Planning Of Power Plants Research Articles

Assessment of the Impacts of Spatial Water Resource Variability on Energy Planning in the Ganges River Basin under Climate Change Scenarios

Availability of water in the Ganges River basin has been recognized as a critical regional issue with a significant impact on drinking water supply, irrigation, as well as on industrial development, and ecosystem services in vast areas of South Asia. In addition, water availability is also strongly linked to energy security in the region. Hence, quantification of spatial availability of water resources is necessary to bolster reliable evaluation of the sustainability of future thermal power plants in the Ganges River basin. This study focuses on the risks facing existing and planned power plants regarding water availability, applying climate change scenarios at the sub-basin and district level up to 2050. For this purpose, this study develops an integrated assessment approach to quantify the water-energy nexus in four selected sub-basins of the Ganges, namely, Chambal, Damodar, Gandak, and Yamuna. The results of simulations using Soil and Water Assessment Tools (SWAT) showed that future water availability will increase significantly in the Chambal, Damodar, and Gandak sub-basins during the wet season, and will negligibly increase in the dry season, except for the Yamuna sub-basin, which is likely to experience a decrease in available water in both wet and dry seasons under the Representative Concentration Pathway (RCP) 8.5 scenario. Changes in the water supply-demand ratio, due to climate change, indicated that water-related risks for future power plants would reduce in the Chambal and Damodar sub-basins, as there would be sufficient water in the future. For 19 out of 23 districts in the Chambal sub-basin, climate change will have a moderate-positive to high-positive impact on reducing the water risk for power plants by 2050. In contrast, existing and future power plants in the Yamuna and Gandak sub-basins will face increasing water risks. The proposed new thermal power installations, particularly in the Gandak sub-basin, are likely to face serious water shortages, which will adversely affect the stability of their operations. These results will stimulate and guide future research work to optimize the water-energy nexus, and will inform development and planning organizations, energy planning organizations, as well as investors, concerning the spatial distribution of water risks for future power plants so that more accurate decisions can be made on the location of future power plants.

The future of coal investment, trade, and stranded assets

The future of coal investment, trade, and stranded assets

Closing the void in energy planning modeling: Integrating local realities

Closing the void in energy planning modeling: Integrating local realities

An interactive multi-criteria decision-making framework between a renewable power plant planner and the independent system operator

Providing efficient support mechanisms for renewable energy promotion has drawn much attention from researchers in the recent years. The connection of a new renewable power plant to the transmission system has impacts on different electricity market indices since the other strategic generation units change their behaviour in the new multi-agent environment. In this paper, as the main contribution to the previous literature, a combination of multi-criteria decision-making approach and multi-agent modelling technique is developed to obtain the maximum possible profits for an intended renewable generation plan and also direct the investment to be located in a way to improve electricity market indices besides supporting renewable energy promotion. Fuzzy Q-learning electricity market modelling approach in combination with the technique for order preference by similarity (TOPSIS) is used as a new decision support system for promotion of renewable energy for the first time in the literature. The proposed interactive multi-criteria decision-making framework between the independent system operator (ISO) and the renewable power plant planner provides a win-win situation that improve market indices while help the renewable power plant planning. The effectiveness of the proposed method is examined on the IEEE 30-bus test system and the results are discussed.

A machine-learning approach to predicting Africa’s electricity mix based on planned power plants and their chances of success

Energy scenarios, relying on wide-ranging assumptions about the future, do not always adequately reflect the lock-in risks caused by planned power-generation projects and the uncertainty around their chances of realization. In this study we built a machine-learning model that demonstrates high accuracy in predicting power-generation project failure and success using the largest dataset on historic and planned power plants available for Africa, combined with country-level characteristics. We found that the most relevant factors for successful commissioning of past projects are at plant level: capacity, fuel, ownership and connection type. We applied the trained model to predict the realization of the current project pipeline. Contrary to rapid transition scenarios, our results show that the share of non-hydro renewables in electricity generation is likely to remain below 10% in 2030, despite total generation more than doubling. These findings point to high carbon lock-in risks for Africa, unless a rapid decarbonization shock occurs leading to large-scale cancellation of the fossil fuel plants currently in the pipeline. Energy scenarios can sometimes miss lock-in caused by planned power plant projects and uncertainty around their realization. Here, Alova et al. build a machine-learning model that predicts Africa’s electricity generation mix in 2030 based on the commissioning chances of planned projects.

