Power Capacity Expansion Research Articles

Investigating the Structural and Power Performance of a 15 MW Class Wind Energy Generation System under Experimental Wind and Marine Loading

The global transition to renewables in response to climate change has largely been supported by the expansion of wind power capacity and improvements in turbine technology. This is being made possible mainly due to improvements in the design of highly efficient turbines exceeding a 10 MW rated power. Apart from power efficiency, wind turbines must withstand the mechanical stress caused by wind–hydro conditions. Such comprehensive structural analysis has rarely been performed previously, especially for large-scale wind turbines under real environmental conditions. The present work analyzes the energy production and structural performance of an NREL-IEA 15 MW wind turbine using measured wind and hydro data. First of all, an optimum operating range is determined in terms of the wind speed and blade pitch angle to maximize the power coefficient. Then, at this optimum range, a detailed breakdown of the forces and moments acting on different components of the wind turbine is presented. It was found that wind speeds of 9 to 12 m/s are best suited for this wind turbine, as the power coefficient is at its maximum and the mechanical loads on all components are at a minimum. The loads are at a minimum due to the optimized blade pitch angle. The bending force on a monopile foundation (fixed on the seabed) is found to be at a maximum and corresponds to nearly 2000 kN. The maximum blade force is nearly 700 kN, whereas on the tower it is almost 250 kN. The maximum force on the tower occurs at a point which is found to be undersea, whereas above-sea, the maximum force on the tower is nearly 20% less than the undersea maximum force. Finally, seasonal and annual energy production is also estimated using locally measured wind conditions.

Research on methods and scale of deeply exploitable hydropower resources

Deep development of hydropower is an important measure to optimize the structure of clean energy, meet the peak shaving and backup needs of the power system, and achieve the national dual carbon goals. This article conducts a systematic analysis of the development potential of developed and undeveloped hydraulic resources, including unplanned river sections, undeveloped planned power stations, unit renovation and capacity expansion, and the expansion of existing/ongoing power stations. We focused on studying the factors that affect the deep development of hydropower, including the reasonable operation mode of hydropower with different regulation performance in the power grid, the energy structure characteristics of the power system, and the comprehensive water use requirements. A calculation method for the reasonable installation and utilization hours and deep development resources of hydropower stations with different regulation performance after deep development has been proposed. According to calculations, Zhejiang Province can increase its scale by 3331MW, with great potential for deep development.

Evidence-Based Policymaking: Insights and Recommendations for the Implementation of Clean Energy Transition Pathways for Kenya’s Power Sector

With ambitious targets to drastically increase economic activity over the next decade, Kenya’s future is undoubtedly energy-intensive. Current power capacity expansion plans will see Kenya considerably ramp up fossil fuel generation, significantly increasing emissions. Therefore, Kenya is at a crucial stage of its national development, with critical decisions to make regarding its future power expansion and production. OSeMOSYS modelling software (clicSAND version v1.1) is employed to produce a series of possible clean energy transition pathways to increase renewable power production under rapidly intensifying demand. This study integrates existing national priorities and policies into six modelled scenarios to provide insights into their generation, total production, and costs, which can assist future policymaking and capacity-building efforts. The high-level insights gained in this research were employed to suggest key recommendations for Kenya’s power sector. Most notably, policy alignment, increased wind power production, energy-efficiency penetration, finance and investment securement, the development of storage technologies, power transmission, and distribution improvements should be prioritised.

THE CONCEPT OF TRANSFORMATION OF THE RETAIL ELECTRICITY MARKET IN THE CONTEXT OF DIGITAL TRANSFORMATION OF THE INDUSTRY

The market model of the Russian power industry that currently exists is facing trends and challenges, such as rising electricity prices for end-consumers, rising tariffs for power transmission services and sales mark-ups, the complexity and high cost of technological connection to power grids and capacity expansion, as well as increase in cross-subsidisation.At the same time, the energy industry is influenced by external factors caused by a number of technological changes that have matured in Russia and in the world, such as the transition from electric energy production at large power plants to the use of distributed generators (including those using renewable energy sources (RES), an increase in the final price on electricity due to growth in demand, as well as the transformation of the spread and reduction in the cost of generator techniques with the use of renewable energy sources, technologies for energy storage systems, as well as the development of smart metering systems.In turn, cutting-edge technologies of the electric power industry not only lead to positive economic effects when implemented separately, they reveal the fullest range of effects and lead to the emergence of new models of interaction in the retail market if their interaction is coordinated by Industry 4.0 digital technologies. Thus, the combined influence of digital transition technologies in the electric power industry forms the prerequisites for the emergence of a new type of consumer of electric power – an active consumer.The purpose of this study is to analyse the factors influencing the adoption of these technologies by retail market entities and the effectiveness of their implementation, as well as the formation of a target retail market concept that takes into account the introduction of active consumers and has the potential to create direct and indirect economic effects for energy industry entities.

