Power Generation Scenarios Research Articles

Wind Speed Clustering Using Linkage-Ward Method: A Case Study of Khaaf, Iran

The use of renewable energy for providing electricity is growing rapidly. Among others, wind power is one of the most appealing energy sources. The wind speed has direct impact on the generated wind power and this causes the necessity of wind speed forecasting. For better power system planning and operation, we need to forecast the available wind power. Wind power is volatile and intermittent over the year. For getting better insight and a tractable optimization problem for different decision making problems in presence of wind power generation, it is required to cluster the possible wind power generation scenarios. This article presents probabilistic wind speed clustering prototype for wind speed data of Khaaf, Iran. This region is known as one of the high potential wind sites in Iran and several wind farm projects is planned in this area. The average speed of wind for a ten-minute period measured at height of 40m over a year (2008) is used for clustering. From the result of this research, the most appropriate probabilistic model for the wind speed can be obtained.

Control of a new stand-alone wind turbine-based variable speed permanent magnet synchronous generator using quasi-Z-source inverter

In this paper a new variable speed permanent magnet synchronous generator (PMSG)-based stand-alone wind energy conversion system (SWECS) is proposed. The interface between the PMSG and the isolated load is accomplished by a quasi-Z-source inverter (qZSI) with battery storage system. The battery-assisted qZSI can balance the stochastic fluctuations of the wind power injected to the load and improve the voltage and frequency control. In addition to the battery storage, a dump load is used in the proposed SWECS to better maintain the active power balance and stability of the dc-link voltage during over-generation condition as well as sudden load changes. The proposed control system is able to provide an uninterrupted and reliable supply to sensitive loads under various power generation scenarios of the SWECS and sudden changes in the load demand. Moreover, the proposed controller provides maximum power point tracking (MPPT) which is essential for optimum operation of the SWECS. The validity of the proposed system is proved by simulation results carried out using MATLAB/SIMULINK.

Utilization of Food and Agricultural Residues for a Flexible Biogas Production: Process Stability and Effects on Needed Biogas Storage Capacities

Biogas plants can contribute to future energy systems’ stability through flexible power generation. To provide power flexibly, a demand-oriented biogas supply is necessary, which may be ensured by applying flexible feeding strategies. In this study, the impacts of applying three different feeding strategies (1x, 3x and 9x feeding per day) on the biogas and methane production and process stability parameters were determined for a biogas plant with a focus on waste treatment. Two feedstocks that differed in (1) high fat and (2) higher carbohydrate content were investigated during semi-continuous fermentation tests. Measurements of the short chain fatty acids concentration, pH value, TVA/TIC ratio and total ammonium and ammonia content along with a molecular biology analysis were conducted to assess the effects on process stability. The results show that flexible biogas production can be obtained without negative impacts on the process performance and that production peaks in biogas and methane can be significantly shifted to another time by changing feeding intervals. Implementing the fermentation tests’ results into a biogas plant simulation model and an assessment of power generation scenarios focusing on peak-time power generation revealed a considerable reduction potential for the needed biogas storage capacity of up to 73.7%.

A Model Predictive Control Design for Power Generation Heavy-Duty Gas Turbines

The new electric power generation scenario, characterized by growing variability due to the greater presence of renewable energy sources (RES), requires more restrictive dynamic requirements for conventional power generators. Among traditional power generators, gas turbines (GTs) can regulate the output electric power faster than any other type of plant; therefore, they are of considerable interest in this context. In particular, the dynamic performance of a GT, being a highly nonlinear and complex system, strongly depends on the applied control system. Proportional–integral–derivative (PID) controllers are the current standard for GT control. However, since such controllers have limitations for various reasons, a model predictive control (MPC) was designed in this study to enhance GT performance in terms of dynamic behavior and robustness to model uncertainties. A comparison with traditional PID-based controllers and alternative model-based control approaches (feedback linearization control) found in the literature demonstrated the effectiveness of the proposed approach.

