Intra-day Operation Research Articles

Co-optimization for day-ahead scheduling and flexibility response mode of a hydro–wind–solar hybrid system considering forecast uncertainty of variable renewable energy

Hydropower, characterized by its quick ramping speed and energy buffering capacity, has the ability to respond to the flexibility demand caused by the forecast uncertainty of variable renewable energy (VRE). However, how to determine the flexibility supply of each hydropower station to ensure the reliable execution of the day-ahead scheduling in the intraday operation is a challenging issue for a hydro–wind–solar hybrid system (HWSHS). Due to complex characteristics of cascade hydropower stations, there is a mutual influence between the day-ahead scheduling and its execution. Therefore, flexibility response mode (FRM) is proposed to determine the flexibility supply of each hydropower station according to the flexibility demand of VRE, and a co-optimization model is constructed to optimize the day-ahead scheduling and the parameters of FRM considering the forecast uncertainty of VRE. A HWSHS in Guizhou province, China, is employed as a case study. The results suggest that the FRM can ensure the reliable execution of the day-ahead scheduling in the intraday operation. Compared to a contrast model without FRM, the execution deviation and its duration are decreased by more than 22% and 37%, respectively, in both long-run and out-of-sample analyses.

A novel stochastic multistage dispatching model of hybrid battery-electric vehicle-supercapacitor storage system to minimize three-phase unbalance

The unbalanced load distribution, the single-phase connection of renewable energy, and the uncoordinated charging of electric vehicles (EVs) will bring a severe issue corresponding to the three-phase unbalance in modern distribution networks. To deal with this issue, a novel multistage optimal dispatching model with the hybrid energy storage system (HESS), consisting of the battery energy storage system (BESS), EV, and supercapacitor (SC), is proposed in this paper. The first stage is conducted on the day-ahead stage, driven by minimizing the total operation cost and relieving power unbalance, the unbalanced penalty cost is introduced into the optimal dispatching model with the electricity purchasing cost and the degradation cost of HESS. Also, the chance-constraint stochastic programming (SP) model with the coordinated operation of HESS is accommodated to handle the diverse uncertainties of renewable energy, load demand, and EV users’ behaviors. In the intra-day operation stage, a rolling optimization-based dispatch model is formulated to re-optimize the operation of the SC for the rapid response of mitigating the real-time three-phase unbalance caused by renewable energy. The numerical experiments are executed on the IEEE 34-bus three-phase test system. Compared with the cases without considering SC and the degradation cost of HESS, the power unbalance in the proposed model is reduced by 3.27% and 3.62% per day at the real-time stage, respectively, while total operation cost is reduced by 46.77 US$ and 350.62 US$ per day, respectively. The results validated that the proposed model is efficient in reducing the three-phase unbalance under multi-time scales, improving the economic operation of the distribution network.

Stochastic Flexible Resource Operations in Coordinated Green-Seaport Energy-Logistics Systems Using Constraint Generation Approach

The increasing penetration of renewable energy sources (RESs) in modern green seaports calls for more flexible management approaches that can minimize the daily seaport operation costs and RES curtailments. This paper establishes an optimal strategy in two-time intervals for flexible operations of energy storage systems and combined electric-thermal power demands of reefer containers in the coordinated green-seaport energy-logistics systems (ELS). For the day-ahead operation, a multi-stage stochastic optimization model is established to minimize the expected ELS operation cost according to the 24-hour-ahead forecasts. Uncertainties of RES power outputs, load demands, electricity prices, and ambient temperatures are considered in the optimization of day-ahead operation. For the intra-day operation, hydrogen, battery, and thermal storage systems as well as reefer containers operations are optimized based on the short-lead-time forecasts. Numerical simulations are carried out based on the Jurong seaport operation in Singapore to verify the effectiveness of the proposed optimal operation strategy for the coordinated green-seaport ELS. Simulation results reveal that the proposed strategy can achieve the lowest operation cost and the lowest RES curtailment in a green-seaport.