Attention mechanism for developing wind speed and solar irradiance forecasting models

This article presents the Recurrent Neural Network (RNN) and its Attention mechanism to develop forecasting models for renewable energy applications. In this study, wind speed and solar irradiance forecasting models have been developed as these two factors play a significant role in renewable energy production. The irregular nature of wind poses the challenge of accurate wind speed prediction, while solar irradiance forecasting can aid in the planning and deployment of solar power plants. In this paper, six RNN techniques, namely RNN, GRU, LSTM, Content-based Attention, Luong Attention, and Self-Attention based RNN are considered for forecasting the future values of wind speed and solar irradiance in particular geographical locations. The aim is the identification of the advantages, comparison, and importance of different recurrent neural network methods for forecasting models. All models are developed on the datasets of the National Renewable Energy Laboratory (NREL) and NASA’s Prediction of Worldwide Energy Resource (POWER).

Localizing the Water-Energy Nexus: The Relationship between Solar Thermal Power Plants and Future Developments in Local Water Demand

Water availability plays an important role in the expansion planning of utility-scale solar power plants, especially in the arid regions of the Middle East and North Africa. Although these power plants usually account for only a small fraction of local water demand, competition for water resources between communities, farmers, companies, and power suppliers is already emerging and is likely to intensify in future. Despite this, to date there has been a lack of comprehensive studies analyzing interdependencies and potential conflicts between energy and water at local level. This study addresses this research gap and examines the linkages between water resources and energy technologies at local level based on a case study conducted in Ouarzazate, Morocco, where one of the largest solar power complexes in the world was recently completed. To better understand the challenges faced by the region in light of increased water demand and diminishing water supply, a mixed-method research design was applied to integrate the knowledge of local stakeholders through a series of workshops. In a first step, regional socio-economic water demand scenarios were developed and, in a second step, water saving measures to avoid critical development pathways were systematically evaluated using a participatory multi-criteria evaluation approach. The results are a set of water demand scenarios for the region and a preferential ranking of water saving measures that could be drawn upon to support decision-making relating to energy and water development in the region.

A Branch-and-Price-and-Cut Algorithm for the Cable-Routing Problem in Solar Power Plants

A solar power plant is a large-scale photovoltaic (PV) system designed to supply usable solar power to the electricity grid. Building a solar power plant needs consideration of arrangements of several important components, such as PV arrays, solar inverters, combiner boxes, cables, and other electrical accessories. The design of solar power plants is very complex because of various optimization parameters and design regulations. In this study, we address the cable-routing problem arising in the planning of large-scale solar power plants, which aims to determine the partition of the PV arrays, the location of combiner boxes, and cable routing such that the installation cost of the cables connecting the components is minimized. We formulate the problem as a mathematical programming problem, which can be viewed as a generalized capacitated minimum spanning tree (CMST) problem, and then devise a branch-and-price-and-cut (BPC) algorithm to solve it. The BPC algorithm uses two important valid inequalities, namely the capacity inequalities and the subset-row inequalities, to tighten the lower bounds. We also adopt several acceleration strategies to speed up the algorithm. Using real-world data sets, we show by numerical experiments that our BPC algorithm is superior to the typical manual-based planning approach used by many electric power planning companies. In addition, when solving the CMST problem with unitary demands, our algorithm is highly competitive compared with the best exact algorithm in the literature.