Fault Monitoring of Solar Panels Using Wi-Fi

In today’s scenario of power generation, the solar power plays a vital role. Harnessing the solar energy to its optimum level has been a major challenge for the scientists and engineers for over a decade. With advances in the technology, we have come up with two widely accepted methods of generation of electricity from the solar energy, viz. solar thermal generation and solar photovoltaic generation, of which, the later one has been the most popular. Solar photovoltaic, in spite of being the most passive and convenient method, is quite heavier on the budget when it comes to its installation cost. Also, replacement or maintenance of this expensive equipment can cause a heavy toll on the economics of the utility. Therefore, it becomes essential to protect the photovoltaic system from severe damage. The solar cells are semiconductor devices, which are quite sensitive to its parameters such as current, temperature, resistance, etc. As the solar panels are open in the atmosphere, they are subjected to the drastic changes that occur in the atmosphere. This may lead to uncontrolled damage to the solar panels. In order to avoid any severe damage, it is essential to detect and resolve the faults in time.

Convex Hull Based Robust Security Region for Electricity-Gas Integrated Energy Systems

Increasing interdependency between electric power systems and natural gas systems creates additional dimensions of flexibility of system operation; but it may also escalate risk levels if the two systems are managed without proper coordination. This paper presents a robust security region (RSR) for electricity-gas integrated energy systems (IESs). It is a collection of uncertain parameters, especially wind power generations, under which system power flow has at least one feasible solution. First, a nonlinear optimization model is employed to obtain a set of uniformly distributed points on power flow solvability boundaries, from which the IES security region is constructed using a convex hull. Then, the proposed RSR is constructed to provide secure operation for the base-case scenario, where uncertain wind power generation values are replaced with expected values, and provide a feasible redispatch plan for each wind power generation scenario. Other implications of the RSR on IES security assessment are also envisioned. Results for two test systems indicate that the convex hull approach provides a more accurate approximation of the IES security region than a hyperplane approach. Meanwhile, the benefits of RSR are demonstrated by comparing test results with those obtained for a deterministic security region.

Green Scenarios for Power Generation in Vietnam by 2030

Green Scenarios for Power Generation in Vietnam by 2030

Green Scenarios for Power Generation in Vietnam by 2030

Energy for future sustainable economic development is considered one crucial issue in Vietnam. This article aims to investigate green scenarios for power generation in Vietnam by 2030. Four scenarios named as business as usual (BAU), low green (LG), high green (HG) and crisis have been proposed for power generation in Vietnam with projection to 2030. Three key factors have been selected for these scenarios, namely: (1) future fuel prices, (2) reduction of load demand caused by the penetration of LED technology and rooftop photovoltaic (PV) systems, and (3) the introduction of power generation from renewable sources. The least costly structure of power generation system has been found. CO2 emission reduction of HG in comparison to the BAU scenario and its effect on generation cost reduction are computed. Results show that BAU is the worst scenario in terms of CO2 emissions because of the higher proportion of power generation from coal and fossil fuels. LG and HG scenarios show their positive impacts both on CO2 emissions and cost reduction. HG is defined as the greenest scenario by its maximum potential on CO2 emission reduction (~146.92Mt CO2) in 2030. Additionally, selling mitigated CO2can make green scenarios more competitive to BAU and Crisis in terms of cost. Two ranges of generation cost (4.3-5.5 and 6.0-7.7US$cent/kWh) have been calculated and released in correspondence with low and high fuel price scenarios in the future. Using LED lamps and increasing the installed capacity of rooftop PVs may help reduce electric load demand. Along with the high contribution of renewable sources will make the HG scenario become more attractive both in environmental and economic aspects when the Crisis scenario comes. Generation costs of all scenarios shall become cheap enough for promoting economic development in Vietnam by 2030.

