Energy models for commercial energy prediction and substitution of renewable energy sources

In this paper, three models have been projected namely Modified Econometric Mathematical (MEM) model, Mathematical Programming Energy-Economy-Environment (MPEEE) model, and Optimal Renewable Energy Mathematical (OREM) model. The actual demand for coal, oil and electricity is predicted using the MEM model based on economic, technological and environmental factors. The results were used in the MPEEE model, which determines the optimum allocation of commercial energy sources based on environmental limitations. The gap between the actual energy demand from the MEM model and optimal energy use from the MPEEE model, has to be met by the renewable energy sources. The study develops an OREM model that would facilitate effective utilization of renewable energy sources in India, based on cost, efficiency, social acceptance, reliability, potential and demand. The economic variations in solar energy systems and inclusion of environmental constraint are also analyzed with OREM model. The OREM model will help policy makers in the formulation and implementation of strategies concerning renewable energy sources in India for the next two decades.     

A comparative analysis of energy and CO2 taxes on the primary energy mix for electricity generation

In many countries, economies are moving towards internalization of external costs of greenhouse‐gas (GHG) emissions. This can best be achieved by either imposing additional taxes or by using an emission‐permit‐trading scheme. The electricity sector is under scrutiny in the allocation of emission‐reduction objectives, not only because it is a large homogeneous target, but also because of the obvious emission‐reduction potential by decreasing power generation based on carbon‐intensive fuels. In this paper, we discuss the impact of a primary‐energy tax and a CO₂ tax on the dispatching strategy in power generation. In a case study for the Belgian power‐generating context, several tax levels are investigated and the impact on the optimal dispatch is simulated. The impact of the taxes on the power demand or on the investment strategies is not considered. As a conclusion, we find that a CO₂ tax is more effective than a primary‐energy tax. Both taxes accomplish an increased generation efficiency in the form of a promotion of combined‐cycle gas‐fired units over coal‐fired units. The CO₂ tax adds an incentive for fuel switching which can be achieved by altering the merit order of power plants or by switching to a fuel with a lower carbon content within a plant. For the CO₂ tax, 13 €/tonCO₂ is withheld as the optimal value which results in an emission reduction of 13% of the electricity‐related GHG emissions in the Belgian power context of 2000. A tax higher than 13 €/tonCO₂ does not contribute to the further reduction of GHGs. Copyright © 2005 John Wiley & Sons, Ltd.     

Model‐based approach for planning renewable energy transition in a resource‐constrained electricity system—A case study from India

Globally, electricity systems are going through transitions. The contributions from renewable energy‐based power generation, both in installed capacity and electricity generation, are moving from marginal to the mainstream. India is not an exception; it is aggressively pursuing this transition by fixing steep targets for renewable capacity additions. While the cost of renewable energy sources is expected to fast reach grid parity, the policy interventions play a critical role in ramping up the efforts to support the proposed investments in renewable capacity and renewable electricity generation. In this respect, this research attempts to analyze the effectiveness of renewable energy policies such as Renewable Purchase Obligation (RPO) and Renewable Energy Certificate mechanisms in tapping the renewable energy potential in India. We propose a mixed‐integer linear programming model‐based approach to evaluate the effectiveness of the above interventions in the Indian context. The model is developed and validated as a low carbon electricity planning tool to optimally meet the dynamic electricity demand and RPO targets as well as to manage the unmet total electricity demand and RPO targets. The Karnataka state electricity system (a state in south India) is chosen as a case study. The results suggest that Karnataka Electricity System is moving toward a sustainable renewable energy future even without any support from nonsolar Renewable Energy Certificate policy. However, policy interventions are critical for optimally utilizing the solar generation capacity.     

Optimal asset allocation of wind energy units in conjunction with demand response for a large‐scale electric grid

Demand response is considered to be a realistic and comparatively inexpensive solution aimed at increasing the penetration of renewable generations into the bulk electricity systems. The work in this paper highlights the demand response in conjunction with the optimal capacity of installed wind energy resources allocation. Authors proposed a total annual system cost model to minimize the cost of allocating wind power generating assets. This model contains capacity expansion, production, uncertainty, wind variability, emissions, and elasticity in demand to find out cost per hour to deliver electricity. A large‐scale electric grid (25 GW) is used to apply this model. Authors discovered that demand response based on interhourly system is not as much helpful as demand response grounded on intrahourly system. According to results, 32% wind generation share will provide the least cost. It is also worth noting that optimal amount of wind generation is much sensitive to installation cost as well as carbon tax.     

