PPPs). To this end, this study analyses around 1,700 renewable projects on the World Bank’s Private Participation in Renewable Energy (PPRE) database. We then follow an inductive approach for a case review and examine a 5-MW rooftop solar PPP in Gujarat, India, that had been implemented in 2012. In spite of the rapid growth of renewable PPPs, regional disparity is distinct and most PPPs have been undertaken in Latin America and the Caribbean or a few selective countries such as China or South Africa. The case study informs that the successful PPPs may be attributed to such factors as policy coordination in multi-governance systems to attract project investments, the handling of land constraints in a project planning stage, and Green Incentive given to project participants. It offers a valuable insight into the significance of well-designed PPPs for enhanced energy access in developing countries, while accelerating the global transition to renewable-based energy supply to promote sustainable development.