Load Hours Research Articles

Economic Evaluation of Solar Cooling Schemes

This paper provides an economic evaluation and comparison of ejector, absorption and vapor compression systems driven either partly or fully by solar energy. Life cycle costing has been used to assess the relative economic ranks of eight solar cooling schemes. It has been shown that the capital cost of solar collectors is the dominant capital cost item in the total inventory of solar cooling plant, which has a strong influence on the system life cycle costs. Lower collector costs are therefore critical in establishing economically viable solar cooling systems. Primary energy analysis has been carried out and the running costs of chillers were determined for the maximum range of thermal and electric solar fractions and a number of annual equivalent full load hours of operation. The results indicate that two low temperature flat-plate collector assisted single-effect absorption chillers compete favorably with a PV assisted centrifugal mechanical compression chiller across the maximum range of thermal and electrical solar fractions. Low temperature options for solar cooling have been shown to be more economical than their high temperature counterparts. At current collector prices, solar cooling systems are still not cost effective compared with conventional centrifugal cooling systems, however, it is shown that at a collector cost of £57/m2 for thermal energy and £1.8/Wp for electrical energy, single-effect solar absorption and PV-centrifugal compression could become cost effective within an annual EFLH of 5840 hours. As such, the paper presents technical guidance on the procurement and operation of solar cooling plant. The novel energy and cost calculation methodology developed here can be applied globally to a wide range of solar collectors, chillers, heat rejection and ancillary subsystems.

Equivalent full-load hours for assessing climate change impact on building cooling and heating energy consumption in large Asian cities

Estimating cooling and heating energy requirements is an integral part of designing and managing buildings. Further, as buildings are among the largest energy consumers in cities, the estimates are important for formulating effective energy conservation strategies. Where complex hourly simulation models are not favored, such estimates may be derived by simplified methods that are less computationally intensive but still provide results that are reasonably close to those obtained from the more complicated approach. The equivalent full load hours (EFLH) method is a simplified energy estimation method that has recently gained popularity. It offers a straightforward means of evaluating energy efficiency programs. However, to date, easily accessible EFLH data exist only for a very limited number of countries in North America and Europe, but not Asia. This current work provides previously unavailable monthly EFLH data for building cooling and heating in three large Asian cities, viz. Hong Kong, Seoul and Tokyo. To assess the effects of changing temperature over the course of decades on building cooling and heating energy consumption, EFLH data are calculated for three time periods: past (1983–2005), present (2006–2014) and future (2015–2044). The projections for the future time period are based on the climate scenarios Representative Concentration Pathways (RCPs) 4.5 and 8.5 of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report. RCP-4.5 assumes a stabilization of future greenhouse gas (GHG) emissions followed by a reduction, while RCP-8.5 assumes their further increase. From the EFLH data, considering just the effects of ambient temperature changes, it is projected the total energy required to heat and/or cool residential dwellings in Hong Kong, Seoul and Tokyo to increase by 18.3%, 4% and 10.4%, respectively over 62years from 1983 to 2044 in the case of RCP-4.5, and by 23.3%, 9.3% and 15.8%, respectively in the case of RCP-8.5. This shows that even with future stabilization and reduction of GHG emissions, as per scenario RCP-4.5, the energy needs of the three cities for building heating and cooling combined can be expected to increase over the next few decades. This has significant implications, namely increased demands for additional primary energy, which will result in further GHG emissions. These effects, however, can be controlled with adjustments to the electricity fuel mix of each location, and also by use of more efficient heating, ventilation and air-conditioning (HVAC) devices.

Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm

In the domestic sector, increased energy consumption of home appliances has become a growing issue. Thus, reducing and scheduling energy usage is the key for any home energy management system (HEMS). To better match demand and supply, many utilities offer residential demand response program to change the pattern of power consumption of a residential customer by curtailing or shifting their energy use during the peak time period. In the present study, real time optimal schedule controller for HEMS is proposed using a new binary backtracking search algorithm (BBSA) to manage the energy consumption. The BBSA gives optimal schedule for home devices in order to limit the demand of total load and schedule the operation of home appliances at specific times during the day. Hardware prototype of smart sockets and graphical user interface software were designed to demonstrate the proposed HEMS and to provide the interface between loads and scheduler, respectively. A set of the most common home appliances, namely, air conditioner, water heater, refrigerator, and washing machine has been considered to be controlled. The proposed scheduling algorithm is applied under two cases in which the first case considers operation at weekday from 4 to 11 pm and the second case considers weekend at different time of the day. Experimental results of the proposed BBSA schedule controller are compared with the binary particle swarm optimization (BPSO) schedule controller to verify the accuracy of the developed controller in the HEMS. The BBSA schedule controller provides better results compared to that of the BPSO schedule controller in reducing the energy consumption and the total electricity bill and save the energy at peak hours of certain loads.