Renewable energy planning for India in 21st century

Energy is a vital input for economic and social development of any country. With increasing industrial and agricultural activities in the country, the demand for energy is also rising. Solar, wind and biomass are accepted as dependable and widely available renewable sources of energy. Development of an energy model will help in the proper allocation of these renewables in meeting the future demand of energy in India. The present work deals with the development of an Optimal Renewable Energy Model (OREM) for the effective utilisation of renewable energy sources in India for the year-2020-21. The objective of the Optimal Renewable Energy Model (OREM) was minimising cost/efficiency ratio based on social acceptance, reliability, demand and potential constraints. The OREM model allocated renewable energy sources for different end-uses such as lighting, cooking, pumping, heating, cooling and transportation for the year 2020-21.     

Renewable energy in India — a modelling study for 2020–2021

The energy requirement in India is steadily increasing and this requirement is being met by both commercial and renewable energy sources. Due to the non-availability of sufficient resources and a considerable amount of emission of pollutants from commercial energy, it is now being felt that renewable energy has to be utilized to a greater extent. An optimization model was developed to determine the optimum allocation of renewable energy in various end-uses in 2020–2021, taking into account commercial energy requirement. In lighting end-use renewable energy to an extent of 1.27×10¹⁵ kJ can be utilized. Scenarios were developed for various parameters and sensitivity analysis was performed on the model. It was found that for a 3% increase in social acceptance of bio resources, there was 65% decrease in solar PV utilization and to that extent bioresources were introduced. Similar analysis was performed on the model by changing the demand, potential, reliability, emission and employment factors. The analysis revealed the critical parameters for the utilization of a renewable energy source. Using the critical parameters, appropriate policies can be formulated for promoting renewable energy sources.     

对我国发展纯电动汽车的质疑与思考

近年来国家对纯电动汽车的扶持力度正在不断加大。然而纯电动汽车、混合动力汽车等以电力作燃料,并非真正意义上的新能源汽车。纯电动汽车所消耗的电力需要由发电厂提供,相当于以煤代油,其外部负效应表现在燃煤电厂的负效应上。燃油汽车和纯电动汽车每辆车每年的能耗折算成标煤分别为1.53t和1.6t,基本上相等,但燃油排放的二氧化碳比燃煤少,所以纯电动汽车并不是低碳汽车。用纯电动汽车替代燃油汽车,很可能是减少了石油进口,但却要增加煤炭进口,并不能从根本上提高我国能源的安全性。发展纯电动汽车需要大量投资,再加上环境污染,经济性很差。鉴于此,建议我国应重新定义新能源汽车,要真正利用新能源作为汽车的动力,寻找适合我国能源资源条件的真正的新能源汽车或替代能源汽车;中国在相当长的时间里节能减排还是主要依靠传统燃油汽车,在重视研发新能源汽车、研究替代燃油汽车的同时,应重视传统燃油汽车的节能降耗;控制汽车消费是最有效的节油措施;节能减排必须要从一次能源算起,我国电源结构以煤电为主,并不适宜发展纯电动汽车。中国要等到第一次和第二次能源大转换完成之后,当天然气、水电、核电在电源结构中占据主体地位时,纯电动汽车才会有广阔的发展前景。     

Reliability based socio economic optimal renewable energy model for India

Renewable energy sources such as solar, wind and biomass have to play a vital role in the developing countries like India in order to meet the growing energy demand. In the last five years, some renewable energy sources had emerged as technically and economically viable alternatives in the energy sector, as a result, more ambitious plans for their dissemination were being launched. In this situation, development of an energy model exclusively for renewables will help in the allocation of appropriate renewable energy systems for different end-uses in the future. An attempt has been made to develop a reliability based socio economic optimal renewable energy model for India in the year 2020–2021. The effect of social acceptance variation in OREM model was analysed. The lighting end-use would be met by solar PV and biogas system to an extent of 0.5198×10¹⁵ kJ and 0.75×10¹⁵ kJ, respectively. Similarly, the renewable energy utilisation is found for other end-uses.     

