Electricity Production Units Research Articles

Security-constrained unit commitment: A decomposition approach embodying Kron reduction

We address the day-ahead scheduling of electricity production units throughout a network imposing N-1 security constraints, which ensures uneventful operation under any single-branch failure. For realistic electric energy systems, this optimization problem, which is mixed-integer linear or nonlinear but convex, involves millions of continuous variables, millions of constraints, and thousands of binary variables. This problem is intractable if state-of-the-art branch-and-cut solvers are used. As a solution methodology, we propose a Benders-type decomposition technique with a dynamically enriched master problem. Such master problem incorporates scheduling (binary) decisions and decisions pertaining to under-contingency operating conditions. The subproblems represent the operation of the system under no failure and single-branch failure. As the algorithm progresses, the master problem incorporates additional under-contingency operating conditions, which increases its computational burden. We use Kron reduction to compact (reducing variables and constraints) the description of the under-contingency operating conditions in the master problem without losing accuracy, which renders major computational gains. The methodology proposed allows solving, within reasonable computing times, instances intractable with state-of-the-art branch-and-cut solvers and decomposition algorithms.

Long-Term Electricity Load Forecasting Using Artificial Neural Network: The Case Study of Benin

Africans in general and specially Beninese’s low rate access to electricity requires efforts to set up new electricity production units. To satistfy the needs, it is therefore very important to have a prior knowledge of the electrical load. In this context, knowing the right need for the electrical energy to be extracted from the Beninese network in the long term and in order to better plan its stability and reliability, a forecast of this electrical load is then necessary. The study has used the annual power grid peak demand data from 2001 to 2020 to develop, train and validate the models. The electrical load peaks until 2030 are estimated as the output value. This article evaluates three algorithms of a method used in artificial neural networks (ANN) to predict electricity consumption, which is the Multilayer Perceptron (MLP) with backpropagation. To ensure stable and accurate predictions, an evaluation approach using mean square error (MSE) and correlation coefficient (R) has been used. The results have proved that the data predicted by the Bayesian regulation variant of the Multilayer Perceptron (MLP), is very close to the real data during the training and the learning of these algorithms. The validated model has developed high generalization capabilities with insignificant prediction deviations.

Carbon Capture Performance Assessment Applied to Combined Cycle Gas Turbine Under Part-Load Operation

Abstract The growing share of renewable energies in our electricity production, together with the still lacking storage capacity, strongly reinforces the need for more flexible electricity production units. In this context, combined cycle gas turbines (CCGTs) have a role to play, both in the current and future electricity production system due to their high efficiency, high load flexibility, and low CO2 emissions compared to other conventional thermal power plants. Nevertheless, bearing in mind our current challenges concerning climate change, the CO2 emissions of these CCGTs need to be reduced drastically. The amine-based absorption carbon capture (CC) process is currently the most mature and applicable CC technology. This process is known to require a considerable amount of thermal energy, degrading plant performance. However, to back-up renewable production, CCGTs will operate most of the time under part-load conditions. The impact of these part-load operations on the CC is still relatively unknown. Within this framework, this study aims to assess the performance of the CC process applied to a typical CCGT under part-load operation using specific simulation models. The CC plant model has been successfully validated against experimental data from a pilot-scale capture facility. Then, the CC plant has been scaled-up to the CCGT scale and the process has been optimized for each operating condition. The simulation results show that the specific reboiler duty increases for part-load operation, while the specific cooling requirements decrease. Moreover, the analysis of the yearly CCGT operation highlights a relative increase in CC energy penalty of 21% for an annual CCGT load factor of 0.5, impacting significantly plant performance. The next step will involve reducing this energy penalty.