Modeling of a Photovoltaic-Powered Electric Vehicle Charging Station with Vehicle-to-Grid Implementation

This paper is aimed at modelling of a distinct smart charging station for electric vehicles (EVs) that is suitable for DC quick EV charging while ensuring minimum stress on the power grid. Operation of the charging station is managed in such a way that it is either supplied by photovoltaic (PV) power or the power grid, and the vehicle-to-grid (V2G) is also implemented for improving the stability of the grid during peak load hours. The PV interfaced DC/DC converter and grid interfaced DC/AC bidirectional converter share a DC bus. A smooth transition of one operating mode to another demonstrates the effectiveness of the employed control strategy. Modelling and control of the different components are explained and are implemented in Simulink. Simulations illustrate the feasible behaviour of the charging station under all operating modes in terms of the four-way interaction among PV, EVs and the grid along with V2G operation. Additionally, a business model is discussed with comprehensive analysis of cost estimation for the deployment of charging facilities in a residential area. It has been recognized that EVs bring new opportunities in terms of providing regulation services and consumption flexibility by varying the recharging power at a certain time instant. The paper also discusses the potential financial incentives required to inspire EV owners for active participation in the demand response mechanism.

Techno-Economic Assessment of Power-to-Liquids (PtL) Fuels Production and Global Trading Based on Hybrid PV-Wind Power Plants

This paper introduces a value chain design for transportation fuels and a respective business case taking into account hybrid PV-Wind power plants, electrolysis and hydrogen-to-liquids (H2tL) based on hourly resolved full load hours (FLh). The value chain is based on renewable electricity (RE) converted by power-to-liquids (PtL) facilities into synthetic fuels, mainly diesel. Results show that the proposed RE-diesel value chains are competitive for crude oil prices within a minimum price range of about 79 - 135 USD/barrel (0.44 – 0.75 €/l of diesel production cost), depending on the chosen specific value chain and assumptions for cost of capital, available oxygen sales and CO2 emission costs. A sensitivity analysis indicates that the RE-PtL value chain needs to be located at the best complementing solar and wind sites in the world combined with a de-risking strategy and a special focus on mid to long-term electrolyser and H2tL efficiency improvements. The substitution of fossil fuels by hybrid PV-Wind power plants could create a PV-wind market potential in the order of terawatts.

Comparative analysis of design criteria for hybrid photovoltaic/wind/battery systems

Reliability analyses are essential for the design of hybrid photovoltaic/wind/battery systems. The selection of design criteria is an important task and has to ensure proper reliability and optimal configuration. In the literature, loss of power supply probability (LPSP) and loss of load hours (LOLH) are the most common reliability criteria used for this matter. This study presents a comparative analysis on LPSP and LOLH design criteria based on a Monte Carlo simulation, taking into consideration uncertainties in the variables involved in the design process. Moreover, two new statistical design criteria are proposed, aiming to avoid over-sizing of the optimal configuration. According to the obtained results, LOLH is a stricter design criterion compared with LPSP, leading to a more reliable energy system. In addition, the optimal configuration selected by using the proposed statistical design criteria showed better performance when compared with the solution based on LPSP or LOLH.

Loading-Induced Heat-Shock Response in Bovine Intervertebral Disc Organ Culture.

Mechanical loading has been shown to affect cell viability and matrix maintenance in the intervertebral disc (IVD) but there is no investigation on how cells survive mechanical stress and whether the IVD cells perceive mechanical loading as stress and respond by expression of heat shock proteins. This study investigates the stress response in the IVD in response to compressive loading. Bovine caudal disc organ culture was used to study the effect of physiological range static loading and dynamic loading. Cell activity, gene expression and immunofluorescence staining were used to analyze the cell response. Cell activity and cytoskeleton of the cells did not change significantly after loading. In gene expression analysis, significant up-regulation of heat shock protein-70 (HSP70) was observed in nucleus pulposus after two hours of loading. However, the expression of the matrix remodeling genes did not change significantly after loading. Similarly, expressions of stress response and matrix remodeling genes changed with application and removal of the dynamic loading. The results suggest that stress response was induced by physiological range loading without significantly changing cell activity and upregulating matrix remodeling. This study provides direct evidence on loading induced stress response in IVD cells and contributes to our understanding in the mechanoregulation of intervertebral disc cells.

