Search

Highlights • Novel formulation of unit commitment model for large-scale power system simulation. • Validated with standard test system and applied to real regional systems in China. • Increase the computational speed by more than 20,000 times with ∼1% error. • Can be applied to planning issues, e.g. optimal sizing of wind, solar and hydro. Abstract Deploying high penetration of variable renewables represents a critical pathway for decarbonizing the power sector. Hydro power (including pumped-hydro), batteries, and fast responding thermal units are essential in providing system flexibility at elevated renewable penetration. How to quantify the merit of flexibility from these sources in accommodating variable renewables, and to evaluate the operational costs considering system flexibility constraints have been central challenges for future power system planning. This paper presents an improved linear formulation of the unit commitment model adopting unit grouping techniques to expedite evaluation of the curtailment of renewables and operational costs for large-scale power systems. All decision variables in this formulation are continuous, and all chronological constraints are formulated subsequently. Tested based on actual data from a regional power system in China, the computational speed of the model is more than 20,000 times faster than the rigorous unit commitment model, with less than 1% difference in results. Hourly simulation for an entire year takes less than 3 min. The results demonstrate strong potential to apply the proposed model to long term planning related issues, such as flexibility assessment, wind curtailment analysis, and operational cost evaluation, which could set a methodological foundation for evaluating the optimal combination of wind, solar and hydro investments. [ABSTRACT FROM AUTHOR]

Power grid resilience has been a major concern as extreme weather events become more frequent recently. Particularly, wind power generation has constituted a significant body of generation portfolio worldwide. Under stormy weather conditions, wind power generation would be curtailed due to the over-speed protection of wind turbines. Subsequently, the sharply reduced generation resources would significantly endanger the reliability of power supply. In this study, the authors propose to apply the risk-limiting methodology to improve the efficient utilisation of wind power prior to reluctant wind curtailment on the advent of wind stormy events. The proposed method integrates three programs, namely, day-ahead unit commitment (UC), hourly-ahead UC, and real-time load shedding into one single model along with risk-limiting constraints. The first program provides the baseline of hourly dispatch; whereas, the latter two programs serve as recourse means while wind stormy event unfolds. The proposed model is cast into a multi-stage mixed-integer linear programming model and solved by commercial solvers. Illustrative examples demonstrate that the proposed method can reduce the total dispatch cost over the time-horizon including the wind stormy event, by postponing the timing of proactively starting quick-start generating units and shedding loads, in contrast to conventional two-stage stochastic dispatch methods. [ABSTRACT FROM AUTHOR]

The penetration of renewable energy resources into the power system is rising prominently worldwide, and is causing increased uncertainty in power system operations. Increasing loads and scarce transmission resources have exacerbated this issue, imposing critical operational challenges, such as congestion issues, on the power system operator (SO). This study proposes a unit commitment that employs dynamic line rating (DLR) to develop a method that enables efficient use of network resources. A mathematical formulation is suggested, based on two-stage stochastic programming, to manage the newly introduced uncertainties related to DLR properly. In addition, we build approximated power-flow equations for the proposed model. The performance of the proposed method is verified using a six-bus system and the modified IEEE 118-bus system integrated with wind power units. The simulation results clearly demonstrate that, with DLR, an SO can significantly reduce the expected operating cost. This advantage stems from the sufficiently effective utilization of the existing transmission line, which leads to the optimal commitment of the generating units and dispatched volume, and efficient use of the harvested wind energy. Moreover, the results, which demonstrate that the expected-energy-not-supplied is markedly reduced by incorporating the DLR, prove that the proposed method is highly reliable. [ABSTRACT FROM AUTHOR]

Nuclear power plants are commonly operated in a “baseload” mode at maximum rated capacity whenever online. However, nuclear power plants are technically capable of flexible operation, including changing power output over time (ramping or load following) and providing frequency regulation and operating reserves. At the same time, flexibility is becoming more valuable as many regions transition to low-carbon power systems with higher shares of variable renewable energy sources such as wind or solar power. We present a novel mixed integer linear programming formulation to more accurately represent the distinct technical operating constraints of nuclear power stations, including impacts of xenon transients in the reactor core and changing core reactivity over the fuel irradiation cycle. This novel representation of nuclear flexibility is integrated into a unit commitment and economic dispatch model for the power system. In a case study using representative utility data from the Southwest United States, we investigate the potential impacts of flexible nuclear operations in a power system with significant solar and wind energy penetration. We find that flexible nuclear operation lowers power system operating costs, increases reactor owner revenues, and substantially reduces curtailment of renewables. [ABSTRACT FROM AUTHOR]

