Regional Electricity Generation Research Articles

Analysis of China's regional thermal electricity generation and CO2 emissions: Decomposition based on the generalized Divisia index

Even though Chinese government has been promoting the development of renewable energy, coal-fired thermal electricity generation still accounts for nearly 70% of the total electricity generation, proving to be the largest carbon dioxide (CO2) emitter in China. Uncovering the driving forces of CO2 emissions, thus, is of great significance to provide appropriate mitigation policies for the sustainable development of China's thermal electricity generation. In this regard, this study aims to fill a research gap by applying Index Decomposition Analysis (IDA) approach via the Generalized Divisia Index Model (GDIM) to examine the driving factors behind the CO2 emission changes in China's thermal electricity generation during 2000–2016. The decomposition results indicate that the factors contributing to the growth in CO2 emission can be ranked as follows: economic activity (52.0%), electricity demand (45.8%) and energy use (36.2%), whereas factors suppressing the growth in the mission are carbon intensity change (−17.7%), technology (−11.3%), energy mix (−2.4%), energy efficiency (−1.7%) and electricity efficiency (−0.9%). Noteworthy, the promoting effect of the economic activity varied little with time, whereas that of electricity demand and energy use exhibited a downward trend in general. Besides, though the progress in technology contributed a lot to the decrease of CO2 emission, its decreasing effects tended to diminish since 2013. Northeast and East regions appeared as those contributing to the mitigation of the CO2 emissions from China's thermal electricity generation, whereas the North and Northwest regions exerted a lag to the abatement of CO2 emission.

Incentivizing sustainable development: The impact of a recent policy reform on electricity production efficiency in China

AbstractChina's rapid economic growth has tremendously accelerated its energy use, calling for a more sustainable supply of scarce and nonrenewable energy. Using a firm‐level dataset of 30 major Chinese electricity utilities from 2010 to 2014, this paper applies a stochastic frontier analysis to determine the utilities' technical efficiency, incorporating their operational environments related to a recent policy reform to encourage sustainable development. Our main findings are (a) state ownership, consumer density, and a chief executive officer with a science and engineering background are factors that can improve technical efficiency; (b) asset‐related subsidy increases efficiency whereas income‐related subsidy lowers efficiency; and (c) the five largest regional electricity generation firms exhibit above‐average efficiency levels. These results provide evidence that supports the recent Chinese policy reform. The findings also suggest that electricity generation efficiency, which is essential to sustainable economic development, can be improved through performance‐based regulation and incentives.

Quantifying the geographical distribution effect on decreasing aggregated nitrogen oxides intensity in the Chinese electrical generation system

Over the past 20 years, the spatial distribution of electrical generation and its relationship to cross-regional power transmission has impacted China's power generation system and significantly affected the total amount of NO x and the aggregated nitrogen oxides intensity (ANI) of the system. An investigation of the driving mechanisms of ANI that considers the unevenness of regional electricity generation will be crucial to future improvements in the NO x efficiency of the electrical generation system in China. In this study, we built a decomposition model for ANI by incorporating the spatial distribution of electrical generation and found that the spatial distribution of electricity generation together with energy-related factors gradually caused decreases in ANI. The efficiency of electricity generation presented the dominant inhibitory effect on ANI, but its effect size has weakened since 2010. In contrast, the fossil fuel structure of thermal power shows an increasingly positive effect on changes in ANI. The primary energy composition only slightly affected changes in ANI. Moreover, the changed geographical distribution of electricity generation is non-negligible and has a positive effect on reduction of the ANI of the Chinese electrical generation system. The transferred amount of local NO x emissions by cross-provincial electricity transmission, however, could cause lead to additional environmental costs for generators. This issue should receive more attention in the future.

Evaluation of Future Water Use for Electricity Generation under Different Energy Development Scenarios in China

Water scarcity and uneven water demand in regional electricity generation pose substantial challenges to the sustainable development of water resources and electricity production in China. Based on the latest official policy of China’s electricity development, i.e., the 13th Five-Year Plan of electricity development, this study quantified annual water withdrawal and consumption for future electricity generation in China from 2015 to 2030. This study simulated a three-prong approach to impacting water use for electricity development, i.e., updating the cooling technology mix, increasing non-thermal power generation and relocating thermal power plants to the west. The results showed that solutions to relieve water stress caused by electricity production entail major trade-offs. Annual water withdrawal and consumption were projected to exceed 63.75 and 8.30 billion m3 by 2030, up approximately 14% and 21% of those in 2015, respectively, if China does not implement any new water and energy policies. Replacing once-through cooling systems with closed-loop cooling systems would decrease national water withdrawal remarkably but increase water consumption. The west-centered spatial distribution of thermoelectric power generation would reduce water use at the national level; however, it will largely increase water stress in northern and northwestern China. Thus, relieving the stress of growing electricity demand on water resources in China requires comprehensive measures and quantitative estimates.