Optimal Operation Scheduling of Multi-energy Complementary Systems Including Offshore Wind Power

Background: With the rapid development of offshore wind power in China, offshore wind power is accounting for an increasing proportion of the whole installed power generation in China, which put forward higher requirements for operation scheduling of multi-energy complementary systems including offshore wind power. However, existing studies have paid little attention to the complementary output scheduling of multi-energy systems with a high proportion of offshore wind power, this phenomenon is not conducive to the realization of reasonable operation scheduling. Objective: To realize the reasonable operation scheduling of multi-energy systems with a high proportion of offshore wind power. Methods: Firstly, the output characteristics of offshore wind power were analyzed and summarized, and modeling was conducted from the perspectives of time distribution, spatial correlation, volatility and randomness, which would serve as the theoretical basis for subsequent researches. Secondly, the complementary rule among offshore wind power and the multi-energy system was analyzed, and the complementary characteristic index was put forward. Then the safety operation was taken as the constraint, and the two-stage operation model of the integrated energy system was established, which considers the auxiliary service costs. In the first stage, the initial generation scheduling was developed according to the day-ahead forecast values of the offshore wind power; in the second stage, the more accurate intra-day operation scheduling was further achieved by considering the intra-day forecast values of offshore wind power and the day-ahead scheduling results. Costs of rotating reserve capacity could be reduced by correcting the deviation of generating capacity, then the revenue-maximizing scheduling strategies were obtained and the output of the pumped-storage power plants was optimized. Results: The effectiveness of the model proposed in this paper was verified based on the simulation examples. Compared with the traditional scheduling strategy, the scheduling strategy considering auxiliary service cost proposed in this paper can promote the consumption of offshore wind power, reduce the wind curtailment proportion and improve the electricity sales revenues in the end. Conclusion: The research results in this paper provide a reference for the reasonable consumption of multi-energy complementary systems with a high proportion of offshore wind power and improving electricity sales revenues

Health-aware coordinate long-term and short-term operation for BESS in energy and frequency regulation markets

The penetration of renewable energy in modern power systems is still increasing. Battery energy storage can rapidly respond to a dispatch order and is expected to provide multiple auxiliary services. However, deep charging cycles have negative impacts on battery health. This paper presents a health-aware long-term operation strategy for lithium-ion battery energy storage participating in the energy and frequency regulation markets. The strategy determines the capacity bounds that can be bid in the energy and frequency regulation markets and updates these bounds every three months, aiming to preserve battery health and increase market revenue. A long-term operational modeling framework is proposed to address the multi-timescale nature, integrating frequency control, energy arbitrage, and the evolution of battery degradation in a holistic model. A nonlinear degradation model is developed to approximate the health impact of main stress factors, which captures the nonlinearity of three-stage capacity degradation process caused by the formation of solid electrolyte interphase film and lithium plating. For intraday operation, a two-scale stochastic programming model is proposed, in which the market bidding and automatic generation control response are simulated in the timescales of one hour and two seconds, respectively. This simulation based method is closer to industrial practice, as it accounts for various factors in BESS operation. For the seasonal update of the capacity allocation strategy, a dynamic programming problem is established and solved based on the nonlinear degradation model and the daily revenue obtained from simulation of intraday operation; the capacity allocation strategy is determined from the renowned Principle of Optimality by Bellman. This two timescale modeling framework captures the interaction between short-term operation strategy and long-term degradation process. Numerical simulations validate that the proposed method can slow down battery degradation and increase lifetime revenue.