Sustainable Power Grid Expansion: Life Cycle Assessment, Modeling Approaches, Challenges, and Opportunities

Electric demand is steadily increasing, hence requiring continuous investments in modernizing, and expanding power grids worldwide. Traditionally, power system planning projects have considered minimizing the costs of capacity expansion and minimizing the amount of energy not served as the main objectives. With climate change policies enforcing the decommissioning of fossil-fuel-based generation, new clean and renewable generation technologies are being considered for power system capacity expansion projects. However, the environmental impacts of energy resources are not limited to carbon emissions and their contribution to global warming. In fact, every power generation technology can result in undesired impacts during its entire life cycle, which could negatively affect air quality, water resources, material resources, and/or human health. This paper provides an overview of how to assess the sustainability of power systems and power generation technologies based on life cycle assessment (LCA). A review of LCA, as applied to power systems and generation technologies, is presented with a discussion of general findings, challenges, and limitations. A review of the literature is then provided related to how sustainability objectives are currently incorporated in power grid design and capacity expansion models. Finally, shortcomings of the current models are discussed, along with opportunities for future research.

Probabilistic wind power expansion planning of bundled wind-thermal generation system with retrofitted coal-fired plants using load transfer optimization

The bundled wind-thermal generation system (BWTGS) is an attractive option to integrate wind energy to actively participate in power systems. With the greatly enhanced penetration of wind energy in BWTGS, wind curtailment against sharp wind power fluctuations is inevitable. Thus, our study is stimulated by whether and to what extent we can maximize wind power penetration with proper provision of dispatchable loads against wind curtailment, and this is exactly the problem we aim to solve in this paper. Here, we initially focus on the flexible characteristics of retrofitted coal-fired units in BWTGS and the load transfer optimization (LTO) for high voltage distribution networks (HVDNs). Then, an appropriate probabilistic wind power capacity expansion planning method for BWTGS with retrofitted coal-fired units using LTO can be reformulated as a mixed-integer second-order cone programming problem. Moreover, since load and wind power data have inherited uncertainty, we extend this proposed model to be a probabilistic expanding model. This contributes to better decision-making for the short-term wind capacity expansion under quantified uncertainty. Numerical results of the practical HVDNs demonstrate the effectiveness of the proposed method.

Sports Smart Data Writing Based on New-Type Semiconductor Nonvolatile Storage Mode

The development of smart sports has just taken a new chapter. Based on the digital transformation in all aspects today, the data content of smart sports with a large number of branches is huge. However, the current storage system, an external storage disk, has not adapted to the computer’s increasingly high requirements for IO performance; DRAM- and SRAM-based memory and cache also face power consumption and capacity expansion problems; the storage system on the overall performance of the computer system constraints is increasingly prominent. Based on the data storage of smart sports for digital transformation, this article proposes a smart for the writing of sports data; a better cache method has been explored under the new semiconductor nonvolatile cache mode. The experimental results in this article show that by comparing single-value and dual-value STT-RAM (spin-torque transfer RAM) caches, the three-valued cache with hierarchical mapping of page swap design has a significant improvement in IPC performance under each test load, compared with single-value STT-RAM caches. Compared with the value type, the average performance has increased by 16%; the overall energy consumption including memory has been reduced by an average of 12.3%.

India’s Strategy for Achieving Net Zero

This paper reviews and assesses India’s energy policy in the context of its commitment to achieve the target of net-zero carbon emissions by 2070. It emphasizes the central role of green electrification, particularly through the expansion of solar power capacity. It discusses policies outside the energy sector that need to be part of a strategy of achieving this target, including the use of information technology, infrastructure development, and transportation. Furthermore, it examines possible policy options for accelerating the target date to 2050, especially with the use of carbon capture to manage the transition from the current heavy use of coal. The paper also discusses the possible financial and growth implications of various strategy options, arguing that feasible external financing commitments can allow India to achieve net-zero goals without sacrificing economic growth.