Multi-objective optimal allocation strategy for the energy internet in Huangpu District,Guangzhou, China

To improve the overall efficiency of the energy system, the basic structure for the energy internet of coordination and optimization of “generation-grid-load-storage” of Huangpu District, Guangzhou, China is designed, while the arrangement for the output of centralized and distributed energy module and energy storage are proposed. Taking economic benefit maximization, environmental benefit maximization and energy efficiency maximization as sub-objectives, the mathematical model of multi-objective optimal allocation and operation strategy of the energy internet is established considering supply-demand balance constraints, equipment characteristic constraints, operation mode constraints, and energy conditions constraints. The calculation results show that without considering the outsourced electricity, the balanced strategy, the economic development strategy, the environmental protection strategy, and the energy efficiency strategy are obtained by calculation, which are all superior to the traditional energy supply strategy. Moreover, considering the outsourced electricity, the proportion of outsourced electricity to total electricity is 19.8%, which is the system optimization of the energy internet under certain power demand. Compared with other strategies without outsourced electricity, the outsourced electricity strategy can have a certain emission reduction effect, but at the same time reduce the economic benefit. Furthermore, the huge difference in demand for thermal and cooling load between industrial and commercial areas results in the installed capacity of gas distributed energy stations in industrial areas being nearly twice as large as that in commercial areas. The distributed photovoltaic power generation is allocated according to the proportion of the installed roof areas of photovoltaic power generation system in residential, industrial, and commercial areas.     

Multi-objective optimization of large-scale grid-connected photovoltaic-hydrogen-natural gas integrated energy power station based on carbon emission priority

Establishing integrated energy systems is conducive for improving renewable energy utilization and promoting decarbonization. In this study, a grid-connected photovoltaic-hydrogen-natural gas integrated energy system is established to explore the effects of the configuration of the integrated energy system on its environment and economy. A multi-objective hierarchical optimization allocation model is developed, and an optimization strategy with carbon emission superior to total cost is established for the first time. Additionally, the economy, environment, and energy efficiency of the system are analyzed. A comparative study is performed using a strategy considering that the total cost is superior to carbon emission. A case study reveals that the levelized cost of electricity increases by 62.24%, levelized carbon emission of power decreases by 74.19%, and energy efficiency increases by 8.51%, as compared with those of the comparison strategy. Thus, the carbon emission of the system is reduced considerably, and the energy efficiency is improved. Although the cost of the system optimized by the proposed strategy is higher, it is economically feasible. Further analyses indicate that extending the grid-connected period would be infeasible, as it might increase the total cost and carbon emission of the system. Moreover, sensitivity analyses show that increasing the natural gas price or carbon tax base price will not reduce the carbon emission of the system.     

Joint electricity and carbon market for Northeast Asia energy interconnection

Decarbonization of the electricity sector is crucial to mitigate the impacts of climate change and global warming over the coming decades. The key challenges for achieving this goal are carbon emission trading and electricity sector regulation, which are also the major components of the carbon and electricity markets, respectively. In this paper, a joint electricity and carbon market model is proposed to investigate the relationships between electricity price, carbon price, and electricity generation capacity, thereby identifying pathways toward a renewable energy transition under the transactional energy interconnection framework. The proposed model is a dynamically iterative optimization model consisting of upper- level and lower-level models. The upper-level model optimizes power generation and obtains the electricity price, which drives the lower-level model to update the carbon price and electricity generation capacity. The proposed model is verified using the Northeast Asia power grid. The results show that increasing carbon price will result in increased electricity price, along with further increases in renewable energy generation capacity in the following period. This increase in renewable energy generation will reduce reliance on carbon-emitting energy sources, and hence the carbon price will decline. Moreover, the interconnection among zones in the Northeast Asia power grid will enable reasonable allocation of zonal power generation. Carbon capture and storage (CCS) will be an effective technology to reduce the carbon emissions and further realize the emission reduction targets in 2030-2050. It eases the stress of realizing the energy transition because of the less urgency to install additional renewable energy capacity.     