Cost Functions Over Feasible Power Transfer Regions of Virtual Power Plants

A virtual power plant (VPP) facilitates the integration of distributed energy resources (DERs) for transmission-level operations. A challenge in operating a VPP is to characterize the cost function over its feasible power transfer region under DERs' uncertainties. To address this challenge, a characterization method is proposed for the intraday operation of a VPP with nonanticipativity and robustness considerations to DERs' uncertainties. The characterization stems from designing operational requirements subject to nonanticipativity and robustness, by which a feasible power transfer region across all the time periods is constructed by boundary points at each time period and time-coupling constraints. Furthermore, a cost function over the feasible power transfer region is formulated as the combination of convex piecewise surfaces and a constant compensation cost. Finally, a perspective to analyze the impacts of DERs' uncertainties on feasible power transfer regions is studied. Numerical experiments in the IEEE 33-bus and 136-bus systems show that 1) our feasible power transfer region fulfills nonanticipativity and robustness to face DERs' uncertainties, 2) our cost function covers the true operational cost of a VPP, and 3) impacts of DERs' uncertainties on feasible power transfer regions can be measured by the product of their fluctuation intervals and system parameters.

A Method for Optimal Allocation of Distributed PV and Energy Storage Based on High Reliability of Distribution Network

In the context of the current widespread application of distributed PV, the energy storage system has bi-directional power characteristics and flexible regulation capability, and the reasonable joint planning of the two can enhance the safe and reliable operation of the system. In order to maximize long-term economic benefits, this paper proposes an optimal allocation method of distributed PV and energy storage based on high reliability of distribution network. Considering both normal operation and disaster scenarios for distributed photovoltaic storage planning, firstly, the investment and operation cost of distributed energy storage system is the target, and the storage planning model under normal operation scenario is established by considering the storage access location, configuration capacity, load state and grid operation state as constraints; then, the distributed photovoltaic storage optimal configuration model under disaster scenario is established by taking grid economy as the objective function. In summary, a two-layer planning model is established, with the upper layer using genetic algorithm to optimize the location, power and capacity of energy storage configuration, and the lower layer using particle swarm algorithm combined with tide calculation tool to optimize the intra-day operation strategy of energy storage; finally, the feasibility and effectiveness of the optimal configuration method are verified.

Enhancing Voltage Compliance in Distribution Network Under Cloud and Edge Computing Framework

While high penetration of DER significantly helps facilitate the decarbonization of the electric power grid, it also brings unexpected operational challenges, among which voltage compliance has been a significant concern. To address this issue, the efficient load profile forecast, the operational framework and related strategies are critical challenges that need to be addressed urgently. Hence, this paper presents the cloud-edge computing-based framework to effectively operate a coupled medium-voltage (MV) and low-voltage (LV) distribution network. The high computational efficiency in cloud computing and low data latency in edge computing are presented and explored to coordinate the day-ahead and intraday operations ranging different framework layers. Under the framework, a customer-level forecasting algorithm is employed to predict both day-ahead and real-time load profiles. Next, an optimization model based on unbalanced-three phase optimal power flow is proposed and solved by an efficient and accurate linearization-based approach that considers the controllability of on-line tap changers, distributed static var generator and the PV inverters. Simulations based on an extensive mocked MV-LV distribution network and show the proposed forecasting method is adopted in the real-time operations in terms of high accuracy, and demonstrate the efficiency of the proposed method in enhancing the voltage compliance in the network.

Risk-Based Reserve Scheduling for Active Distribution Networks Based on an Improved Proximal Policy Optimization Algorithm

Achieving the coordinated optimization of tie-line reserve and energy storage on two timescales is a key issue in reserve scheduling for active distribution networks. To effectively manage the uncertainty related to renewable energy, we propose risk-based reserve scheduling for active distribution networks based on an improved proximal policy optimization algorithm. The reserve scheduling for active distribution networks is constructed as a two-scale multistage stochastic programming model, in which intraday real-time operation is constructed as a multi-stage stochastic programming model. With the help of more accurate and realistic intraday operation scenario simulation, the energy storage can be fully utilized to improve the system’s ability to climb and peak, reduce the reserve pressure of the main network, and achieve the purpose of improving the flexibility of system operation. An improved proximal policy optimization algorithm is proposed to solve optimization problems in a model-free manner while effectively handling conditional value-at-risk constraints. The effectiveness of the proposed risk-based reserve scheduling for active distribution networks based on an improved proximal policy optimization algorithm is verified by a modified IEEE33 case.