The variable and intermittent nature of large-scale wind power integration makes day-ahead unit commitment (UC) decision-making difficult. This paper establishes a novel and effective UC model with wind power integration by optimizing the utilization of the forecast error and reserve decision. First, considering the temporal feature of the UC model, a time sequence segment-fitting method (TSFM) for the wind power forecast error is presented, in which the non-parametric fitting method is used to address the ‘fat-tail’ effect of error distribution. Second, according to the probability intervals of the forecast error and characteristic of the reserve, a new reserve decision method is proposed to define three classes of reserve strategies and optimize the capacity for each type of reserve. Third, a UC model with time-varying confidence levels is established by introducing conditional value at risk (CVaR) and chance-constrained programming (CCP), and is linked with the TSFM. This novel model can balance the costs of fuel, various reserves, load shedding risk, and wind curtailment risk, which can improve the economy of the power grid operation. Finally, an improved hybrid particle swarm optimization algorithm with a heuristic searching strategy is applied to solve this multivariate mixed integer non-linear programming problem. The simulation results verify the effectiveness and practicality of the model proposed. [ABSTRACT FROM AUTHOR]

The increasing penetration of uncertain generation such as wind and solar in power systems imposes new challenges to the unit commitment (UC) problem, one of the most critical tasks in power systems operations. The two most common approaches to address these challenges — stochastic and robust optimization — have drawbacks that restrict their application to real-world systems. This paper demonstrates that, by considering dispatchable wind and a box uncertainty set for wind availability, a fully adaptive two-stage robust UC formulation, which is typically a bi-level problem with outer mixed-integer program (MIP) and inner bilinear program, can be translated into an equivalent single-level MIP. Experiments on the IEEE 118-bus test system show that computation time, wind curtailment, and operational costs can be significantly reduced in the proposed unified stochastic–robust approach compared to both pure stochastic approach and pure robust approach, including budget of uncertainty. [ABSTRACT FROM AUTHOR]

Unified Power Flow Controller (UPFC) is recognized as the most powerful flexible AC transmission systems (FACTS) device for power system operation. This paper addresses how UPFC explores the transmission flexibility and facilitates the integration of uncertain and volatile wind power generation. To this end, a comprehensive unit commitment (UC) model with UPFC and uncertain wind power generation is proposed. Then, some metrics are introduced to evaluate the impacts of UPFC on the reliability, security and economy of power system operation. Further, different dispatch strategies of UPFC are compared to provide helpful guidances on making full use of UPFC to hedge against uncertainties. In addition, facing the challenging mixed-integer non-linear non-convex problems, approximate models are proposed to provide a starting point to solve the problems efficiently. All these models are easy to adapt to other types of FACTS devices. Illustrative numerical results are provided. [ABSTRACT FROM AUTHOR]

Summary: This paper establishes a probabilistic scenario–based framework for the stochastic dynamic economic emission dispatch with unit commitment (SDEED‐UC) problem, taking into account wind power uncertainty. To solve the stochastic UC problem, this paper presents a probabilistic scenario analysis approach to find the unit scheduling solution considering all the original scenarios under a predetermined probability level. And a reduced scenario set can be obtained by the simultaneous backward method. Consequently, the SDEED problem is decomposed into a number of deterministic multiobjective dispatch problems. For each scenario, an enhanced multiobjective particle swarm optimization (EMOPSO) algorithm integrated with the handling strategy for system operation constraints is proposed to produce the Pareto optimal solutions. Furthermore, the available reserve capacity by the UC solution is verified. The simulation results demonstrate that the proposed probabilistic scenario–based and EMOPSO approach is practicable for solving SDEED‐UC from the perspectives of system operation economy, emission, and reliability simultaneously. [ABSTRACT FROM AUTHOR]