A methodology for the electrical energy system planning of Tamil Nadu state (India)

In this paper, an energy planning optimisation procedure of a selected territory is illustrated and applied using an energy flow optimisation model. The developed approach takes into account various electricity generating options to meet the energy needs of various demand sectors. Energy saving techniques and hybrid technologies are considered and various scenarios are developed by assessing the contribution of renewable energy technologies over the planning period. The procedure aims to reduce the total actualised cost of energy generation over selected time horizon and predicts the additional installations required along with the existing facilities to meet the energy demand. At the same time the role of renewable energy technologies and of energy saving measures is evaluated by imposing suitable constraints on CO₂ emissions and primary energy sources exploitation. The procedure is applied to the territory of Tamil Nadu state (India) by considering different energy planning scenarios.     

Micro-level energy planning in India—A case study of bangalore north Taluk

Demand for energy in India is constantly on the rise and the conventional supply options available have failed to cope with this increase. The emergence of efficiency improvement, carrier substitution and renewable energy as alternative sources of energy supply, make adherence only to macro-level energy planning unrealistic. A micro-level (district/taluk) energy planning becomes pragmatic under these circumstances to pursue the goal of sustainable development and to harness locally available energy resources. This paper considers the energy consumption pattern in Bangalore North taluk in 1987–88 and projects the demand for energy in 1995–96. Taking into account the different energy sources used to provide different end-use services through different end-use devices, the paper presents a linear programming formulation for optimum allocation. The model considers the conventional and new alternative technologies for meeting the demand for energy service. The results show that substantial savings could be achieved by this optimal allocation. The cost savings could be to the tune of Rs 41.879 million in Bangalore North taluk during 1995–96 (terminal year of Eighth Five-Year Plan). Energy savings of about 27% and cost savings of 16% could also be achieved.     

A multiobjective programming approach to energy resource allocation problems

The optimal allocation of energy resources to various energy end uses is an important strategy for bridging the energy supply and demand gap in India. It has been recognized that the allocation should be guided by multiple criteria. A multiobjective programming model for such an allocation process is presented in the paper. The normative model has been applied for the households sector of Madras city. The model is solved using non pre-emptive goal programming. Variations in the original model have been made to build alternative scenarios. The results of the original model and the alternative scenarios indicate that the use of solar thermal energy, natural gas, LPG, fuelwood, kerosene and lignite should be promoted for cooking, and the use of grid electricity and diesel, should be promoted for meeting water pumping demands. They favour the use of electricity generated from diesel for lighting, and the use of solar photovoltaics for meeting the electricity demands of household appliances. The results also indicate that grid electricity and electricity generated from fuelwood should be promoted to meet the demands of all the four household end uses, and point to the need for more research into solar photovoltaics, which may become competitive for meeting household demands in the future.     

Scheduling coupled photovoltaic,battery and conventional energy sources to maximize profit using linear programming

To address the increase of electricity demand, the need for reducing carbon dioxide, and the reduction of available fossil fuel resources, renewable energy sources are being recruited. Specifically energy generated by photovoltaic (PV) cells is becoming one of the most promising alternatives. In this context, this paper presents an optimization model for the scheduling problem where conventional and photovoltaic sources of energy are scheduled to be delivered to satisfy energy demand. The optimization model is formulated as a Linear Program (LP) with a bounded number of variables and constraints. The respective solution can be obtained in polynomial time and provides the optimal combination or schedule of energy generated from different sources (conventional, renewable and battery storage) such that the total demand is satisfied and the profit is maximized. Numerical results demonstrate the effectiveness and the generality of the scheme.     

Optimal dispatch of nearly-zero carbon integrated energy system considering waste incineration plant-carbon capture system and market mechanisms

Considering the waste incineration plant-carbon capture (WIP-CC) system and market mechanisms, the optimal dispatching strategy of nearly-zero carbon integrated energy system (NZC-IES) is proposed. The incineration of large amounts of Municipal Solid Waste can result in significant carbon emission and air pollution. However, lack of consideration of mechanisms for carbon processing and environmental protection will prevent the development of NZC-IES. Accordingly, a novel mixed integer nonlinear mathematical model, NZC-IES, is established that minimizes the total cost and controls carbon emission and air pollution. Unlike previous studies of optimal dispatching for WIP-CC system, the climate and health costs of WIP and the social costs of energy sources for CC are considered. A model of adjustable thermoelectric ratios for WIP and a model for CC with the storage tanks are established to enhance the flexibility of the thermoelectric output. Also, demand response model and ladder-type carbon trading model are developed to serve for NZC-IES. Case studies reveal that the proposed optimal dispatching strategy can realize the waste to energy utilization and low-carbon emission with economic performance.     

