Generation Portfolio Research Articles

An approach to evaluating the impact of contaminants on flux deposition in gas turbines

AbstractGas turbines are a key part of many countries’ power generation portfolios, but components such as blades can suffer from hot corrosion attack, which can decrease component lifetimes. Corrosion is driven by impurity levels in the fuel and air (e.g., species containing sulphur and/or alkali metals) and depends on environmental conditions (e.g., air pollution, seawater droplets), that can lead to formation of harmful species in the gas. Understanding and determining the deposition flux of such contaminants is crucial for understanding the problem. Thermodynamic simulations were used to determine types and amounts of potentially corrosive contaminants, this was followed by deposition fluxes calculations. An operating scenario, based upon an offshore platform was evaluated. The effectiveness of different filtration systems has been evaluated. The impurity levels of alkali metals, such as sodium, greatly impacts the calculated deposition flux of species linked to corrosion attack. The presence of Na2SO4, and K2SO4 was found, at temperature representative of stage 2 nozzle guide vanes. Lowering sulphur input (from fuel or air) can be an efficient way to decrease deposition, attention must also be paid to lowering the amount of alkali metal entering the gas turbine, which can be lowered by the filtration systems’ correct use.

Power sector effects of green hydrogen production in Germany

Power sector effects of green hydrogen production in Germany

Renewable energy communities optimal design supported by an optimization model for investment in PV/wind capacity and renewable electricity sharing

Renewable energy communities optimal design supported by an optimization model for investment in PV/wind capacity and renewable electricity sharing

Optimal coordinated generation scheduling considering day‐ahead PV and wind power forecast uncertainty

AbstractEconomic operation and reliable supply‐demand balance are problems of paramount importance in power grids with a massive share of intermittent renewable energy sources (RESs) of great interest. This paper sought an optimal coordinated generation scheduling for day‐ahead power system operation considering RESs and energy storage units. Renewable power generation, particularly, wind and photovoltaic are uncontrollable, whereas can be predicted using forecasting models. Within the proposed framework, a hyperparameter‐optimized long short‐term memory (LSTM) regression model is employed to forecast the day‐ahead weather from the historical time‐series weather data. Eventually, an empirical formula is used to estimate the power conversion from the day‐ahead weather forecasts for a selected PV module and wind turbine. The objective of the scheduling framework is to keep a delicate supply‐demand balance at the lowest possible cost of generation while maintaining the prevailing generation and system constraints. A variance measure uncertainty handling‐based grey wolf optimizer (GWO) technique is used to find the optimal day‐ahead generation schedules and dispatches under RESs forecast uncertainty. The proposed generation scheduling framework is examined on the IEEE 6 and 30‐bus systems. In the studied scenarios, the coordinated operation of generators can decrease the total day‐ahead operating cost for the modified IEEE 6‐bus system by 2.57% compared to supplying electricity generation with conventional generators alone. Likewise, the total operating cost from the coordinated operation of all generation portfolios was reduced by 6.93% from the operating cost of generation during base case simulation (supply only from dispatchable thermal units) on the modified IEEE 30‐bus system. Moreover, the case studies show that coordinated generation scheduling can mitigate the RESs power variability problem, provide secure supply‐demand operation, and minimize the operating cost of electricity generation.

Scenario generation and risk-averse stochastic portfolio optimization applied to offshore renewable energy technologies

Scenario generation and risk-averse stochastic portfolio optimization applied to offshore renewable energy technologies

A framework to assess multi-hazard physical climate risk for power generation projects from publicly-accessible sources

Demand for information about physical climate risk is growing, particularly for the power generation sector, given its size and pronounced exposure to climate hazards. However, quantifying physical climate risks for a large number of assets remains challenging. Here we introduce a scalable and transparent methodology that enables multi-hazard physical climate risk assessments for any thermal or hydro power generation project. The methodology relies on basic power plant type and geolocation data inputs, publicly-available climate datasets, and hazard- and technology-specific vulnerability factors, to translate hazard severity into generation losses. We apply the methodology to the European Bank for Reconstruction and Development’s early 2021 thermal and hydro power generation portfolios of 80 assets. We show that under the Representative Concentration Pathway 4.5 scenario, those 80 power plants could experience a 4.0-10.9 TWh loss in annual generation (or 1.87-5.07% of total annual maximum generation) by 2030 compared to its baseline losses of 0.70–0.87 TWh (or 0.33–0.41%). One of the largest drivers of the increased risk is rising water temperatures, which is currently overlooked by mainstream climate risk disclosure guidelines.