Improving the flexibility of coal-fired power generators: Impact on the composition of a cost-optimal electricity system

A transformation of the electricity generation system is required to drastically reduce the associated CO2 emissions. In future systems, variable renewable energy sources (wind and solar) are expected to provide a significant fraction of the electricity supply, increasing the requirement for variation management compared with today’s situation. This paper investigates the impacts of measures designed to increase the competitiveness of coal-fired power plants in future energy systems, which are facing restrictions related to CO2 emissions and variable operation as a consequence of high penetration levels of wind and solar power. We investigate the cost-optimal compositions of three regional electricity generation systems with different conditions for generation using renewables with a linear cost-minimizing investment model. The model is applied in two energy policy scenarios: one with a tight cap on CO2 emissions, and one with a stringent requirement for generation from renewables.In a system with a stringent requirement for electricity generation from renewables but without a CO2 cap, coal-based technologies with improved cycling properties provide variation management, given that the development of measures for ensuring improved flexibility continues and reaches full-scale implementation at moderate cost. The effects of improved cycling properties on the system composition are especially relevant for regions with moderate potential for wind and solar generation, in that they reduce wind curtailment and improve the underlying conditions for investments in solar power. In the system with a tight CO2 cap, only coal-based technologies with Carbon Capture and Storage (CCS) and co-firing of biomass are feasible. Increasing the share of biomass in co-firing technologies to achieve negative CO2 emissions increases the competitiveness of these technologies to a greater extent than if simply the cycling properties are improved. A larger co-firing fraction reduces the total system costs, since it facilitates the provision of low-cost flexibility by Natural Gas Combined Cycle (NGCC) plants, and it is especially important in regions where nuclear power is otherwise cost-competitive, as low-cost flexibility stimulates investments in wind and solar power at the expense of nuclear power.

Explicit cost-risk tradeoff for renewable portfolio standard constrained regional power system expansion: A case study of Guangdong Province, China

In this paper, a risk explicit interval two-stage programming (REITSP) model was proposed for supporting the regional electricity generation expansion with renewable portfolio standard (RPS) constraint. It could effectively tackle multiple uncertainties expressed as interval numbers. But unlike the traditional interval two-stage programming model, the proposed REITSP model could provide an explicit trade-off information between system cost and risk for decision makers with different risk preferences. It could minimize the total system cost, as well as the decision risk according to the aspiration risk level of decision maker. The developed REITSP model was applied to the case study in Guangdong Province, China for its long-term electricity system planning. Crisp solutions under different aspiration risk levels for varying RPS targets were obtained and analyzed. The results showed that according to the current available renewable energy and affordable construction speed, the maximum RPS target for Guangzhou Province during 2016–2025 should be 17%. Higher RPS level would promote the renewable energy generation, especially solar power; meanwhile, it would reduce the CO2 emission and the imported electricity, but with greater investment cost. The obtained results and trade-off information would be valuable for the optimal long-term electricity system expansion planning when facing future uncertain situation.

Impact of thermal plant cycling on the cost-optimal composition of a regional electricity generation system

A regional cost-minimizing investment model that accounts for cycling properties (i.e., start-up time, minimum load level, start-up cost and emissions, and part-load costs and emissions) is developed to investigate the impact of thermal plant cycling on the cost-optimal composition of a regional electricity generation system. The model is applied to an electricity system that is rich in wind resources with and without accounting for cycling in two scenarios: one with favorable conditions for flexible bio-based generation (Bio scenario); and one in which base load is favored (Base load scenario) owing to high prices for biomass. Both scenarios are subject to a tight cap on carbon dioxide emissions, limiting the investment options to technologies that have low or no carbon emissions.We report that in the Bio scenario, the cost-optimal system is dominated by wind power and flexible bio-based generation, whereas base-load generation dominates the Base load scenario, in line with the assumptions made, and the level of wind power is reduced. In the Base load scenario, 19% of the capacity is cycling-dependent, i.e., for this share of installed capacity, the choice of technology is different if cycling properties are included, compared to a case in which they are omitted. In the Bio scenario, in which flexible bio-based generation is less costly, 9% of the capacity is cycling-dependent. We conclude that it is critical to include cycling properties in investment modeling, to assess investments in thermal generation technologies that compete at utilization times in the range of 2000–5000 h.