How fusion power can contribute to a fully decarbonized European power mix after 2050

In the second half of this century, the European energy mix will be very likely completely decarbonized. Two main options are available to generate carbon free electricity: either to rely on renewable energy sources only, or to integrate renewables with a new generation of nuclear power plant, e.g. fusion power plant. In the former case a large storage capacity and/or dispatchable generation are required to compensate for the variable electricity generation. The size of the necessary storage systems and dispatchable power plants can be reduced if a base-load carbon free power technology is available, such as fusion. These options are analysed in this paper with the CO.ME.S.E. (COsto MEdio del Sistema Elettrico – Electric System Average Cost) code that assesses the hourly balance between load and generation, through a simplified dispatch model and the system costs, through a stochastic economic analysis. Two sets of power generation scenarios are considered in typical North and South-Europe cases. The study gives key indications to make fusion a cost-effective ingredient of a future European decarbonized power mix.

An islanding partition method of active distribution networks based on chance-constrained programming

With the high integration of uncontrollable distributed generators (NDGs) comprising photovoltaic arrays (PVs) and wind turbines (WTs), many technical issues have become increasingly prominent in the islanding operation of active distribution networks (ADNs). Such problems including branch overloading and voltage violations threaten the secure operation of distribution systems and a continuous power supply. To address the uncertainties of NDG outputs when implementing islanding partition strategies, this paper proposes a new method of the islanding partition of ADNs based on chance-constrained programming. First, power generation scenarios and probability distributions for these scenarios are generated according to historical data considering the time series characteristics and uncertainties of NDGs. Then, the chance-constrained description of islanding operation is expanded to consider the effect of uncertainties related to NDGs. Moreover, an islanding partition model of ADNs based on chance-constrained programming is established. By applying convex relaxation and introducing auxiliary variables, the model is converted to a mixed-integer quadratically constrained programming model that can be effectively solved. Finally, case studies involving the modified IEEE 33-node system, IEEE 123-node system and an actual distribution system are conducted to verify the effectiveness and scalability of the proposed method.

Transition scenarios of power generation in China under global 2 °C and 1.5 °C targets

Under the Paris Agreement, targets implemented for 2100 specify temperature increases well below 2 °C, with an ambitious target of 1.5 °C. China signed this agreement and will support these global targets. The question remains whether they are possible, especially considering the slow progress in recent decades, despite the fact that the Kyoto Protocol implemented these targets in 2010. The Intergovernmental Panel on Climate Change (IPCC) required modeling research teams to analyze possible pathways, policy options, and cost benefit analyses for GHG mitigation. China’s CO2 emissions from the energy and cement industries already accounted for almost 29% of global emissions in 2017, and this trend is expected to continue increasing. The role of China in global GHG mitigation is therefore crucial. This study presents a scenario analysis for China’s power generation against the background of the global 2 °C and 1.5 °C targets. We discuss the possibility of a lower CO2 emission power generation scenario in China in order to evaluate the national emission pathway towards these targets. Our findings suggest that China can accomplish rapid transition in the power generation sector, reaching its emission peak before 2025. This would make the global 2 °C target possible because energy system development is a key factor. Furthermore, the recent progress of key power generation technologies, potential for further investment in the power generation sector, and recent policy implementation all significantly contribute to China following a low carbon emission development pathway.

Enhanced prioritisation of prospective scenarios for power generation in Spain: How and which one?

Many countries are implementing energy policies that focus on the transformation of national power generation mixes into highly efficient and sustainable systems. In the case of the Spanish electricity sector, regulations applicable to existing coal thermal plants and the proximity to the end of the lifetime of nuclear power plants arise as critical concerns to be addressed. In this respect, a sensible energy strategy is required in order to fill the future gap of coal and nuclear power plants. Within this context, this work applies a novel methodological framework for the prioritisation of 15 energy scenarios formulated for power generation in Spain. In addition to a business-as-usual scenario, alternative ones are built based on potential restrictions on fossil-based technologies and lifetime extensions of coal and nuclear power plants. The novel multi-criteria decision analysis approach for enhanced, sustainability-oriented energy planning is based on the combined use of energy systems modelling, life cycle assessment, and data envelopment analysis. Besides conventional techno-economic results, the evolution of life-cycle indicators endogenously integrated into the energy system model of Spain is provided. The subsequent use of a dynamic data envelopment analysis model leads to identify and rank the most suitable energy scenarios according to sustainability criteria.