Integration of large-scale heat pumps in the district heating systems of Greater Copenhagen

This study analyses the technical and private economic aspects of integrating a large capacity of electric driven HP (heat pumps) in the Greater Copenhagen DH (district heating) system, which is an example of a state-of-the-art large district heating system with many consumers and suppliers. The analysis was based on using the energy model Balmorel to determine the optimum dispatch of HPs in the system. The potential heat sources in Copenhagen for use in HPs were determined based on data related to temperatures, flows, and hydrography at different locations, while respecting technical constraints. The Balmorel model was developed further in order to provide a better representation of HPs, for analysing the seasonal variations of COP (Coefficient of Performance), and to represent the difference in performance of HPs connected to either distribution or transmission networks. The optimization yields roughly 3500 FLH (full load hours) for the HPs connected to the DH distribution networks when considering a current scenario. In a zero carbon-dioxide emission scenario expected in year 2025, approximately 4000 FLH, are achieved. In the case where HPs are connected to the DH transmission network at elevated temperatures, their operation decreases by roughly 1000 FLH. No significant impact was found when comparing fixed and varying operation characteristics of the HP.

Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: A comparative study

The uncertainties of renewable energy have brought great challenges to power system commitment, dispatches and reserve requirement. This paper presents a comparative study on integration of renewable generation uncertainties into SCUC (stochastic security-constrained unit commitment) considering reserve and risk. Renewable forecast uncertainties are captured by a list of PIs (prediction intervals). A new scenario generation method is proposed to generate scenarios from these PIs. Different system uncertainties are considered as scenarios in the stochastic SCUC problem formulation. Two comparative simulations with single (E1: wind only) and multiple sources of uncertainty (E2: load, wind, solar and generation outages) are investigated. Five deterministic and four stochastic case studies are performed. Different generation costs, reserve strategies and associated risks are compared under various scenarios. Demonstrated results indicate the overall costs of E2 is lower than E1 due to penetration of solar power and the associated risk in deterministic cases of E2 is higher than E1. It implies the superimposed effect of uncertainties during uncertainty integration. The results also demonstrate that power systems run a higher level of risk during peak load hours, and that stochastic models are more robust than deterministic ones.

Robust resource adequacy planning in the face of coal retirements

We investigate the resource adequacy requirements of the PJM Interconnection, and the sensitivity of capacity procurement decisions to the choice of reliability metric used to measure resource adequacy. Assuming that plants fail independently, we find that PJM's 2010 reserve margin of 20.5% was sufficient to achieve the stated reliability standard of one loss of load event per ten years, with 0.012 expected loss of load events per year. PJM could reduce reserve margins to 13% and still achieve adequate levels of reliability as measured by the 2.4 Loss of Load Hours metric and the 0.001% Unserved Energy metric, which are used by other U.S. and international systems. A reserve margin of 13–15% would minimize long-run system costs. Reducing reserve margins from 20.5% to 13% in 2010 would have reduced PJM's capacity procurement by 11GW, the same amount of coal capacity that PJM has identified as at high risk of retirement. We also investigate the risk posed by correlated failures among generators, a risk traditionally not modeled by system planners. We illustrate that three types of correlated failures may increase outage risks: natural gas supply disruptions, reduced reliability among generators during winter months, and the simultaneous shutdown of multiple nuclear generators for regulatory reasons.

A non-linear stochastic model for an office building with air infiltration

This paper presents a non-linear heat dynamic model for a multi-room office building with air infiltration. Several linear and non-linear models, with and without air infiltration, are investigated and compared. The models are formulated using stochastic differential equations and the model parameters are estimated using a maximum-likelihood technique. Based on the maximum-likelihood value, the different models are statistically compared to each other using Wilk's likelihood ratio test. The model showing the best performance is finally verified in both the time domain and the frequency domain using the auto-correlation function and cumulated periodogram . The proposed model which includes air-infiltration shows a significant improvement compared to previously proposed linear models. The model has subsequently been used in applications for provision of power system services, e.g. by providing heat load reduction during peak load hours, control of indoor air temperature and for generating forecasts of power consumption from space heating.