Electric power systems worldwide are receiving an increasing volume of wind power generation (WPG) because of environmental concerns and cost declines associated with technological innovation. To manage the uncertainty of WPG, a system operator must commit sufficient conventional generators to provide an appropriate reserve. At times, frequent start and stop operations are applied to certain generators, which incurs maintenance costs associated with thermal-mechanical fatigue. In this paper, we suggest a comprehensive approach to unit commitment (UC) that considers maintenance cost: the parameters of equivalent start (ES) and equivalent base load hours (EBHs) are adopted in the UC problem to determine optimal generation scheduling. A new formulation for the maintenance cost that can be readily combined with an existing mixed integer linear programming algorithm is presented. The effectiveness of the proposed UC method is verified through simulations based on an IEEE 118-bus test system. The simulation results show that considering maintenance cost in the UC problem effectively restricts frequent start and stop operation scheduling. Furthermore, the operating cost is reduced, the required reserve level is maintained, and the computational time is comparable with that of the conventional UC method. [ABSTRACT FROM AUTHOR]

In this work, we develop a mathematical model and framework to represent rolling-horizon unit commitment (UC) processes with multiple periodicities. In control center operations, UC is solved repeatedly to adjust device commands based on new information about load, generation availability, renewable energy production, and other aspects of system state as uncertain conditions are realized. We develop a three-level model including 24-h UC, rolling-horizon UC (RHUC), and economic dispatch (ED) and give formulations for the three problems including interdependencies. This framework allows for evaluation of, among other things, different periodicities of the rolling horizon problem and the benefits of more accurate forecasting information. Experimental results are shown for a 6-bus system and a 3012-bus system with wind generation in two wind scenarios. Although the generation costs are very similar, the deviation between RHUC schedules and actual deployment is noted to be superior for a 20-min periodicity compared to a 30-min periodicity. Additionally, less reserve is deployed in the 20-min RHUC case. [ABSTRACT FROM AUTHOR]

Because wind power spillage is barely considered, the existing robust unit commitment cannot accurately analyze the impacts of wind power accommodation on on/off schedules and spinning reserve requirements of conventional generators and cannot consider the network security limits. In this regard, a novel double-level robust security-constrained unit commitment formulation with optimizable interval of uncertain wind power output is firstly proposed in this paper to obtain allowable interval solutions for wind power generation and provide the optimal schedules for conventional generators to cope with the uncertainty in wind power generation. The proposed double-level model is difficult to be solved because of the invalid dual transform in solution process caused by the coupling relation between the discrete and continuous variables. Therefore, a two-stage iterative solution method based on Benders Decomposition is also presented. The proposed double-level model is transformed into a single-level and two-stage robust interval unit commitment model by eliminating the coupling relation, and then this two-stage model can be solved by Benders Decomposition iteratively. Simulation studies on a modified IEEE 26-generator reliability test system connected to a wind farm are conducted to verify the effectiveness and advantages of the proposed model and solution method. [ABSTRACT FROM AUTHOR]

The renewable energy sources (RES) integration in the electricity supply utilities can reduce the energy procurement costs as well as the environmental concerns. Wind power is the most popular form of RES which is vastly utilized worldwide. This paper proposes a robust model for unit commitment (UC) problem, minimizing the operating costs considering uncertainty of wind power generation. In order to handle the uncertainties arising from volatile nature of wind power, information gap decision theory (IGDT) is utilized, where risk averse (RA) and opportunity seeker (OS) strategies are developed. RA strategy gives a robust decision making tool for handling the severe uncertainty of wind power, whereas the OS strategy makes benefit of possible uncertainties by adjusting the decision variables in a right way. Besides, the impact of demand flexibility (or demand response) on the operation costs is also investigated. The proposed model is examined on the IEEE 118-bus test system, and its benefits over the existing stochastic programming technique is examined. The obtained results demonstrate the applicability of the proposed method to deal with the UC problem with uncertain wind power generation. It is also observed that demand flexibility has positive impacts in both RA and OS strategies. [ABSTRACT FROM AUTHOR]

Unit commitment, one of the significant tasks in power system operations, faces new challenges as the system uncertainty increases dramatically due to the integration of time-varying resources, such as wind. To address these challenges, we propose the formulation and solution of a generalized unit commitment problem for a system with integrated wind resources. Given the prespecified interval information acquired from real central wind forecasting system for uncertainty representation of nodal wind injections with their correlation information, the proposed unit commitment problem solution is computationally tractable and robust against all uncertain wind power injection realizations. We provide a solution approach to tackle this problem with complex mathematical basics and illustrate the capabilities of the proposed mixed integer solution approach on the large-scale power system of the Northwest China Grid. The numerical results demonstrate that the approach is realistic and not overly conservative in terms of the resulting dispatch cost outcomes. [ABSTRACT FROM AUTHOR]