Development of hybrid energy system with cycle charging strategy using particle swarm optimization for a remote area in India

In recent years, renewable energy can be seen as one of the important prospect of today's research, as it is likely to enlighten the lives of millions of people by fulfilling demand of electricity in their daily life. The present work focuses on the development of optimal hybrid energy system sizing model based on comparative analysis of particle swarm optimization, genetic algorithm and Homer software for energy index ratio of 1. The model also incorporates renewable fraction, emissions of carbon di oxide from diesel generator, net present cost and cost of energy. The system is developed to supply the demand of 7 un-electrified villages of Dhauladevi block of Almora district in Uttarakhand, India with the help of the available resources of solar, hydro, biomass and biogas energy along with the addition of diesel generator, for meeting out the energy deficit. From the optimization results, minimum cost of energy and maximum renewable fraction are obtained as 5.77 Rs/kWh and 92.6% respectively.     

Advanced optimal planning for microgrid technologies including hydrogen and mobility at a real microgrid testbed

This paper investigates the optimal planning of microgrids including the hydrogen energy system through mixed-integer linear programming model. A real case study is analyzed by extending the only microgrid lab facility in Austria. The case study considers the hydrogen production via electrolysis, seasonal storage and fueling station for meeting the hydrogen fuel demand of fuel cell vehicles, busses and trucks. The optimization is performed relative to two different reference cases which satisfy the mobility demand by diesel fuel and utility electricity based hydrogen fuel production respectively. The key results indicate that the low emission hydrogen mobility framework is achieved by high share of renewable energy sources and seasonal hydrogen storage in the microgrid. The investment optimization scenarios provide at least 66% and at most 99% carbon emission savings at increased costs of 30% and 100% respectively relative to the costs of the diesel reference case (current situation).     

Effect of wind energy system performance on optimal renewable energy model-an analysis

The Optimal Renewable Energy Model (OREM) has been developed to determine the optimum level of renewable energy sources utilisation in India for the year 2020–21. The model aims at minimising costefficiency ratio and determines the optimum allocation of different renewable energy sources for various end-uses. The extent of social acceptance level, potential limit, demand and reliability will decide the renewable energy distribution pattern and are hence used as constraints in the model. In this paper, the performance and reliability of wind energy system and its effects on OREM model has been analysed. The demonstration windfarm (4 MW) which is situated in Muppandal, a village in the southern part of India, has been selected for the study. The windfarm has 20 wind turbine machines of 200 KW capacity. The average technical availability, real availability and capacity factor have been analysed from 1991 to 1995 and they are found to be 94.1%, 76.4% and 25.5% respectively. The reliability factor of wind energy system is found to be 0.5 at 10,000 hours. The OREM model is analysed considering the above said factors for wind energy system, solar energy system and biomass energy systems. The model selects wind energy for pumping end-use to an extent of 0.3153×10¹⁵ KJ.     

The role of public policy in optimizing renewable energy development in the greater southern Appalachian mountains

This research presents a third component of a comprehensive decision support system for energy planning that allows for combining existing electricity generating capabilities with increased use of renewable energy sources. It focuses on energy planning at the regional level, and concentrates specifically on the greater southern Appalachian mountains of the eastern United States: a region that was chosen for analysis not only due to its heavy dependence on coal for electricity, but also because of its potential for increased use of wind and solar power. Previous research used a geographic information system (GIS) model for identifying renewable energy potential to provide input data for a multi-objective linear programming (MOLP) model to determine the optimal constrained mix of renewable energy sources and existing fossil fuel facilities by balancing annual generation costs against the corresponding greenhouse gas emissions. This new component of the system analyzes three potential public policies—renewable portfolio standard, carbon tax, and renewable energy production tax credit—that have been used to foster increased renewable energy usage. These policies require minor modifications to the MOLP model for implementation. The results of these policy cases were then analyzed to determine the impact that these policies have on generation cost and pollution emissions within the region.