Optimization of dry cooled parabolic trough (CSP) plants for the desert regions of the Middle East and North Africa (MENA)

In this research, the effectiveness of dry cooled concentrating solar power plants in hot arid regions similar to those in the Middle East and North Africa (MENA) is investigated. A previous study (Liqreina, 2014) has proven that dry cooled Concentrating Solar Power (CSP) parabolic trough power plants are an attractive option in such regions mainly due to the high solar irradiation flux present, which compensates the efficiency losses caused by replacing wet cooling by dry cooling systems. This work further investigates improvements of the performance of dry cooled CSP plants in the MENA region by optimizing their design configurations and adapting them for local conditions. Ma’an area in southern Jordan is a representative of the MENA region; it is selected as a case study location for this study. Design configurations and optimization are performed for the local conditions of this site.The configurations of the reference Andasol-type power plant considered in Liqreina (2014) are varied in this work to characterize the efficiency of the power plant under different conditions and in a later step, to find an optimum design of the plant. The results of this study reassure that dry cooled concentrating power plants in sunny arid regions are a competitive option and can produce power at competitive costs.The plant simulation results show that plants with oversized solar field (solar multiple of 2) and large thermal energy storage systems (TES of 7–9 full load hours) perform better and can generate power at lower costs than smaller plants. Furthermore, it is concluded that increasing the overall plant size decreases the levelized cost of energy (LCOE) due to the reduction in plants’ specific costs and the increase in operating efficiency. Power generation costs obtained from this study for dry cooled power plants options range from 12.88 to 13.40c€/kWhel for three different sizes and 13.13c€/kWhel for 50MW wet cooled CSP plant.Given these excellent economic conditions, it is concluded that CSP dry cooled power plants provide an alternative for green power production given that excellent solar conditions are available at a plant site.

Competitiveness of Different Operational Concepts for Power-to-Gas in Future Energy Systems

In the future energy system flexibility demand will increase due to the growing share of intermittent renewable energy sources (RES). Power-to-Gas (PtG) is one of many flexibility options that can provide flexibility on the demand side by converting electricity into gas during times of high RES supply. This renewable gas is a versatile energy carrier that can be used in the electricity, heating or transport sector as well as for industrial processes. Thus, the use of renewable gas could help to reduce greenhouse gas emissions in these sectors and therewith contribute to fulfil the respective national and EU policy objectives. However, the operation of PtG today is unprofitable because of high specific investment and low efficiency factors of present units. A determining factor for the cost effectiveness is the possible utilisation rate of PtG. Therefore, this paper analyses the potential future deployment hours and their impact on profitability of four different PtG operational concepts. The concepts are distinguished by two criteria. The first distinction is made with regard to the purpose of operation. PtG is assumed either to balance intermittent RES surplus electricity or to use additional RES to produce renewable gas for the heat and transport sector. The second distinction refers to the area of operation, i.e. decentralised or centralised. The respective deployment hours of these four concepts are determined for different future scenarios. The results show that PtG needs high full load hours in order to be competitive. Such numbers can only be achieved in scenarios with extreme high RES penetration. Furthermore, it can be seen that the centralised approach is likely to have lower gas production costs compared to the decentralised one. This is due to the fact that the centralised operation profits from balancing effects of the electricity grid and therefore leads to higher utilisation rates. Nonetheless, the findings also suggest that PtG can only compete with conventional hydrogen production costs or natural gas if electricity for PtG can be obtained at low costs or if the specific investment for electrolysis decreases significantly.

Perfect competition vs. strategic behaviour models to derive electricity prices and the influence of renewables on market power

A variety of fundamental modelling approaches exist using different competition concepts with and without strategic behaviour to derive electricity prices. To investigate the quality and practicability of these different approaches in energy economics, a perfect competition model, a Cournot model and a Bilevel model are introduced and applied to different situations in the German electricity market. The three electricity market approaches are analysed with respect to their ability to represent electricity prices and the possibility of market power abuse. Market prices are taken as a benchmark for model validity. As a result, the perfect competition model fits best to today's market situation in most hours of the year. The Bilevel approach explains prices in high load hours sometimes better than the competition model. But complexity and calculation time increase disproportionately. In addition to the analysis of model quality, we use three scenarios to quantify how a high renewable feed-in influences the ability to abuse market power. Results show that the ability to address market power strongly depends on the amount of installed capacities.