Overcoming the uncertainty and volatility of wind power: Day-ahead scheduling of hydro-wind hybrid power generation system by coordinating power regulation and frequency response flexibility

Uncertainty and instantaneous volatility of wind power make it crucial to schedule the hydropower scientifically to supply flexibility at multiple timescales in renewable energy hybrid power generation systems (RHPS). However, current day-ahead scheduling strategies do not consider the flexibility of time scales below the minimum scheduling time resolution, resulting in insufficient flexibility in intraday operation, power curtailment and load loss. Therefore, in this paper, day-ahead scheduling model coordinating power regulation flexibility (PRF) at 15 min timescale and frequency response flexibility (FRF) at seconds timescale is proposed for hydro-wind hybrid power generation system (HWHPS). Meanwhile, the ability, benefits, and costs of hydropower on supplying flexibility in HWHPS are studied in detail. First, the demand and supply for PRF and FRF are defined and quantified at different confidence levels considering the regulation characteristics of hydropower units. Then, a heuristic piecewise method is utilized to linearize the nonlinear FRF expression, and a day-ahead scheduling MILP model of the HWHPS is constructed. The MILP model is applied to a HWHPS composed of a hydropower station in southwest China and a virtual wind farm simulated based on the data representative for the hydropower station region. We found that the available hydropower flexibility has potential in reducing the maximum frequency deviation, wind power curtailment and load loss. Meanwhile, hydropower provides flexibility at the cost of hydro energy loss due to increased water consumption, especially for supplying FRF during periods of high wind penetration. Finally, since benefits and costs are affected by different confidence levels, a comprehensive index is proposed to evaluate different capacity ratios and scenarios to suggest confidence levels for day-ahead scheduling. Decision makers can also refer to the flexibility supply capacity of hydropower station to select the connected wind power capacity and system load to ensure comprehensive benefits. The methods, models and conclusions in this study can provide a valuable reference for the planning and dispatching of HWHPS, which contributes to the reliability and stability.

Rolling horizon optimization for real-time operation of prosumers with Peer-to-Peer energy trading

Peer-to-Peer energy trading is recognized as a promising approach for prosumers to improve the operational economics. However, the uncertainty of renewable energy poses challenges to the reliable and economical operations of prosumers. This paper focuses on the real-time operation of prosumers. An efficient rolling horizon optimization-based real-time operation strategy for prosumers is proposed to minimize the total operational cost in one-day, in which the thermal comfort demand of the prosumers is also considered. The case study is finally conducted for 94 prosumers located at an urban community microgrid in a typical summer day scenario. Simulation results show that the proposed rolling horizon operation strategy can make full use of the flexible energy resources of prosumers, realizing the local energy supply–demand balance while improving prosumers’ operational economics. Moreover, the negative impact of day-ahead forecasting errors on the intra-day operation can be reduced significantly.

Stochastic Hybrid Approximation for Uncertainty Management in Gas-Electric Systems

Gas-fired generators, with their ability to quickly ramp up and down their electricity production, play an important role in managing renewable energy variability. However, these changes in electricity production translate into variability in the consumption of natural gas, and propagation of uncertainty from the electric grid to the natural gas system. To ensure that both systems are operating safely, there is an increasing need for coordination and uncertainty management among the electricity and gas networks. A challenging aspect of this coordination is the consideration of natural gas dynamics, which play an important role in intra-day operation, but give rise to a set of non-linear and non-convex equations that are hard to optimize even in the deterministic case. Ideally, the problem is formulated as a stochastic problem but many conventional methods for stochastic optimization are numerically intractable because they either incorporate a large number of scenarios directly or require the underlying problem to be convex. To address these challenges, we propose using a Stochastic Hybrid Approximation algorithm to more efficiently solve these problems and investigate the efficacy of several different variants of this algorithm. Our case study demonstrates that the proposed technique is able to quickly obtain high quality solutions and outperforms existing benchmarks such as Generalized Benders Decomposition. We demonstrate that coordinated uncertainty management that accounts for the gas system can significantly reduce both electric and gas system load shed in stressed conditions.