Regional power system transitions towards carbon neutrality: The case of North China

China has announced its goal of reaching carbon neutrality by 2060, which will have a profound impact on its energy and economic systems. During this process, the power sector will play a key role in helping the country on its road towards carbon neutrality. This study develops a multi-regional power dispatch and capacity expansion model to combine long-term capacity expansion with short-term power dispatch. The model is built to optimise the carbon-neutral transition pathway from the perspective of economics, focussing on power system stability and reliability under high penetrations of renewables. Using the case study of North China, the cost-effective methods for power system transitions under the targets of reaching peak emissions by 2030 and achieving carbon neutrality by 2060 are discussed under different scenarios. We find that the future power supply system will rely heavily on renewable energy which will account for more than 89% of power generation. Inner Mongolia will produce more than 84% of power in the North China region. The inter-regional power transmission capacity will be five times higher than its current level. Policy choices will lead to different transition pathways as the early-stage installation decisions will have a lock-in effect on the following carbon emission reduction strategies and determine the importance of technologies adopted in achieving carbon neutrality. Although the transition to a low-carbon economy will result in an increase in cost by 17–19%, it will bring huge climate benefits to the world.

A coupled non-deterministic optimization and mixed-level factorial analysis model for power generation expansion planning – A case study of Jing-Jin-Ji metropolitan region, China

In this study, a coupled non-deterministic optimization and mixed-level factorial analysis model (NOMFA) is proposed for supporting power generation expansion planning. By integrating interval-parameter programming, multistage stochastic programming and mixed-level factorial analysis within a general system optimization framework, this model can not only help determine the optimized schemes for power generation and capacity expansion under various uncertainties, but also reflect dynamic variations of system conditions; moreover, it can take into account the effects of external interferences and their interactions on the system outputs in the decision-making process. A case study of Jing-Jin-Ji (JJJ) metropolitan region is provided to demonstrate the effectiveness of the proposed approach. The results indicate that although renewable technologies are becoming increasingly significant, electricity generated by fossil fuels would still dominateJJJ’s power systems during the planning horizon. It is also found that the import price of electricity is the most influential factor on both total system cost and total CO2 emissions; meanwhile, the interaction between the import price of electricity and the price of gas impacts CO2 emissions to a certain extent. It is expected that the modeling results will provide solid bases for formulating power generation expansion plans for JJJ Metropolitan Region.

Development of a Chance-Constrained Dual-Objective Fractional Programming for Shandong’s Clean Power Transition

In this study, an inexact mixed-integer interval stochastic fractional model (IMSFP) is developed for Shandong’s sustainable power system management under uncertainties. Shandong has a high proportion of fossil-fuel power, which has resulted in significant greenhouse gas emissions. Future is an essential period for energy structure transition. Developed IMSFP can effectively tackle dual objective, system efficiency represented as output/input ratios, as well as uncertainties described as interval values and probability distributions in the constraints and objectives. The results indicate that the clean power transition and capacity expansion scheme are sensitive to different constraint-violation risk levels. Obtained interval solutions can provide flexible strategies for resource allocation and expansion capacities under multiple complexities. An economic single objective model (IMCLP) is also developed, which aims at minimizing the system cost. The comparative results illustrate that the IMSFP model can better characterize the real-world power system problems through optimizing a ratio between clean energy utilization and system cost. Biomass and wind power would be major developed electricity forms in the future, and solar energy has great development potential. In short, the proposed IMSFP model is advantageous in balancing conflicting dual objectives and reflecting complicated interactions among system efficiency, economic cost, system reliability, and constraint-violation scenarios.

The Expanding Coal Power Fleet in Southeast Asia: Implications for Future CO2 Emissions and Electricity Generation

AbstractCoal combustion for power generation made up 30% of global CO2 emissions in 2018. To achieve the goal of the Paris Agreement to keep global average temperatures below 2°C, power generation must be decarbonized globally by mid‐century. This requires a rapid phase‐out of coal‐fired power generation. However, global coal power expansion continues, mostly in developing countries where electricity demand continues to increase. Since the early 2010s, Southeast Asia's coal power capacity expansion has been among the fastest in the world, following China and India, but its implications for the global climate and regional energy transition remain understudied. Here we examine Southeast Asia's power generation pipeline as of mid‐2020 and evaluate its implications for the region's CO2 emissions over the plant lifetime as well as projected electricity generation between 2020‐2030 in Indonesia, Vietnam, and the Philippines. We find that power plants under construction and planned in Southeast Asia as of 2020 will more than double the region's fossil fuel power generation capacity. If all fossil fuel plants under development are built, Southeast Asia's power sector CO2 emissions will increase by 72% from 2020 to 2030 and long‐term committed emissions will double. Moreover, in Indonesia, Vietnam, and the Philippines, projected electricity generation from fossil fuel plants under development, combined with generation from renewable capacity targets and existing power capacity, will exceed future national electricity demand. As a result, fossil fuel plants will likely be underutilized and/or become stranded assets while also potentially crowding out renewable energy deployment.