Wind power and photovoltaic power, two types of renewable energy (RE), have made large inroads into the power system. In this paper, we study a unit commitment (UC) problem that considers the uncertainty in RE and pumped hydro-energy storage (PHES). To improve the optimisation performance for this problem, we propose a novel heuristic algorithm called the Binary Artificial Sheep Algorithm (BASA) that is based on the social behaviour of sheep flock. To evaluate the effect of the uncertainty of RE, a scenario evaluation method is defined to assess quantitatively the stability and economy of the UC results with respect to different levels of RE forecasting errors. In addition, we investigate and analyse the effect of PHES on the UC problem. Three UC test systems with different RE and PHES combinations are used to verify the feasibility and effectiveness of the proposed BASA as well as its performance. The proposed BASA performed better than traditional fundamental metaheuristics in solving UC problems. Our results also demonstrated that the equivalent load fluctuation and operating costs of the thermal units will increase significantly with an increase in RE power forecast error, but the PHES can effectively counterbalance this adverse effect. [ABSTRACT FROM AUTHOR]

With the increased share of power generation based on wind energy, the complexity of the unit commitment (UC) and economic dispatch (ED) problems increases due to the stochastic nature of wind power. Therefore, an accurate and fast optimization method is needed when the generation process involves large quantities of wind sources to effectively manage generation mix and load requirements. In this paper, model predictive control (MPC) is used to solve power system UC/ED problems with the presence of wind energies. Because the UC/ED problem is nonlinear, non-convex and mixed integer problem, the MPC must be used as nonlinear. To produce a nonlinear MPC (NMPC), MPC must be integrated with a fast optimization methodology. This paper presents a generic mathematical formula for a NMPC, integrated with swarm optimization technique to describe the nonlinear behavior in the mathematical formulation. This new formulation will be called swarm model predictive control (SMPC) optimization. The control model will be able to address the effect of the system disturbances and fluctuations using a controlled autoregressive integrated moving average (CARIMA). A general form of future predictions can be expressed as a function of input and output past data, and a future control sequence, and the degree of freedom in the SMPC problem. Also, the prediction part improves the swarm technique, because it identifies the size of search space in a better way. In this paper, UC schedule is designed using the swarm technique offline, while ED is solved using the proposed SMPC optimization method on real-time basis and fed into the automatic generation control system. There is no load deficit in real time ED results in less spinning reserve requirements compared with swarm optimization and dynamic program techniques used discretized load. The system under study is a standard IEEE 30 bus modelled in MATLAB environment, with all data from the city of Florida, USA. [ABSTRACT FROM AUTHOR]

We propose a probabilistic unit commitment problem with incentive-based demand response and high level of wind power. Our novel formulation provides an optimal allocation of up/down spinning reserve. A more efficient unit commitment algorithm based on operational cycles is developed. A multi-period elastic residual demand economic model based on the self- and cross-price elasticities and customers’ benefit function is used. In the proposed scheme, the probability of residual demand falling within the up/down spinning reserve imposed by n − 1 security criterion is considered as a stochastic constraint. A chance-constrained method, with a new iterative economic dispatch correction, wind power curtailment, and commitment of cheaper units, is applied to guarantee that the probability of loss of load is lower than a pre-defined risk level. The developed architecture builds upon an improved Jaya algorithm to generate feasible, robust and optimal solutions corresponding to the operational cost. The proposed framework is applied to a small test system with 10 units and also to the IEEE 118-bus system to illustrate its advantages in efficient scheduling of generation in the power systems. [ABSTRACT FROM AUTHOR]

Wind power probabilistic forecast is being used as input in several decision-making problems, such as stochastic unit commitment, operating reserve setting and electricity market bidding. This work introduces a new on-line quantile regression model based on the Reproducing Kernel Hilbert Space (RKHS) framework. Its application to the field of wind power forecasting involves a discussion on the choice of the bias term of the quantile models, and the consideration of the operational framework in order to mimic real conditions. Benchmark against linear and splines quantile regression models was performed for a real case study during a 18 months period. Model parameter selection was based on k -fold crossvalidation. Results showed a noticeable improvement in terms of calibration, a key criterion for the wind power industry. Modest improvements in terms of Continuous Ranked Probability Score (CRPS) were also observed for prediction horizons between 6 and 20 h ahead. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm to deal with thermal Unit Commitment which takes into account the intermittency and volatility of the renewable energies such as wind and solar energies. Dynamic Programming (DP) integrating Priority Listing order (PL) based on Best Per Unit Cost (BP) was applied to commit the thermal units in an isolated island with generators based on renewable sources. In this work, the effects of a high time resolutions such as 60, 30, 15, 10 and 5 min on production costs, reserves and intermittent generators are investigated. In order to demonstrate the capability of the proposed algorithm, two cases were studied. Firstly, a test system composed of ten diesel generators, three wind turbines and one Photovoltaic (PV) power plant is examined and then the IEEE 118-bus test system, integrating wind and PV power plants, is considered. The presented simulation results show that a proper schedule for each generation unit can be reached at a time resolution closer to real time unit commitment and economic dispatch while a high level of reliability can be guaranteed by assuring practical constraints fulfillment. [ABSTRACT FROM AUTHOR]