A stochastic-robust optimization model for inter-regional power system planning

A stochastic-robust optimization model for inter-regional power system planning

A multi-stage stochastic programming model for the unit commitment of conventional and virtual power plants bidding in the day-ahead and ancillary services markets

A multi-stage stochastic programming model for the unit commitment of conventional and virtual power plants bidding in the day-ahead and ancillary services markets

Adoption of electric vehicles in the transport sector: effect on net emission reduction

It is estimated that the transport sector contributes to 17-18% of greenhouse gas emissions in Australia. Out of which, 63% of these toxic emissions are originating from the internal combustion engine (ICE) based light vehicles. These poisonous emissions negatively impact on the environment, climate, and health and are responsible for asthma, cancer, bronchitis, and sometimes premature death. Governments and policymakers are setting up policy directives and subsidies to replace the ICE-based vehicles with electric vehicles (EVs). The adoption of EVs will shift the energy demand from the transport sector to the electricity sector. As the electricity sector of some countries (e.g., Australia) is still heavily depending on the fossil-fuel based sources, it is argued that by simply adopting EVs in the transport sector would not make a profound impact on the net reduction in carbon emissions. In this paper, we investigated the impact on net carbon emission reduction due to adoption of EVs using the transport and electricity generation data in Australia. We have investigated four case studies by applying various modelling assumptions based on the type of EV, percentage of ICE-based vehicles converting into EVs, and renewable energy generation projections. Finally, study has made some carbon emission projections for the transport sector till 2050 considering a constant conversion rate for ICE vehicles to EVs, annual uptake of renewable energy into the power grid, and increase in number of EVs. With the present rate of adoption of renewable energy into the power generation portfolio, it would take at least another 8-10 years to make a profound impact on emission reduction in the transport sector, and by 2050 a 75% emission reduction can be achieved compared with the current emission levels in the transport sector.

Decarbonization Analysis for Thermal Generation and Regionally Integrated Large-Scale Renewables Based on Minutely Optimal Dispatch with a Kentucky Case Study

Decarbonization of existing electricity generation portfolios with large-scale renewable resources, such as wind and solar photo-voltaic (PV) facilities, is important for a transition to a sustainable energy future. This paper proposes an ultra-fast optimization method for economic dispatch of firm thermal generation using high granularity, one minute resolution load, wind, and solar PV data to more accurately capture the effects of variable renewable energy (VRE). Load-generation imbalance and operational cost are minimized in a multi-objective clustered economic dispatch problem with various generation portfolios, realistic generator flexibility, and increasing levels of VRE integration. The economic feasibility of thermal dispatch scenarios is evaluated through a proposed method of levelized cost of energy (LCOE) for clustered generation portfolios. Effective renewable economics is applied to assess resource adequacy, annual carbon emissions, renewable capacity factor, over generation, and cost to build between thermal dispatch scenarios with incremental increases in VRE penetration. Solar PV and wind generation temporally complement one another in the region studied, and the combination of the two is beneficial to renewable energy integration. Furthermore, replacing older coal units with cleaner and agile natural gas units increases renewable hosting capacity and provides further pathways to decarbonization. Minute-based chronological simulations enable the assessment of renewable effectiveness related to weather-related variability and of complementary technologies, including energy storage for which a sizing procedure is proposed. The generally applicable methods are regionally exemplified for Kentucky, USA, including eight scenarios with four major year-long simulated case studies and 176 subcases using high performance computing (HPC) systems.

First principles insight into band gap tuning in bismuth based double perovskites X2NaBiCl6 (X = Cs, Rb, K) for enhanced optoelectronic and thermoelectric properties

First principles insight into band gap tuning in bismuth based double perovskites X2NaBiCl6 (X = Cs, Rb, K) for enhanced optoelectronic and thermoelectric properties

The effect of demand response on CO2 Emissions in the Iberian electricity market – Combining economic and environmental perspectives

The effect of demand response on CO2 Emissions in the Iberian electricity market – Combining economic and environmental perspectives

Electricity generation portfolio planning and policy implications of Turkish power system considering cost, emission, and uncertainty

Electricity generation portfolio planning and policy implications of Turkish power system considering cost, emission, and uncertainty

Implications of power industry marketization for sustainable generation portfolios in China

Implications of power industry marketization for sustainable generation portfolios in China

Maximizing financial benefits of investment tax allowances in renewable energy portfolios

ABSTRACT Policy makers offer fiscal incentives to encourage companies to invest in renewable technologies. This work considers generation companies that take advantage of fiscal incentives to minimize income tax. Thus, a novel mixed-integer linear optimization model that minimizes total tax payments of a company owning a portfolio of energy projects is designed. The model strategically manages depreciation, tax loss carryforward, and tax incentive use for minimizing discounted income tax. A set of revenue scenarios was employed to analyze model results. The proposed model yields tax savings between 8.2% and 19.2% of the company’s taxes. Tax savings are significantly larger for companies with a large generation portfolio; which represents a clear advantage over small generation companies. The proposed model can also be employed by policy makers to adjust future ITA policies by taking advantage of the anticipative knowledge of the optimal tax strategies implemented by generation companies.