Excess electricity and power-to-gas storage potential in the future renewable-based power generation sector in the United Arab Emirates

Future power generation scenarios for the United Arab Emirates (UAE) that emphasize solar photovoltaic (PV) and concentrated solar power (CSP) with thermal energy storage are analyzed at PV:CSP generation ratios of 1:1 to 4:1, and up to 50% renewable share. Such scenarios enable up to 24–38% reduction in primary fuel consumption at 30–50% renewable share, respectively, relative to the base case scenario (i.e., business-as-usual), with accompanying reductions in carbon dioxide emissions of up to 34–54%, respectively. In parallel, over the same range of renewable shares, excess electricity is generated in the range of 1–13 TWh (0.4–5% of annual demand) and 3–25 TWh (1–10%) annually, at PV:CSP ratios of 1:1 and 4:1, respectively. Monthly excess electricity maxima and minima occur in winter and summer, respectively. At 40% renewable electricity share, the PV:CSP 4:1 scenario would lead to annual power generation costs, inclusive of power-to-gas (PtG), approximately 11% higher than for the base case scenario, and 13% lower than for the PV:CSP 1:1 scenario. At an electricity tariff of 0.05 USD/kWh and 50% capacity utilization, the levelized costs of hydrogen and synthetic natural gas (SNG) are estimated at 101 USD/MWhth and 127 USD/MWhth, respectively.

Techno-economic assessment of combined hydrogen & power co-generation with carbon capture: The case of coal gasification

The gasification technology has multiple key advantages for future low carbon power generation scenario e.g. reduced energy and cost penalties for CO2 capture, multi-fuel multi-product operation capability, ability to process lower grade fuels, plant flexibility, etc. This paper is assessing the updated most important techno-economic aspects of IGCC power plant equipped with pre-combustion CO2 capture. Several key design options were assess in view of techno-economic indicators: selection of the gasification reactor, chemical vs. physical gas-liquid absorption used for CO2 capture, F and H-class gas turbines used for the combined cycle power block, flexible hydrogen and power co-generation, etc. Evaluated coal-based gasification concepts generate 400–600 MWe net power with a flexible hydrogen output from zero up to 300 MWth with 90% carbon capture rate. The similar design without carbon capture was also consider as a base case to quantify the technical and economical modifications induced by the CO2 capture step. As the techno-economical results show, the optimised IGCC plant (using dry fed entrained flow gasifier, H-class gas turbine, physical solvents, flexible hydrogen and power co-generation, etc.) provides improved results. In conclusion, the IGCC with pre-combustion capture is one promising technology for the future low-carbon economy.

Available Electrical Energy Resources in Egypt and World

The research aims at identify the available energy sources in Egypt and the World, whether traditional or renewable (petroleum, coal, natural gas, wind, solar, underground) and a background on their technologies and capabilities available in the world and Egypt. The researcher used the descriptive approach and the analytical method to identify the electrical energy sources and their technologies. The research applied on Egyptian Electricity Holding Company. The researcher carried out calculations using economic analysis models to link the patterns of consumption sectors with economic and social factors in addition to use of historical data to forecast demand scenarios for electric energy consumption until 2050 in order to conceptualize power generation scenarios based on the expansion of renewable energies. The researcher concluded that Egypt has various energy potentials especially renewable energies which enable Egypt to set medium and long plans with a balanced energy mix from all energy sources with a priority for renewables. The researcher recommended the necessity of strategic planning for the energy security in Egypt including energy mix form all available energy sources especially solar energy. Beside the approach of making Egypt as an energy hub (gas – electricity) depending of the geographical position of Egypt, in addition to completing the intuitional, legislation and regulation frameworks in order to enhance the role of the gas and electricity regulators to set regulations for gas and electricity markets.