Evaluating financial risk management strategies under climate change for hydropower producers on the Great Lakes

AbstractHydropower on the Great Lakes makes up a substantial fraction of regional electricity generation capacity. Hydropower producers on the Niagara River (flowing between lakes Erie and Ontario) operate as run‐of‐river, and changing lake levels alter interlake flows reducing both generation and revenues. Index‐based insurance contracts, wherein contract payouts are linked to lake levels, offer a tool for mitigating this risk. As a potentially useful tool, pricing of financial insurance is typically based on historical behavior of the index. However, uncertainty with respect to the impacts of climate change on lake level behavior and how this might translate to increased (or decreased) risk for those selling or buying the insurance remains unexplored. Portfolios of binary index‐insurance contracts are developed for hydropower producers on the Niagara River, and their performance is evaluated under a range of climate scenarios. Climate Informed Decision Analysis is used to inform the sensitivity of these portfolios to potential shifts in long‐term, climatological variations in water level behavior. Under historical conditions, hydropower producers can use portfolios costing 0.5% of mean revenues to increase their minimum revenue threshold by approximately 18%. However, a one standard deviation decrease in the 50 year mean water level potentially doubles the frequency with which these portfolios would underperform from the perspective of a potential insurer. Trade‐offs between portfolio cost and the frequency of underperformance are investigated over a range of climate futures.

The Photovoltaic Giant: Lights and Shadows of Energy Planning in Apulia

Climate change and transition towards sustainability represent global challenges. The urgency of abandoning traditional fossil fuels, on one hand, and the progressive devolution in the European context of energy legislative powers to subnational polities, on the other hand, make the regional energy planning process a subject worth studying. Energy planning is one of the most important tools for addressing climate change-related issues on both small and large scales, and it particularly tests the ability to decide of local governances. Under a newly established political class, in 2007, the Italian region of Apulia provided itself with an energy plan that attracted much attention nationally, for both its ambitious objectives and the inclusive decisional process put in place before and after its approval. This paper aims to assess suspected inconsistencies in this decision-making process and to verify two selected plan objectives. The methodology chosen for this study includes a mostly quantitative collection of statistical data and an analysis of policies and strategy documents. The findings show that 1) during the decision-making there had been a lack of rationality in translating the advice of the actors’ network into plan objectives; 2) the micro-generation of photovoltaic power was an unmet objective; and 3) the enhanced share of photovoltaic power over the total regional electricity generation in 2013 had been widely over-achieved and produced one major consequence – the extensive coverage of mainly agricultural, regional land.

Decomposition analysis of carbon dioxide emissions in China's regional thermal electricity generation, 2000–2020

A major share of China's total carbon dioxide (CO2) emissions is from the electric power sector. In 2010, almost 40% of all CO2 emissions in China were from this sector. This is because the country predominantly depends on thermal electricity generation to meet its power requirement. This study analyses the CO2 emissions from thermal electricity generation in China between 2000 and 2012 at the regional grid level. Logarithmic Mean Divisia Index methodology is employed to identify the factors that influence changes in CO2 emissions over time. This study also predicts China's energy consumption and CO2 emissions patterns between 2013 and 2020, forecasting rates of increase in energy consumption across six regional grids from 0.9% to 9.7%. CO2 emissions related to thermal electricity generation increased from 981.33 million tons (Mt) in 2000 to 3342.79 Mt in 2012, which is an annual growth rate of 10.75%. These increases are not aligned with China's commitments on reducing emissions to the Asia-Pacific Economic Cooperation forum. China's CO2 emissions are forecasted to increase to 5596 Mt by 2020 if the current increasing trends is not effectively curbed after 2012.

Regional electricity generation and employment in UK regions

ABSTRACTRegional electricity generation and employment in UK regions. Regional Studies. A number of electricity-generation technologies reduce carbon emissions, but with different economic and employment effects, partly consequent on how far generation capacity supports regional supply chains. In devolved regions these issues are important because of the role given to renewable electricity generation in economic development strategies. This paper analyses the regional employment supported by different electricity-generation technologies, illustrating trade-offs between generation scale and employment intensity, and shows that the regional employment supported under all pathways is modest compared with the regional economic scale. The policy implications are investigated.