Clinical Implications of Changes in Individual Platelet Reactivity to Aspirin Over Time in Acute Ischemic Stroke.

Time-dependent changes in individual platelet reactivity have been detected in patients with coronary artery disease. Therefore, we sought to evaluate the time-dependent changes in platelet reactivity to aspirin during the acute stage after ischemic stroke and the clinical implications of variable patient responses to aspirin in acute ischemic stroke. We conducted a single-center, prospective, observational study. The acute aspirin reaction unit (ARU) was measured after 3 hours of aspirin loading, with higher values indicating increased platelet reactivity despite aspirin therapy. The follow-up ARU was measured on the fifth day of consecutive aspirin intake. The numeric difference between the follow-up ARU and the acute ARU was defined as ΔARU and was stratified into quartiles. Early neurological deterioration was regarded as an early clinical outcome. Both the acute ARU (476±69 IU) and the follow-up ARU (451±68 IU) were measured in 349 patients in this study. Early neurological deterioration was observed in 72 patients (20.6%). Changes in aspirin platelet reactivity over time showed an approximately Gaussian distribution. The highest ΔARU quartile was independently associated with early neurological deterioration (odds ratio, 3.19; 95% confidence interval, 1.43-7.10; P=0.005) by multivariate logistic regression analysis. The results of our study showed that the increase in platelet reactivity to aspirin over time is independently associated with early neurological deterioration in patients with acute ischemic stroke. In addition, during the acute stage of ischemic stroke, serial platelet reactivity assays may be more useful than a single assay for identifying the clinical implications of aspirin platelet reactivity after ischemic stroke.

Solar repowering of PCC-retrofitted power plants; solar thermal plant dynamic modelling and control strategies

Solar repowering of high pressure side feed water preheaters of post combustion carbon capture (PCC) retrofitted power plants offers a promising capability to offset the power plant output penalty due to capture plant reboiler duty. In this work, a model based approach is used to investigate the technical, operational and control aspects of the solar thermal installation of the super structure (power plant+PCC+solar thermal plant). The base case is a 660MWe PCC-retrofitted power plant operating at 100% capacity and 90% capture rate in Australia. This is repowered by a solar thermal plant consisting of (a) a parabolic trough solar collector field with 806,520m2 aperture area, (b) a thermal storage system with 8 full load hours of thermal storage and (c) an auxiliary gas heating unit, ensuring the solar thermal plant provides the high pressure side feed water preheating duty at all times without requirement to withdraw any steam from the power plant turbine circuit. A total of 230MWth is provided from the solar thermal plant installation, being 100% supply for high pressure and intermediate feed water preheaters duty plus a surplus of 30% for maintaining the deaerator operation. A key feature here is trying to eliminate the frequent changes to the steam flow rate in the turbine circuit for solar feed water preheating and thus promoting ease of retrofit of repowering technology to PCC-retrofitted power plants. Details of the component sizing and modelling of solar thermal installation are provided and control and operating schemes are identified and analysed. A comprehensive dynamic model in TRNSYS dynamic modelling software is developed and used to analyse the solar thermal plant dynamic responses to external disturbances including climatic changes (Typical Meteorological data for Sydney is used). The analysis limitations and capabilities are highlighted. The model performance for month December is assessed showing its full capability to track the changes from power plant duty requirement and meteorological data within the time lapse of 0.01s.

Smart residential load reduction via fuzzy logic, wireless sensors, and smart grid incentives

The incentives such as demand response (DR) programs, time-of-use (TOU) and real-time pricing (RTP) are applied by utilities to encourage customers to reduce their load during peak load hours. However, it is usually a hassle for residential customers to manually respond to prices that vary over time. In this paper, a fuzzy logic approach (FLA) utilizing wireless sensors and smart grid incentives for load reduction in residential HVAC systems is presented. Programmable communicating thermostats (PCTs) are used to control residential HVAC systems in order to manage and reduce energy use, while consumers accommodate their everyday schedules. Hence, the FLA is embedded into existing PCTs to augment more intelligence to them for load reduction, while maintaining thermal comfort. To emulate an actual thermostat, a PCT capable of handling both TOU and RTP is simulated in Matlab/GUI. It is utilized as a ‘simulator engine’ to evaluate the performance of FLA via applying several different scenarios. The results show that the FLA decreases/increases the initialized set points without jeopardizing thermal comfort by applying specific fuzzy rules through evaluating the information received from wireless sensors and smart grid incentives. Our approach results in better energy and cost saving in residential buildings versus existing PCT.