Review of Stability Analysis in Power Systems with High Penetration of Non-synchronous Generation

Sustainability goals basically change the power generation portfolio, as the renewable energy resources developing rapidly. This means system integration challenges for system operators, as these new resources have different attributes. Most of the emerging generation technologies connect to the grid via power electronic converters, therefore does not contribute inherently to the system’s rotational inertia. However, advanced control methods became a progressive research area, where many practical pilots deliver valuable results and discussions nowadays. These stability enhancement techniques have great potential to enhance security of supply with synthetic inertia services. This paper aims to review generation-driven stability analysis techniques based on the modelling methods used for the nonsynchronous generators. Based on the stability phenomena (angle, frequency), system size, generation portfolio, computational time the adequate methods could be quite different. The proposed extension of the stability classification convers the need of different approaches with the integration of non-synchronous generation. Based on the review presented in this paper, the stability assessment needs for a specific system operator can be identified to choose the appropriate modelling assumptions. This can help system operators in long-term planning (stability adequacy) and in the dynamic security assessment tasks as well, both on the identification of possible issues and evaluate the countermeasures.

Towards just transition of coal regions - Cultivation of short rotation copies and dedicated energy crops for biomass co-firing vs photo voltaic power plants

Towards just transition of coal regions - Cultivation of short rotation copies and dedicated energy crops for biomass co-firing vs photo voltaic power plants

Evaluating power grid model hydropower feasibility with a river operations model

Production cost models (PCMs) simulate dispatch of generators across a large power grid and are used widely by planners to study the reliability of electricity supply. As energy systems transition away from the thermoelectric technologies that have traditionally balanced electricity supply and demand, hydropower and its representation in PCMs is of increasing importance. A limitation of PCMs applied to continental power grids with diverse generation portfolios is that hydropower generation is simulated without full consideration of complex river dynamics, leading to possible misrepresentation of grid flexibility and performance. In addition, data used in PCMs may reflect outdated operating policies. In this paper we propose a hydropower generation feasibility test for PCMs. The approach uses a detailed hydropower model to determine whether the hourly hydropower schedule from a PCM with simplified monthly parameterization can be attained after accounting for realistic river dynamics and operating policies, such as spill requirements and general water movement and balance through a cascade reservoir system. We perform this hydropower generation test for the ‘Big 10’ hydropower system on the Columbia River (part of the Western Interconnect of the United States), revealing 9% overestimation of available hydropower generation in a PCM solution in an average hydrologic year. Our evaluation provides insight on the cost and opportunities for better representing hydropower in PCMs.

Hydro-economics tradeoff surfaces to guide unit commitment in production cost models

Hydro-economics tradeoff surfaces to guide unit commitment in production cost models

Optimising sustainable renewable energy portfolios using a multi-tolerance fuzzy goal programming approach

ABSTRACT The application of fuzzy multi-objective decision-making (F-MODM) to the optimization problems of renewable energy (RE) planning with uncertainties has become increasingly popular, although it is still rare in contrast to the use of traditional multi-attribute decision-making (MADM) methods. This study contributes to the domain of F-MODM modeling and the empirical applications of RE. Methodologically, as a recent trend to enhance the solution quality of fuzzy goal programming (FGP) in F-MODM is to introduce the “tolerance” (or admissible violation) concept to membership functions (MFs), this study proposes a novel ‘FGP-MT’ (multi-tolerance FGP) model to overcome the limitations of ‘single tolerance’ during FGP modeling (i.e. being unable to fit complicated ‘multitolerance’ decision contexts and running the risk of missing better optimality). The model is first verified using a numerical example from the F-MODM literature. Empirically, an application of the FGP-MT model is then considered for a SREPO (sustainable renewable energy portfolio optimization) decision, searching for the optimal electricity generation portfolio from multiple RE resources in Algeria for sustainable RE development, subject to the special preference structure of the decision-maker (DM) that involves manifesting two or more tolerances for a fuzzy goal. As the model performs well or even better in determining the most suitable SREPO decision under this condition, insights are gained for the studied case, and future applications of the model are therefore expected.