Appraisal of Available Transfer Capability Determination Methods in Competitive Electricity Market

The existing power market is contentiously promoting sustainability and competitiveness in the electricity industry. It has raised the transmission networks' transfer capacity as an emerging area for researchers. The electricity production units and consumers share the same transmission network. All stakeholders try to generate power from cheaper sources to make more significant profit margins. This situation creates transmission congestion, violation of voltage and thermal limits, and system security threats. The accurate measurement and optimal use of the available transfer capability (ATC) shall resolve this issue up to some extent. Thus, efficient evaluation of ATC is needed to ensure system security, and it provides an open and trending research topic. This article presents a review of the various techniques for ATC determination and provides the characteristics, practices, and features of the methods. This analysis demonstrates the issues of ATC evaluation techniques and provides the research areas to the researchers.

Cumulative Environmental Effects of Hydropower Stations Based on the Water Footprint Method—Yalong River Basin, China

The construction of hydropower stations is not without controversy as they have a certain degree of impact on the ecological environment. Moreover, the water footprint and its cumulative effects on the environment (The relationship between the degree of hydropower development and utilization in the basin and the environment) of the development and utilization of cascade hydropower stations are incompletely understood. In this paper, we calculate the evaporated water footprint (EWF, water evaporated from reservoirs) and the product water footprint of hydropower stations (PWF, water consumption per unit of electricity production), and the blue water scarcity (BWS, the ratio of the total blue water footprint to blue water availability) based on data from 19 selected hydropower stations in the Yalong River Basin, China. Results show that: (a) the EWFs in established, ongoing, proposed, and planning phases of 19 hydropower stations are 243, 123, 59, and 42 Mm3, respectively; (b) the PWF of 19 hydropower stations varies between 0.01 and 4.49 m3GJ−1, and the average PWF is 1.20 m3GJ−1. These values are quite small when compared with hydropower stations in other basins in the world, and the difference in PWF among different hydropower stations is mainly derived from energy efficiency factor; (c) all the BWS in the Yalong River Basin are below 100% (low blue water scarcity), in which the total blue water footprint is less than 20% of the natural flow, and environmental flow requirements are met. From the perspective of the water footprint method, the cumulative environmental effects of hydropower development and utilization in the Yalong River Basin will not affect the local environmental flow requirements.

Life Cycle Assessment of the New Generation GT-MHR Nuclear Power Plant

This study describes a life cycle assessment (LCA) of a fourth generation (4G) nuclear power plant. A high temperature helium cooled reactor and gas turbine technology with modular helium reactor (GT-MHR) is used in this study as an example. This is currently one the safest design of a nuclear power plant. The study also takes into account impact of accidents and incidents (AI) which happened around the world at nuclear power generation facilities. The adopted method for the study is a hybrid LCA analysis. The analysis of each phase of the life cycle was done on the basis of process chain analysis (PCA). Where detailed data were not available, the Input/Output (I/O) databases was employed. The obtained results show that greenhouse gases (GHG) emissions and energy intensity per unit of electricity production are relatively low. In fact, these are even lower than emissions from a number of renewable energy sources. The results show considerably different greenhouse gases (GHG) emissions and energy intensity per unit of electricity production when effects of AI are taken into account.

Numerical analysis of downhole heat exchanger designed for geothermal energy production

A traditional way of geothermal energy production implies hot brine extraction from the reservoir to a surface facility. This method is economically profitable only for high enthalpy reservoirs associated with shallow depths and high temperatures. The low enthalpy geothermal (LEG) projects are out of consideration due to the high cost of produced electric power related to expensive drilling, cost of the binary cycle system, and long term installation cost. This paper presents a new zero mass extraction method utilizing downhole heat exchanger (DHE) with no geo-fluid production to the surface. The well design stays as a single horizontal well with coupled production and injection sections. The brine pump is located between the sections providing hot brine circulation through the DHE. The coupled fluid flow and heat transfer mathematical model was developed and simulated using nodal analysis method. The LEG reservoir prototype located in South Louisiana was used to study several cases of the DHE lengths, inclination angles, reservoir permeabilities, and flow rates optimization. According to the analysis, the power unit is able to provide 160 kW of net electric power with CO2 working fluid circulating inside a single lateral well. Increasing the reservoir temperature and the number of laterals, the available power production rises up to 600 kW with an attractive electricity cost of $21.84/MWh at 7000 m well depth. This methods opens a new prospective for the depleted petroleum wells converting them to the electricity production units.