Optimization, selection and feasibility study of solar parabolic trough power plants for Algerian conditions

In the present study, optimization of two parabolic trough solar thermal power plants integrated with thermal energy storage (TES), and fuel backup system (FBS) has been performed. The first plant uses Therminol VP-1 as heat transfer fluid in the solar field and the second plant uses molten salt. The optimization is carried out with solar multiple (SM) and full load hours of TES as the parameters, with an objective of minimizing the levelized cost of electricity (LCOE) and maximizing the annual energy yield. A 4E (energy–exergy–environment–economic) comparison of the optimized plants alongside the Andasol 1 as reference plant is studied. The molten salt plant resulting as the best technology, from the optimization and 4E comparative study has been chosen for the viability analysis of ten locations in Algeria with semi-arid and arid climatic conditions. The results indicate that Andasol 1 reference plant has the highest mean annual energy efficiency (17.25%) and exergy efficiency (23.30%). Whereas, the highest capacity factor (54.60%) and power generation (236.90GWh) are exhibited by the molten salt plant. The molten salt plant has least annual water usage of about 800,482m3, but demands more land for the operation. Nevertheless the oil plant emits the lowest amount of CO2 gas (less than 40.3kilo tonnes). From the economic viewpoint, molten salt seems to be the best technology compared to other plants due to its lowest investment cost (less than 360 million dollars) and lower levelized cost of electricity (LCOE) (8.48¢/kWh). The viability study proposes Tamanrasset, as the best location for erection of a parabolic trough solar thermal power plant with a low LCOE of 7.55¢/kWh, and a high annual power generation (more than 266GWh). According to the feasibility analysis, the semi-arid and arid Algerian sites are suitable for realization of PTSTPP with integrated TES and FBS; especially the southern locations (19°N–32°N, 8°W–12°E).

Power-to-Gas as an Emerging Profitable Business Through Creating an Integrated Value Chain

Abstract Power-to-gas (PtG) technology has received considerable attention in recent years. However, it has been rather difficult to find profitable business models and niche markets so far. PtG systems can be applied in a broad variety of input and output conditions, mainly determined by prices for electricity, hydrogen, oxygen, heat, natural gas, bio-methane, fossil CO 2 emissions, bio-CO 2 and grid services, but also full load hours and industrial scaling. Optimized business models are based on an integrated value chain approach for a most beneficial combination of input and output parameters. The financial success is evaluated by a standard annualized profit and loss calculation and a subsequent return on equity consideration. Two cases of PtG integration into an existing pulp mill as well as a nearby bio-diesel plant are taken into account. Commercially available PtG technology is found to be profitable in case of a flexible operation mode offering electricity grid services. Next generation technology, available at the end of the 2010s, in combination with renewables certificates for the transportation sector could generate a return on equity of up to 100% for optimized conditions in an integrated value chain approach. This outstanding high profitability clearly indicates the potential for major PtG markets to be developed rather in the transportation sector and chemical industry than in the electricity sector as seasonal storage option as often proposed.

A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources

The penetration of intermittent renewable energy sources (IRESs) into power grids has increased in the last decade. Integration of wind farms and solar systems as the major IRESs have significantly boosted the level of uncertainty in operation of power systems. This paper proposes a comprehensive computational framework for quantification and integration of uncertainties in distributed power systems (DPSs) with IRESs. Different sources of uncertainties in DPSs such as electrical load, wind and solar power forecasts and generator outages are covered by the proposed framework. Load forecast uncertainty is assumed to follow a normal distribution. Wind and solar forecast are implemented by a list of prediction intervals (PIs) ranging from 5% to 95%. Their uncertainties are further represented as scenarios using a scenario generation method. Generator outage uncertainty is modeled as discrete scenarios. The integrated uncertainties are further incorporated into a stochastic security-constrained unit commitment (SCUC) problem and a heuristic genetic algorithm is utilized to solve this stochastic SCUC problem. To demonstrate the effectiveness of the proposed method, five deterministic and four stochastic case studies are implemented. Generation costs as well as different reserve strategies are discussed from the perspectives of system economics and reliability. Comparative results indicate that the planned generation costs and reserves are different from the realized ones. The stochastic models show better robustness than deterministic ones. Power systems run a higher level of risk during peak load hours.