Investigating the environmental and socio‐economic impacts of grid‐tied photovoltaic and on‐shore wind systems in Bangladesh

Current power generation scenarios all over the world are not climate friendly as the generation systems are mainly dependent on fossil fuels that produce greenhouse gas (GHG). As renewable energies (especially solar and wind energies) have the sustainable characteristics, by replacing the conventional energy system with them, it is possible to significantly contribute to reducing the dependency on fossil fuels as well as GHG emissions. Though biomass is the primary renewable source in Bangladesh, this study is exploring new options in photovoltaic (PV) and on-shore wind in the 0.5–2 MW capacity range. This research presents environmental and socio-economic impacts of grid-connected hybrid (PV/wind) power systems by investigating the potentials of the solar and wind energy with considering various sensitivity variables for two different locations, namely Magnama and Dinajpur, in Bangladesh. The main goal of this research is to generate the lowest possible adverse socio-economic and environmental impacts ensuring a certain degree of monetary benefits. Three sizes of plants have been chosen for quantifying the variations in socio-economic and environmental impacts. It is found that the proposed model of the hybrid power system can maximise the benefits by reducing the adverse effects of conventional power plants.

Development of a Bottom-up Energy System Model with High-Temporal-Resolution Power Generation Sector and Scenario Analysis on CO<sub>2</sub> Reduction in Japan

Development of a Bottom-up Energy System Model with High-Temporal-Resolution Power Generation Sector and Scenario Analysis on CO<sub>2</sub> Reduction in Japan

Impact of electric vehicles on a future renewable energy-based power system in Europe with a focus on Germany

Analysing the implications of different scenarios of electric vehicles and renewable power generation considering their spatial and temporal characteristics, we investigate possible effects of electric mobility on the future power system in Germany and Europe. The time horizon of the scenario study is 2050. The approach is based on power system modelling that includes interchange of electricity between European regions, which allows assessing long‐term structural effects in energy systems with over 80% of renewable power generation. The study exhibits strong potential of controlled charging and flexible hydrogen production infrastructure to avoid peak demand increases and to reduce the curtailment of renewable power resulting in reduced system operation, generation, and network expansion costs. A charging strategy that is optimised from a systems perspective avoids in our scenarios 3.5 to 4.5 GW of the residual peak load in Germany and leads to efficiency gains of 10% of the electricity demand of plug-in electric vehicles compared with uncontrolled loading.

Robust Scheduling of EV Charging Load With Uncertain Wind Power Integration

In some micro grids, the charging of electric vehicles (EVs) and the generation of wind power may partially cancel each other. This is an effective way to reduce the variation of the wind power to the state grid. Due to the forecasting error, it is of great practical interest to schedule the EV charging demand under the worst-case scenario of the wind power generation. We consider this important robust scheduling problem in this paper and make three major contributions. First, we formulate this robust scheduling problem as a robust stochastic shortest path problem whereby the objective function is a weighted sum of the wind power utilization and the total charging cost. Second, a robust simulation-based policy improvement method is developed to improve the performance of a base policy in the worst case. This improvement is mathematically shown under mild assumptions. Third, the performance of this method is numerically demonstrated based on real wind and EV data.

The data-driven schedule of wind farm power generations and required reserves

In this paper, a double layer wind farm and power reserve operational framework is introduced to develop the data-driven scheduling model for operating wind power generations and required power reserves. In this framework, data-driven approaches are applied to model real operational characteristics of wind power generation processes of individual wind generators (WGs) based on supervisory control and data acquisition (SCADA) data. The considered optimization objective in scheduling is to minimize the total wind power generation cost under the worst wind power generation scenario. Constraints considered include the wind power supply commitment and power supply reliability. By integrating the data-driven wind power models, multiple uncertainty sets, the cost objective function and reliability constraints, a data-driven scheduling model is formulated. Due to the complexity of the scheduling model, a two-level heuristic solution method is proposed to solve it. Two traditional power reserve approaches are regarded as the benchmark to evaluate the performance of the data-driven scheduling model. A comparative analysis is conducted to study the effectiveness of the solution method as well as the impact of different uncertainty sets and reliability constraints on the scheduling solutions.