A holistic robust method for optimizing multi-timescale operations of a wind farm with energy storages

To ramp-up the utilization of wind energy, the uncertainty of wind power needs to be properly addressed in its integration into the power grid. The main challenge is to well tackle the risk induced by the uncertain wind power generation in different operation stages of a wind farm. This paper studies a novel bi-level multi-timescale scheduling approach for accommodating the wind power uncertainty via a robust optimization formulation. Wind farm day-ahead and intra-day operations consider different scheduling timescales. A two-stage robust optimization model is developed to plan the day-ahead power generation commitments to the power grid. A multi-stage robust optimization model is proposed to refine intra-day wind farm operation instructions. The overall decision-making process aims to minimize the wind farm operational cost. The proposed models are solved by using computationally tractable optimization methods, which offer a desired performance guarantee. A comprehensive case study based on real datasets to demonstrate the effectiveness and robustness of the proposed approach is carried out. Average cost reductions of 46.648% and 6.883% are achieved by comparing with the deterministic method and the static robust method, respectively. Results verify that the proposed method can provide optimal and robust wind farm operations schedules with a lower cost, higher flexibility, and less conservativeness. Results also demonstrate the feasibility of applying the proposed approach to the real-world wind farm and providing reliable operation guidelines.

Optimal intra-day operations of behind-the-meter battery storage for primary frequency regulation provision: A hybrid lookahead method

Battery energy storage systems (BESSs) are being widely installed behind-the-meter to reduce electricity bill. By providing grid ancillary services, behind-the-meter BESSs can increase potential revenue streams. This study targets the simultaneous electricity bill reduction and primary frequency regulation (PFR) provision. With the expansion of the application spectrum, the intra-day operations become more and more complicated. In this paper, a hybrid lookahead method with value function approximation strategy is proposed for intra-day operations, wherein the concept of “offline calculation—online application” is devised and implemented. The approximate value function is trained offline to represent the expected long-term benefit. A two-stage robust approximate dynamic programming (ADP) model is formulated for one day operation which is optimized to adjust the power baseline with a forward rolling horizon. Furthermore, multi-dimensional indicators are introduced to evaluate the proposed strategy. Simulations and benchmarking comparisons are performed for a 0.5 MW/1.0 MWh BESS to verify the superior performance of the proposed strategy. The results show that the approximate value function can be obtained offline with 99.07% convergence precision. Moreover, the proposed strategy can ensure the economic benefit and PFR provision within a short online computing time. The resulting intra-day economic benefit can reach 95.55% of the theoretical optimum, and the online optimization consumes only 4.65s for a prediction horizon of 5 min, which ensures the feasibility of real-time predictive optimization.

A multi-timescale operation model for hybrid energy storage system in electricity markets

The hybrid energy storage system (HESS) connecting different types of energy storage system (ESS) can be used to handle the several timescale variations of the components in power system. In this paper, a multi-timescale economic scheduling strategy for the HESSs to participate in the wholesale energy and reserve market considering the uncertainty of load and renewable generation. The economic multi-timescale HESS scheduling is a three-level model including day-ahead scheduling, intraday operation, and real-time operation. To better cover the hybrid requirements in the stochastic problem, the operation cycle limits model is introduced to avoid the frequent switching of energy-based ESS as well as the deviation cost of HESS’s capacity is used in the finer timescale to guide the future requirement. An improved algorithm that integrates the Progressive Hedging (PH) and Dual Decomposition (DD) algorithms are used to solve this problem. The proposed strategy can realize the efficient management of HESSs to reduce the energy management complexity and system operation costs with high computation efficiency, which indicates its promising application in the HESS economic scheduling.