Advanced Functional Materials: Intrinsic and Doped Silicon Oxide

ABSTRACTThe unique properties of silicon oxide materials, no matter intrinsic or doped, utilized in thin film solar cells (TFSCs) in the area of photovoltaic (PV) are making TFSCs one of the most attractive photovoltaic technologies for the development of high-performing electricity production units to be integrated in everyday life. In comparison to other silicon materials, the particular diphasic structure of silicon oxide materials, in which hydrogenated microcrystalline silicon (μc-Si:H) crystallites are surrounded by an oxygen-rich hydrogenated amorphous silicon (a-Si:H) phase, causes them present excellent photoelectrical material properties, such as a low-parasitic absorption in the broadband spectral range, independent controllability of longitudinal and lateral conductivity, refractive indices (3.5-2.0), band gap (2.0-2.6 eV) and conductivity tenability (with orders of 1-10-9S/cm) with oxygen doping, and so on. Various types of silicon oxide materials, including intrinsic, p- or n- type, further applied in TFSCs have also played significant roles in improving the efficiency of various types of single-, dual-, and triple-junction thin-film solar cells from both the optical and electrical points of view. In this paper, we present our latest progress in studying the performance improvement role of intrinsic or doped silicon oxide materials in pin-type a-Si:H, a-SiGe:H, and μc-Si:H single-junction solar cells. By effectively tuning the band gap values of intrinsic a-SiOx:H materials with oxygen doping and adopting the layers with a suitable band gap (1.86 eV) as the P/I buffer layers of a-Si:H solar cells fabricated on metal organic chemical vapor deposition (MOCVD) boron-doped zinc oxide (ZnO:B) substrates, a significant Vocincreases up to 909 mV and an excellent external quantum efficiency (EQE) response of 75% at the 400 nm typical wavelength can be achieved by matching the band gap discontinuity between the p-type nc-SiOx:H window and a-Si:H intrinsic layers. The serious leakage current characteristics of pin-type narrow-gap (Eg<1.5 eV) a-SiGe:H single-junction solar cells can also be finely tuned by integrating an n-type μc-SiOx:H layer with a small oxygen content in addition to improving the long-wavelength response, an effective approach gives rise to the highest FF of 70.62% for pin-type a-SiGe:H single-junction solar cells with an average band gap of 1.48 eV. In addition, our studies proved that the application of p-type μc-SiOx:H window layers in μc-Si:H single-junction solar cells can effectively improve the short-wavelength light coupling by suppressing the parasitic absorption and promoting the anti-reflectivity with a graded refractive index profile. On the basis of the optimum single-junction solar cells with omnipotent silicon oxide materials, an initial efficiency of 16.07% has been achieved for pin-type a-Si:H/a-SiGe:H/μc-Si:H triple-junction solar cells with an active area of 0.25 cm2. The omnipotent properties of silicon oxide layers in TFSCs, including effective optical coupling and trapping, suitability in compensating for the band gap discontinuity, the shunt-quenching capacity, and so on, make them likely to be extended to other types of solar cells such as polycrystalline chalcopyrite Cu(In,Ga)Se2(CIGS) and perovskite-sensitized solar cells, opening up new opportunities for acquiring solar cells with higher performance.