The Photovoltaic Giant: Lights and Shadows of Energy Planning in Apulia

Climate change and transition towards sustainability represent global challenges. The urgency of abandoning traditional fossil fuels, on one hand, and the progressive devolution in the European context of energy legislative powers to subnational polities, on the other hand, make the regional energy planning process a subject worth studying. Energy planning is one of the most important tools for addressing climate change-related issues on both small and large scales, and it particularly tests the ability to decide of local governances. Under a newly established political class, in 2007, the Italian region of Apulia provided itself with an energy plan that attracted much attention nationally, for both its ambitious objectives and the inclusive decisional process put in place before and after its approval. This paper aims to assess suspected inconsistencies in this decision-making process and to verify two selected plan objectives. The methodology chosen for this study includes a mostly quantitative collection of statistical data and an analysis of policies and strategy documents. The findings show that 1) during the decision-making there had been a lack of rationality in translating the advice of the actors’ network into plan objectives; 2) the micro-generation of photovoltaic power was an unmet objective; and 3) the enhanced share of photovoltaic power over the total regional electricity generation in 2013 had been widely over-achieved and produced one major consequence – the extensive coverage of mainly agricultural, regional land.

Robust response to hydro-climatic change in electricity generation planning

An electricity generation planning framework incorporating adaptation to hydro-climatic change is presented. The planning framework internalizes risks and opportunities associated with alternative hydro-climate scenarios to identify a long-term system configuration robust to uncertainty. The implications of a robust response to hydro-climatic change are demonstrated for the electricity system in British Columbia (BC), Canada. Adaptation strategy is crucial in this region, mainly due to the large contribution of hydropower resources to regional electricity supply. Analysis of results from basin-scale hydrologic models driven with downscaled global climate data suggest that shifts in regional streamflow characteristics by the year 2050 are likely to increase BC’s annual hydropower potential by more than 10 %. These effects combined with an estimated decrease in electricity demand by 2 % due to warmer temperatures, could provide an additional 11 TWh of annual energy. Uncertainties in these projected climate impacts indicate technology configurations offering significant long-term operational flexibility will be needed to ensure system reliability. Results from the regional long-term electricity generation model incorporating adaptive capacity show the significant shifts required in the non-hydro capacity mix to ensure system robustness cause an increase in cumulative operating costs of between 1 and 7 %. Analysis of technology configurations involving high-penetrations of wind generation highlights interactions between flexibility requirements occurring over multiple temporal scales.

Carbon dioxide emissions and energy efficiency analysis of China's regional thermal electricity generation

Abstract In this study, we analyze the energy efficiency and carbon dioxide (CO 2 ) emissions reduction of China's regional thermal electricity generation from 2004 to 2010 on a regional grid level. China has seven regional grids. By using Log Mean Divisia Index (LMDI) analysis, we determine that the intensity and energy mix effects have positive effects on CO 2 emissions reduction, but the effects of structural and CO 2 emissions factor are insignificant. The accumulated intensity and energy mix effects account for −10.70% and −10.41% of the CO 2 emissions reduction, respectively. Such improvements are brought about the policy that encourages large power plants, discourages small energy-inefficient power plants, and promotes large-capacity high-parameter units. This policy has contributed to electricity conservation in thermal electricity generation. However, regional results show that the North grid of China performed poorly. Hockey stick effects were also observed in the intensity effect of the Central and North grids. Thus, regional moving average targets are recommended.

Electric Urban Delivery Trucks: Energy Use, Greenhouse Gas Emissions, and Cost-Effectiveness

We compare electric and diesel urban delivery trucks in terms of life-cycle energy consumption, greenhouse gas (GHG) emissions, and total cost of ownership (TCO). The relative benefits of electric trucks depend heavily on vehicle efficiency associated with drive cycle, diesel fuel price, travel demand, electric drive battery replacement and price, electricity generation and transmission efficiency, electric truck recharging infrastructure, and purchase price. For a drive cycle with frequent stops and low average speed such as the New York City Cycle (NYCC), electric trucks emit 42-61% less GHGs and consume 32-54% less energy than diesel trucks, depending upon vehicle efficiency cases. Over an array of possible conditions, the median TCO of electric trucks is 22% less than that of diesel trucks on the NYCC. For a drive cycle with less frequent stops and high average speed such as the City-Suburban Heavy Vehicle Cycle (CSHVC), electric trucks emit 19-43% less GHGs and consume 5-34% less energy, but cost 1% more than diesel counterparts. Considering current and projected U.S. regional electricity generation mixes, for the baseline case, the energy use and GHG emissions ratios of electric to diesel trucks range from 48 to 82% and 25 to 89%, respectively.