Techno-Economic Analysis of a Concentrating Solar Power Plant Using Redox-Active Metal Oxides as Heat Transfer Fluid and Storage Media

We present results for a one-dimensional quasi-steady-state thermodynamic model developed for a 111.7 MWe concentrating solar power (CSP) system using a redox-active metal oxide as the heat storage media and heat transfer agent integrated with a combined cycle air Brayton power block. In the energy charging and discharging processes, the metal oxide CaAl0.2Mn0.8O2.9-δ (CAM28) undergoes a reversible, high temperature redox cycle including an endothermic oxygen-releasing reaction and exothermic oxygen-incorporation reaction. Concentrated solar radiation heats the redox-active oxide particles under partial vacuum to drive the reduction extent deeper for increased energy density at a fixed temperature, thereby increasing storage capacity while limiting the required on sun temperature. Direct counter-current contact of the reduced particles with compressed air from the Brayton compressor releases stored chemical and sensible energy, heating the air to 1,200°C at the turbine inlet while cooling and reoxidizing the particles. The cool oxidized particles recirculate through the solar receiver subsystem for another cycle of heating and reduction (oxygen release). We applied the techno-economic model to 1) size components, 2) examine intraday operation with varying solar insolation, 3) estimate annual performance characteristics over a simulated year, 4) estimate the levelized cost of electricity (LCOE), and 5) perform sensitivity analyses to evaluate factors that affect performance and cost. Simulations use hourly solar radiation data from Barstow, California to assess the performance of a 111.7 MWe system with solar multiples (SMs) varying from 1.2 to 2.4 and storage capacities of 6–14 h. The baseline system with 6 h storage and SM of 1.8 has a capacity factor of 54.2%, an increase from 32.3% capacity factor with no storage, and an average annual energy efficiency of 20.6%. Calculations show a system with an output of 710 GWhe net electricity per year, 12 h storage, and SM of 2.4 to have an installed cost of $329 million, and an LCOE of 5.98 ¢/kWhe. This value meets the U.S. Department of Energy’s SunShot 2020 target of 6.0 ¢/kWhe (U. S Department of Energy, 2012), but falls just shy of the 5.0 ¢/kWhe 2030 CSP target for dispatchable electricity (U. S Department of Energy, 2017). The cost and performance results are minimally sensitive to most design parameters. However, a one-point change in the weighted annual cost of capital from 8 to 7% (better understood as a 12.5% change) translates directly to an 11% decrease (0.66 ¢/kWhe) in the LCOE.

Optimal operation framework of customer-premise battery storage for energy charge reduction and primary frequency regulation

This paper focuses on the intra-day operation of customer-premise battery storage for multiple applications, in particular energy charge reduction and primary frequency regulation (PFR). The nature of this problem requires the battery to provide reliable PFR, as well as coordinate online optimization performance with computing time while accounting for multidimensional stochastic information. Therefore, we propose a novel optimal operation framework. In the day-ahead phase, the piecewise linear functions (PLFs) are trained offline to approximate the expected operating cost of the long-term future. During the intra-day operation, a two-stage approximate dynamic programming (ADP) model is formulated utilizing the updated short-term future information and the PLFs based long-term estimation. To ensure the reliability of frequency regulation, we develop a robust correction strategy of PFR signals. Combining the correction feedback, the two-stage ADP model is optimized through rolling horizon procedures to dynamically adjust the power basepoint. Numerical simulations demonstrate the performance of our proposed operation framework. The convergence rate of PLFs offline training is 97.3%. In addition, the intra-day operating cost of our proposed framework is merely 1.62% higher than the theoretical optimum, while the online calculation takes only 3.25 s for one rolling period.