GIS-based optimization \u2013 achieving Austria\u2019s 2030 wind energy target

In this paper, we take a look at Austria’s renewable energy targets established in the Renewable Energy Expansion Act (EAG), aiming to annually generate an additional 10 TWh of wind power by 2030. We conduct a GIS (geographic information system)-based analysis to determine average wind power density in Austria on a cellular level while considering prohibited regions, such as national parks, where building wind turbines might not be allowed. The calculated expansion potential for all remaining regions of Austria is allocated to the closest corresponding transmission nodes. Furthermore, we suggest an optimization algorithm to geographically distribute the expansion of wind power capacity to applicable transmission nodes. Finally, we conduct a case study to validate the algorithm using historical data on expansion and utilize it to predict an annual scenario for wind power expansion from 2021 to 2030 on a regional level. The total expansion required to achieve the goal of 10 TWh is assessed to be 4 GW based on predefined full load hours while assuming an exponential increase in annually added capacity (from 250 MW in 2021 to 590 MW in 2030).

Distributed neural network enhanced power generation strategy of large-scale wind power plant for power expansion

Power generation is an important energy conversion process. A prominent feature of wind power plant compared with traditional power plants is that the equipment utilization has great intermittency and uncertainty. Fortunately, with the popularity of converter-based wind turbines, doubly-fed induction generator wind power plants are able to not only employ wind turbines in converting wind power to electrical power, but also use the converter capacity to produce reactive power. Traditionally, PQ curves considering current constraints are used for active and reactive power distribution, and therein the fixed maximum slip in power control is conservative for the utility of converters. To fully extract the power generation capacity, a novel power control realizing adaptive variation of maximum slip is proposed for each wind turbine to dynamically expand the reactive power capacity in the high active power region. In the meanwhile, the dispatch scheme of the wind power plant based on the conventional PQ curve has to be upgraded accordingly. Given that the unfixed maximum slip makes it impossible to obtain the PQ curve with explicit expression, an online learning method based on neural network is adopted and further developed in a distributed architecture to be consistent with natural distributed characteristics of the large-scale wind power plant. With the help of communication among agents through consensus protocol, the algorithm convergence can be guaranteed even under distinct information fragments. Case studies demonstrate that the proposed scheme is able to achieve up to 30% expansion of reactive power capacity and 16.3% voltage sag alleviation under certain conditions.

A chance-constrained small modular reactor siting model -- a case study for the Province of Saskatchewan, Canada

In this study, a chance-constrained small modular reactor siting (CCSS) model has been first proposed for planning regional electric power systems in the Province of Saskatchewan under the pressure of greenhouse gas emission mitigation. Through incorporating three programming methods (interval linear programming, chance-constrained programming, and mixed-integer programming) in an optimization framework, the CCSS model could effectively deal with multiple uncertainties expressed as probability distribution and intervals in constraints and objectives. Since locations of retired coal-fired power stations have been proposed as new sites for small modular reactor (SMR), this model could provide the construction planning for SMR in terms of its sizes and sites. Various solutions to power generation and capacity expansion with different risk levels were obtained under the objective of minimal system costs. Results are helpful for identifying optimized strategies to increase the proportion of renewable energy under emission constraints. In addition, model without introducing the technology of SMR has been comprehensively compared with the CCSS model to recognise the effects of SMR on system costs and greenhouse gas emissions. Results indicate that the CCSS model is effective for supporting long-term clean and renewable energy utilization as well as emission reduction policy formulation in Saskatchewan.

Inserting socio-environmental variables in the power sector expansion planning

Energy models consider technical and economic criteria to define the optimal energy mix to produce electricity. However, the results are sub-optimal from the social point of view, since they do not include socio-environmental factors. The purpose of this study is to access the sustainability of power plants considering technical-economic, socio-environmental and institutional aspects so that to improve the expansion planning. Thus, the efficiencies calculated by slacks based measure approach of Data Envelopment Analysis methodology can be implemented as constraints in the models, signalizing that the socio-environmental impact cannot exceed a limit, which can be calculated by the energy model itself. The results show that renewable power plants contribute more to allocative efficiency than fossil fuel ones, both in terms of social and environmental aspects, which are of utmost importance in ranking power plants to model power capacity expansion. Considering just technical and economic factors has proved to be insufficient to optimize the system in terms of allocative efficiency.