Matheuristics to optimize refueling and maintenance planning of nuclear power plants

Planning the maintenance of nuclear power plants is a complex optimization problem, involving a joint optimization of maintenance dates, fuel constraints and power production decisions. This paper investigates Mixed Integer Linear Programming (MILP) matheuristics for this problem, to tackle large size instances used in operations with a time scope of five years, and few restrictions with time window constraints for the latest maintenance operations. Several constructive matheuristics and a Variable Neighborhood Descent local search are designed. The matheuristics are shown to be accurately effective for medium and large size instances. The matheuristics give also results on the design of MILP formulations and neighborhoods for the problem. Contributions for the operational applications are also discussed. It is shown that the restriction of time windows, which was used to ease computations, induces large over-costs and that this restriction is not required anymore with the capabilities of matheuristics or local search to solve such size of instances. Our matheuristics can be extended to a bi-objective optimization extension with stability costs, for the monthly re-optimization of the maintenance planning in the real-life application.

Machine Learning-Guided Dual Heuristics and New Lower Bounds for the Refueling and Maintenance Planning Problem of Nuclear Power Plants

This paper studies the hybridization of Mixed Integer Programming (MIP) with dual heuristics and machine learning techniques, to provide dual bounds for a large scale optimization problem from an industrial application. The case study is the EURO/ROADEF Challenge 2010, to optimize the refueling and maintenance planning of nuclear power plants. Several MIP relaxations are presented to provide dual bounds computing smaller MIPs than the original problem. It is proven how to get dual bounds with scenario decomposition in the different 2-stage programming MILP formulations, with a selection of scenario guided by machine learning techniques. Several sets of dual bounds are computable, improving significantly the former best dual bounds of the literature and justifying the quality of the best primal solution known.

The future of coal-fired power generation in Southeast Asia

As both China and India are moving away from coal in the power sector, Southeast Asia has emerged as arguably the most important region for future growth in coal-fired power generation. Here we use data from the January 2017 edition of the Global Coal Plant Tracker to understand coal’s fortunes in the region. More specifically, we manipulate three key policy levers – attrition rate, capacity factor, and plant lifespan – to provide a timely update on the future of coal in Southeast Asia. Using improved estimates of the attrition rate for planned power plants and capacity factors in different countries, we find that status-quo trends in Southeast Asian coal-fired power generation would significantly hamper the region’s ability to contribute to global efforts to limit warming to 1.5 or 2.0 degrees Celsius in line with international agreements, namely the 2009 Copenhagen and 2016 Paris Agreements. Though meeting these targets may be possible if policymakers limit the entry into service of new coal power capacity and reduce use of existing units, qualitative evidence suggests that there may be a lack of political will to do so.

Economical and environmental impacts of decarbonisation of Indonesian power sector

Renewable energy has been prioritised in decarbonising Indonesia’s electricity system. Indonesia aims to attain an efficient energy system by applying renewable energy tariffs that are lower than the cost of fossil fuel-generated electricity. However, the effectiveness of this policy is questionable, as renewable energy investments under previous premium feed-in tariffs did not meet expectations. This study aims to estimate generation costs from renewable energy expansions under three scenarios, namely existing power plant planning, and 11% and 14% emission reductions in Indonesia’s electricity sector. We develop an agent-based model (ABM) tool called PowerGen-ABM that employs multi-approaches: linear programming and input-output analysis. The optimisation result shows that the emission reduction targets would increase the average electricity generation costs in 2028 from 65.3 USD/ MWh in the existing plan of power plant expansions to 68.3 USD/ MWh. The increased costs are caused by insufficient dispatchable renewables in several regions such as North Maluku. Renewable energy production share in total electricity production and emission reduction achievement of the existing plan in 2025 will be 22.8% and 6.5% below the targets of 23% and 11%, respectively. In contrast, the emission reduction scenarios could achieve those targets due to higher renewables productions, especially with wind energy from 5,268 GWh in the existing plan into anywhere between 64,472 to 75,085 GWh. Several policy implications are discussed based on these findings.