Operation of Natural Gas Pipeline Networks With Storage Under Transient Flow Conditions

We formulate a nonlinear optimal control problem for intra-day operation of a natural gas pipeline network that includes storage reservoirs. The dynamics of compressible gas flow through pipes, compressors, reservoirs, and wells are considered. In particular, a reservoir is modeled as a rigid, hollow container that stores gas under isothermal conditions and uniform density, and a well is modeled as a vertical pipe. For each pipe, flow dynamics are described by a coupled partial differential equation (PDE) system in density and mass flux variables, with momentum dissipation modeled using the Darcy-Wiesbach friction approximation. Compressors are modeled as scaling up the pressure of gas between inlet and outlet. The governing equations for all network components are spatially discretized and assembled into a nonlinear differential-algebraic equation (DAE) system, which synthesizes above-ground pipeline and subsurface reservoir dynamics into a single reduced-order model. We seek to maximize an objective function that quantifies economic profit and network efficiency subject to the flow equations and inequalities that represent operating limitations. The problem is solved using a primal-dual interior point solver and the solutions are validated in computational experiments and simulations on several pipeline test networks to demonstrate the effectiveness of the proposed methodology.

The effects of intraday operation time on pain and anxiety of patients undergoing septoplasty

IntroductionAnxiety and pain levels of septoplasty patients may vary according to intraday operation time. ObjectiveTo investigate the effects of septoplasty operation and intraday operation time on anxiety and postoperative pain. MethodsNinety-eight voluntary patients filled out the hospital anxiety and depression scale to measure the anxiety level three weeks before, one hour before and one week after surgery. Forty-nine patients were operated at 8:00am (morning group); other 49 were operated at 03:00pm (afternoon group). We used a visual analogue scale to measure postoperative pain. Preoperative and postoperative scores were compared, as were the scores of the groups. ResultsMedian hospital anxiety and depression scale scores one hour before the operation [6 (2–10)] were significantly higher compared to the median scores three weeks before the operation [3 (1–6)] (p< 0.001), and one week after the operation [2 (1–6)] were significantly lower compared to the median scores three weeks before the operation [3 (1–6)] (p< 0.001). Hospital anxiety and depression scale scores one hour before the operation were significantly greater in the afternoon group [8 (7–10)], compared to the morning group [4 (2–6)] (p< 0.001). Postoperative first, sixth, twelfth and twenty-fourth-hour pain visual analogue scale scores were significantly higher in the afternoon group compared to the morning group (p< 0.001). ConclusionSeptoplasty might have an increasing effect on short-term anxiety and postoperative pain. Performing this operation at a late hour in the day might further increase anxiety and pain. However, the latter has no long-term effect on anxiety.

Stochastic Unit Commitment and Optimal Power Trading Incorporating PV Uncertainty

This paper focuses on the optimal unit commitment (UC) scheme along with optimal power trading for the Northeast Power System (NEPS) of Afghanistan with a penetration of 230 MW of PV power energy. The NEPS is the biggest power system of Afghanistan fed from three main sources; 1. Afghanistan’s own power generation units (three thermal units and three hydro units); 2. imported power from Tajikistan; 3. imported power from Uzbekistan. PV power forecasting fluctuations have been handled by means of 50 scenarios generated by Latin-hypercube sampling (LHS) after getting the point solar radiation forecast through the neural network (NN) toolbox. To carry out the analysis, we consider three deterministic UC and two stochastic UC cases with a two-stage programming model that indicates the day-ahead UC as the first stage and the intra-day operation of the system as the second stage. A binary-real genetic algorithm is coded in MATLAB software to optimize the proposed cases in terms of thermal units’ operation costs, import power tariffs, as well as from the perspective of the system reliability risks expressed as the reserve and load not served costs. The results indicate that in the deterministic UC models, the risk of reserve and load curtailment does exist. The stochastic UC approaches including the optimal power trading are superior to the deterministic ones. Moreover, the scheduled UC costs and reserves are different from the actual ones.