Research to develop the next generation of electric power capacity expansion tools: What would address the needs of planners?

Close coordination between generation and transmission operations and planning is critical to cost effective and reliable energy production and delivery; such coordination, in the presence of ownership diversity, is indeed a primary and challenging goal of regional transmission organizations in the US and similar organizations worldwide. Optimizing these sectors separately overlooks potential synergies that may allow for more effective design and operation of power systems. Coordinated expansion planning (CEP), where both generation and transmission decisions are coordinated, has become especially relevant to present day planning and operations. There are various reasons for this, some of which include the desire to obtain the most environmental and economic benefit from deeper penetration of renewable energy sources, the need for effective deployment of emerging storage technologies, opportunities to capture and harness the electrification of the transport sector, increased interdependencies with other sectors (e.g., gas), and accommodating increased shares of distributed energy resources in distribution grids. These changes result in increased short-term and long-term uncertainties, as well as an increased need for improved representation of multiscale temporal and spatial dynamics (e.g., representing hourly or sub-hourly intertemporal couplings in multi-decadal expansion models). The purpose of this work is to characterize the state-of-the-art in CEP models and identify technical challenges of grid development planning and research and development (R&D) needs for the new generation of these CEP models.

The impact of clean development mechanism on energy-water-carbon nexus optimization in Hebei, China: A hierarchical model based discussion

Clean development mechanism (CDM) is an important principle in CO2 emissions mitigation since it was proposed. However, there will be a long way for the large-scale application of CDM in China, for thermal power is still in the dominant position, while renewable power is far from being competitive. In this study, a mix integer bi-level hierarchical programming model (MIBLHP) is developed to investigate the potential impact of the CDM application on the regional energy-water-carbon nexus optimization in electric power system in China. The model integrates the advantages of bi-level programming and mix integer liner programming in dealing with conflicting objectives with hierarchical and sequential decision making process and help to achieve the optimized strategies with the highest overall system satisfaction degree. Results show that CDM will play an important role in the regional energy-water-carbon nexus optimization, the MIBLHP is able to stabilize the increasing trend of system cost and make better tradeoffs between the economic and environmental goals. Optimal strategies including power generation and capacity expansion path, system cost control, CO2 emissions abatement strategies, water consumption and waste water discharge are presented. The system would reach the supreme satisfaction degrees (λ = 0.934) when the CO2 reduction level is 40%, which means that every part of the system has achieved the best condition. The proposed model has great potential in dealing with similar regional planning problems in energy-water-carbon nexus optimization.

A comparative study of time aggregation techniques in relation to power capacity expansion modeling

In this paper, we studied the aggregation techniques for power capacity expansion problems. Combining a growing demand for green energy with a hard constraint on demand satisfaction causes system flexibility to be a major challenge in designing a stable energy system. To determine both the need for flexibility and which technologies that could satisfy these needs at minimum cost, the system should be analyzed on an hour-by-hour scale for a long period of time. This often leads to computationally intractable problems. One way of getting more tractable models is to aggregate the time domain. Many different aggregation techniques have been developed, all with a common goal of selecting representative time slices to be used instead of the full time scale, gaining a problem size reduction in the number of variables and/or constraints. The art of aggregation is to balance the computational difficulty against the solution quality, making validation of the techniques crucial. We propose new aggregation techniques and compare these to each other and to a selection of aggregation techniques from the literature. We validate the aggregated problems against the non-aggregated problems and look into the sensitivity of the performance of the aggregation techniques to different data sets and to the selection of different element types. Our analysis shows that aggregation techniques can be used to achieve very good solutions in a short amount of time, and that simple aggregation techniques achieve good performance similar to that of techniques with higher complexity. Even though the aggregation techniques in this paper are applied to power capacity expansion models, the methodology can be used for other problems with similar time dependence, and we believe that results in agreement with the results seen here, would be achieved.

Assessing the effect of transmission constraints on wind power expansion in northeast Brazil

The rapid growth of the wind industry in Brazil presents new opportunities and challenges for its electricity industry. As new capacity becomes available, more transmission infrastructure is required for security of supply. The Brazilian electricity system has experienced congestion in the northeast region, affecting the coordinated expansion of transmission and new wind power capacity. This paper uses a simulation model for better assessing long-term policy synchrony of the wind industry. The simulation shows that the resulting prices are competitive in the middle to long terms.