Committed emissions and the risk of stranded assets from power plants in Latin America and the Caribbean

Latin America and the Caribbean (LAC) has the least carbon-intensive electricity sector of any region in the world, as hydropower remains the largest source of electricity. But are current plans consistent with the international climate change goals laid out in the Paris Agreement? In this paper, we assess committed CO2 emissions from existing and planned power plants in LAC. Those are the carbon emissions that would result from the operation of fossil-fueled power plants during their typical lifetime. Committed emissions from existing power plants are close to 6.9 Gt of CO2. Building and operating all power plants that are announced, authorized, being procured, or under construction would result in 6.7 Gt of CO2 of additional commitments (for a total of 13.6 Gt of CO2). Committed emissions are above average IPCC assessments of cumulative emissions from power generation in LAC consistent with international temperature targets. To meet average carbon budgets from IPCC, 10%–16% of existing fossil-fueled power plants would need to be closed before the end of their technical lifespan. Our results suggest that building more fossil-fueled power plants in the region could jeopardize the achievement of the Paris Agreement temperature targets.

Comparison of predictive performance and measurement of off-grid 15 kWp solar power generation in Aceh Tamiang

In 2015, the Government, through a special allocation fund, has finished constructing an off-grid 15 KWp off-grid solar power plant in Paya Tampah Village, Aceh Tamiang district. The long-term reliability of solar power plants is highly reliant on on-site measurements because of the vagueness in some of the constantly-changing variables such as irradiation, drought rate, which possibly lead to cause significant power losses and which turn out to also affect the estimated cost of PV (kWh). With the aim of upgrading capabilities in off-grid solar power plant planning especially in the province of Aceh, this paper outlining detailed comparison of the results of calculating the performance of off-grid 15 KWp off-grid solar power among system performance measurement results after 2 years of operation. Performance calculations performed with the assistant of PVsyst software. The measurement results characterize the total power generated by the off-grid solar power plant is 15 kWp and used by 38 households. The result of this investigation is expected to be used by local governments to be a benchmark on the future planned off-grid solar power plant planning.

The Impacts of Emission Reduction Targets in Indonesia Electricity Systems

Climate change policy often contradicts the least-cost objective of electricity generation in developing countries. The objective of our study is to propose electricity generation mixes that can meet emission reduction targets in Indonesia. We estimate the optimal generation mix, costs, and emissions from three scenarios, namely existing power plant planning, and 11% and 14% emission reductions in Indonesia’s electricity sector. The estimations are based on linear programming, input-output analysis, and life-cycle analysis, integrated into an agent-based modeling (ABM) platform. The simulation results confirm the existing power plant planning, which is dominated by coal-based power plants, as the lowest-cost scenario in the short-term; however, this scenario also produces the highest emissions. Emission reduction scenarios have lower emissions due to a higher share of renewables and, therefore, the Indonesian electricity system is robust from fossil fuel price increases. In the long-term, costs incurred in the emission reduction scenarios will be lower than electricity generation costs under the existing power plant planning. Our findings should be a basis for re-evaluating energy policies, power plant planning, and the research agenda in Indonesia.
 Keyword: linear programming, agent-based modelling (ABM), input-output analysis, life-cycle analysis

Experimental decay-heat simulation-benchmark for 14 MeV neutrons & complex inventory analysis with FISPACT-II

Nuclear inventory simulations have a vital role to play in the planning and execution of future fusion experiments and power plants. They are able to predict the transmutation (burn-up) response of material compositions under neutron irradiation, thus providing information about the build-up of impurities that could impact on material performance. The inventory evolution also quantifies the radiological response of a material by tracing the production (and decay) rates of radioactive nuclides. This information can be used to plan maintenance schedules at nuclear facilities, satisfy nuclear regulators during reactor planning, and quantify the waste disposal needs during reactor decommissioning.However, the validity of inventory simulations must be verified to give confidence in the predictions. This paper describes a validation and verification (V&V) benchmark exercise that tests the quality of nuclear code predictions. Such benchmarks are an important part of the development and release of the FISPACT-II inventory code and its associated input nuclear data libraries. This paper describes the latest V&V based on the fusion decay-heat measurements performed at the Japanese FNS facility. Rigorous and detailed assessment techniques, focussed on the complex breakdown of decay-heat contributions from individual radionuclides, have been employed to interpret the simulated results, benchmark the data against the experimental measurements, and to compare results from different international nuclear data libraries. Example results are presented and discussed for nickel, iron, niobium, tungsten, chromium, and osmium, using FISPACT-II simulations performed with TENDL-2017, JEFF-3.3, ENDF/B-VIII.0, EAF2010, and IRDFF-1.05 nuclear cross section libraries.