Model Predictive Control Strategies for Low-Voltage Microgrids

Modern concepts for grids are being developed owing to the increasing stress put on the electrical producers and the burgeoning need for thermal demands due to varying climate conditions. The increasing awareness of the depletion of natural resources has also been a driver in the direction of modern grids which are more efficient and less susceptible to failures. One solution which is being considered as an alternative for the existing centralised electrical grid and thermal needs is the concept of microgrid. Microgrids are decentralised subunits which consist of different cogeneration units and combined heat and power units along with renewable sources and heat and electricity consumers. Multiple microgrids combine together to aid and complement the existing producers thus, reducing the stress on these producers while increasing the efficiency of the entire electricity grid. The presence of different storage devices and electricity production units mean that the microgrids can also maintain very high quality of electricity, thus reducing losses during transmission and distribution. Another advantage of a microgrid is it's ability of being self-sufficient for a short period time which means that microgrids have the ability to island themselves voluntarily or accidentally from the rest of the electricity grid which could be used to prevent entire grid failures during meteorological problems or when the economic factors are not conducive. The presence of decentralised sources, storage devices and consumers mean that there is a strong need for control of all the different components that constitute a microgrid. There are many different approaches to control a microgrid. This paper attempts to demonstrate a model predictive control (MPC) strategy developed to provide strategies for a low-voltage microgrid with both thermal and electrical devices and requirements. The aim of the strategy is to satisfy the demands and comforts of the consumers while minimising the costs associated by performing a multi-layered mixed-integer optimisation which provides strategy that needs to be employed by each unit of the system. The optimisation consists of two layers, the first of which provides the setpoints for both the thermal and electrical components and the second layer solves a problem which is only takes the electrical units into account and is performed over a shorter timestep. This is owing to the different dynamics of the thermal and electrical units. The fast dynamics of the electrical systems requires us to perform the second layer to maintain the efficiency of the microgrid. The paper then shows the importance of optimal sizing for the model predictive control strategy employed for varying periods of the year and the advantages of this strategy for microgrids over the existing strategies. The paper also attempts to show the impact of certain variables employed in the MPC strategy in particular, for LV (Low-Voltage) microgrid.

A Comparative Environmental Evaluation of the Coal and Natural Gas Life Cycle

Natural gas and coal are the main primary energy resources used in the Romanian energy sector, 73.7% in 2011, taking into account the fuel imports. The objective of the article consists in analyzing all the processes along the coal and the natural gas life cycle in order to assess their overall environmental impact. Two energy technologies were analyzed, for each resource: the pulverized coal combustion with supra-critical parameters and CO2 capture unit and the natural gas combined cycle. Considering the functional unit of electricity production for 1 year, it was found that the natural gas combined cycle remains the more interesting energy technology from an environmental point of view. However, the pulverized coal with supra-critical parameters equipped with a CO2 capture unit has the lowest environmental impact on the climate change. The weakest point of the coal technology is its low efficiency.

Economic feasibility of biomass gasification for power generation in three selected communities of northwestern Ontario, Canada

Biomass gasification is expected to be an attractive option among other competitive applications of biomass conversion for bio-energy. This study analyzes economic feasibility of biomass gasification power generating plants in three selected communities (Ignace, Nipigon and Kenora) of northwestern Ontario. The major variables considered in the model are harvesting and handling costs, logistic costs for biomass feedstock delivery and storage, capital costs of power plant by scales, operation and maintenance costs, labor costs, capital financing costs and other regulatory costs. GIS analysis was undertaken to estimate the distance class matrix to apportion the biomass feedstock supply side from different forest management units. Total cost per MWh power production at a 50MW scale ranges from CAD 61.89 to CAD 63.79. Total cost per unit of electricity production decreases significantly as plant capacity increases due to economy of scale in the production system. Further, the locations of plants explained the cost variability.

Job retention in the British offshore sector through greening of the North Sea energy industry

For the case of the UK there are currently three ways of obtaining energy from sea areas, namely from wind, tides and waves. A methodology was developed to determine the future size of the offshore renewable industry based on the concept of employment factor, or the number of people required to maintain each unit of electricity production. An assessment was made of the decline in the number of people employed in oil related jobs in the North Sea and the gap that this could create in the UK's economy unless this pool of offshore expertise could find an alternative employment in the renewable sector. The paper will also investigate the effect of gradually transforming the UK's oil and gas sector into offshore renewables. If this was to happen by 2050 the UK offshore renewable industry could produce between 127 and 146TWh of electricity, equivalent to around 57–66% of the current energy consumption in the country.