Linking electricity and water models to assess electricity choices at water-relevant scales

Hydrology/water management and electricity generation projections have been modeled separately, but there has been little effort in intentionally and explicitly linking the two sides of the water–energy nexus. This paper describes a platform for assessing power plant cooling water withdrawals and consumption under different electricity pathways at geographic and time scales appropriate for both electricity and hydrology/water management. This platform uses estimates of regional electricity generation by the Regional Energy Deployment System (ReEDS) as input to a hydrologic and water management model—the Water Evaluation and Planning (WEAP) system. In WEAP, this electricity use represents thermoelectric cooling water withdrawals and consumption within the broader, regional water resource context. Here we describe linking the electricity and water models, including translating electricity generation results from ReEDS-relevant geographies to the water-relevant geographies of WEAP. The result of this analysis is water use by the electric sector at the regional watershed level, which is used to examine the water resource implications of these electricity pathways.

A stochastic-fuzzy programming model with soften constraints for electricity generation planning with greenhouse-gas abatement

SUMMARY Increased atmospheric CO2 concentration is widely being considered as the main driving factor that causes the phenomenon of global warming, due to the ever-boosting use of fossil fuels. In this study, a fuzzy-stochastic programming model with soft constraints (FSP-SC) is developed for electricity generation planning and greenhouse gas (GHG) abatement in an environment with imprecise and probabilistic information. The developed FSP-SC is applied to a case study of long-term planning of a regional electricity generation system, where integer programming technique is employed to facilitate dynamic analysis for capacity expansion within a multi-period context to satisfy increasing electricity demand. The results indicate different relaxation levels can lead to changed electricity generation options, capacity expansion schemes, system costs, and GHG emissions. Several sensitivity analyses are also conducted to demonstrate that relaxation of different constraints have different effects on system cost and GHG emission. Tradeoffs among system costs, resource availabilities, GHG emissions, and electricity-shortage risks can also be tackled with the relaxation levels for the objective and constraints. Copyright © 2012 John Wiley & Sons, Ltd.

The regional electricity generation mix in Scotland: A portfolio selection approach incorporating marine technologies

Standalone levelised cost assessments of electricity supply options miss an important contribution that renewable and non-fossil fuel technologies can make to the electricity portfolio: that of reducing the variability of electricity costs, and their potentially damaging impact upon economic activity. Portfolio theory applications to the electricity generation mix have shown that renewable technologies, their costs being largely uncorrelated with non-renewable technologies, can offer such benefits. We look at the existing Scottish generation mix and examine drivers of changes out to 2020. We assess recent scenarios for the Scottish generation mix in 2020 against mean-variance efficient portfolios of electricity-generating technologies. Each of the scenarios studied implies a portfolio cost of electricity that is between 22% and 38% higher than the portfolio cost of electricity in 2007. These scenarios prove to be mean-variance “inefficient” in the sense that, for example, lower variance portfolios can be obtained without increasing portfolio costs, typically by expanding the share of renewables. As part of extensive sensitivity analysis, we find that Wave and Tidal technologies can contribute to lower risk electricity portfolios, while not increasing portfolio cost.

A complex systems approach to planning, optimization and decision making for energy networks

This paper explores a new approach to planning and optimization of energy networks, using a mix of global optimization and agent-based modeling tools. This approach takes account of techno-economic, environmental and social criteria, and engages explicitly with inherent network complexity in terms of the autonomous decision-making capability of individual agents within the network, who may choose not to act as economic rationalists. This is an important consideration from the standpoint of meeting sustainable development goals. The approach attempts to set targets for energy planning, by determining preferred network development pathways through multi-objective optimization. The viability of such plans is then explored through agent-based models. The combined approach is demonstrated for a case study of regional electricity generation in South Africa, with biomass as feedstock.

Transmission capacity and market power: the effect on a dominant generation firm

We examine the effect of transmission on a firm's market power by analyzing the optimal dispatch for a dominant firm in a short-run regional electricity generation market. Application of our research to Colorado shows that the dominant firm can strategically congest transmission into the region to receive a maximum price 54.9% of the time. When it does not get the maximum price, the dominant firm can receive an 11.6% average markup over the competitive price. We show how the threat of entry across an enhanced transmission grid can increase competition and limit market power in Colorado.