Energy and exergy analysis of cold and power production from the geothermal reservoir of Torre Alfina

Geothermal power plants can provide clean and renewable energy and can be proposed as integrated units for simultaneous production of cooling and power. This paper presents a cascade arrangement of an organic Rankine cycle (ORC) and a water/lithium bromide (LiBr) absorption chiller (ABS). Starting from a literature reference layout which is taken as benchmark, some improvements are proposed at system level.To assess the performance of the system, a thermodynamic model is developed in EES and the energy and exergy balance is calculated. The proposed system is re-evaluated with reference to resource conditions corresponding to a planned power plant in central Italy, Torre Alfina (TA). A sensitivity analysis is performed in order to investigate the operating range of the plant and the possibility of adapting its design to the requirements of the customers. Under optimized conditions, the TA Case (targeted on a 5 MW power output) showed an energy utilization factor (EUF) of 46.2% and an exergy efficiency of 27.7%, neglecting the brine reinjection loss. The highest exergy destructions occur in the ORC economizer (8.6%), in the ABS generator (6.3%) and absorber (5.5%). The good resource conditions in TA case drive the design optimization to production of power rather than cold.

Integrated solar thermal combined cycle for power generation in Iraq

Solar thermal power has already shown enormous promise as a source for power generation. With limited environmental impacts and an immense abundance, it offers a reasonable prospect to the sunny countries such as Iraq, that enables the country to lead the development of industrial-scale solar power generation in the region. However, the electricity distribution systems would be confronted by daily and annual variance of solar irradiation. To compensate for this variance the integrated solar combined cycle (ISCC) with a natural gas power plant is the solution. This study investigates the potential of ISCC power plants in Iraq in which preferred technologies and economic feasibility are analysed. The analysis shows it is the best interest of Iraq to invest in at least one large-scale ISCC power plant among the future planned power plants. The most promising location for such a plant would be in Al Anbar, the immense western desert in Iraq, yet other six locations for such power plants have potential as well. The economic analysis showed a high capital cost for the concentrated solar power (CSP) plant compared to the natural gas (NG) plant, as the CSP formed 67% of the total capital investment, though, the operating and maintenance cost of CSP formed only about 5% of the total ISCC plant cost at $1,688,281 per year, and the direct operating and maintenance cost of CSP and NG plants were 34, and 164 $.kW−1, respectively.

Overview of the main topics in wind energy systems planning

The renewable energy production suffered very much in Romania, being practically stopped since a couple of years, because of legislative changes made “during the game”. Beside the main steps of wind power plant planning, identifying locations with good wind potential, the design of the wind park layout, choosing the best wind turbine type for the given locations, proximity to the transmission/distribution network and other technical features, the legislation in the electricity production from RES will have to ensure economical feasible support scheme, in a predictive legislative framework. The present paper presents possible scenarios for the development of the Romanian RES market at the horizon of 2030, strongly related to the direction that the policy transposes in the legislation.

Climate Change, Operating Flexibility, and Corporate Investment Decisions

Extreme temperatures lead to large fluctuations in electricity demand and wholesale prices of electricity, which in turn affects the optimal production process for firms to use. Using a large international sample of planned power plant projects, we measure the way that electric utilities’ investment decisions depend on the frequency of extreme temperatures. We find that they invest more in regions with more extreme temperatures. These investments are mostly in flexible gas and oil-fired power plants that can easily adjust their output to improve their operating flexibility. Our results suggest that climate change is becoming a meaningful factor affecting firms’ behavior.