The use of appropriate hourly reserve margins can maintain power system security by balancing supply and demand in the presence of errors in the forecast demand, generation outages, or errors in the forecast of wind power generation. Because the cost of unit commitment increases with larger reserve margins, cost analysis to determine the most economical reserve margin is an important issue in power system operation. Here, we define the "short-term reliability of balance" and describe a method to determine the reserve margin based on the short-term reliability of balance. We describe a case study, in which we calculate the reserve margin using this method with various standards of short-term reliability of balance. A cost analysis is then performed to determine the most economic standard, and a comparison between our method and a conventional method is carried out. The results show that our method with an economic short-term reliability of balance enables more reliable and efficient operation of the power system. Moreover, with an hourly reserve margin, we show that an increase in wind power generation can result in a significant decrease in the operating cost, which makes wind power generation economically viable. [ABSTRACT FROM AUTHOR]

Wind generation with a low capacity factor and uncertain nature poses many challenges in grid integration and secure operation of power system. This demands larger quantities of balancing services known as Ancillary services (AS), at cheaper cost in short notice of time. Operating Reserve is a major portion of Ancillary service that can balance the power system with wind integration uncertainties. Utilizing Energy Storage and Demand Response as Operating Reserve, maximum wind energy integration can be achieved in a constrained power system. This paper presents the participation of Pumped Hydro Storage (PH) and Demand Response (DR) as Operating Reserve Ancillary services (ORAS) to mitigate the issues related to wind power integration into the system. Simultaneous scheduling of PH and DR in Energy and Ancillary service markets with wind uncertainties has been attempted by solving Probabilistic unit commitment problem using Lagrangian Relaxation (LR) method. Various cases studied by emphasizing on optimization of operating cost and cost of Operating Reserve are presented and results are found to be encouraging. [ABSTRACT FROM AUTHOR]

In this paper, a methodology to solve Unit Commitment (UC) problem from a probabilistic perspective is developed and illustrated. The method presented is based on solving the Economic Dispatch (ED) problem describing the Probability Distribution Function (PDF) of the output power of thermal generators, energy not supplied, excess of electricity, Generation Cost (GC), and Spinning Reserve (SR). The obtained ED solution is combined with Priority List (PL) method in order to solve UC problem probabilistically, giving especial attention to the probability of providing a determined amount of SR at each time step. Three case studies are analysed; the first case study explains how PDF of SR can be used as a metric to decide the amount of power that should be committed; while in the second and third case studies, two systems of 10-units and 110-units are analysed in order to evaluate the quality of the obtained solution from the proposed approach. Results are thoroughly compared to those offered by a stochastic programming approach based on mixed-integer linear programming formulation, observing a difference on GCs between 1.41% and 1.43% for the 10-units system, and between 3.75% and 4.5% for the 110-units system, depending on the chosen significance level of the probabilistic analysis. [ABSTRACT FROM AUTHOR]

In this paper, a two-stage stochastic unit commitment (UC) model considering flexible scheduling of demand response (DR) is proposed. In the proposed UC model, the DR resources can be scheduled: (1) in the first stage, as resources on a day-ahead basis to integrate the predicted wind fluctuation with lower uncertainty; (2) in the second stage, as resources on an intra-day basis to compensate for the deviation among multiple wind power scenarios considering the coupling relationship of DR on available time and capacity. Simulation results on the Pennsylvania-New Jersey-Maryland (PJM) 5-bus system and IEEE 118-bus system indicate that the proposed model can maximize the DR value with lower cost. Moreover, different types of DR resources may vary in the contract costs (capacity costs), the responsive costs (energy costs), the time of advance notice, and the minimum on-site hours. The responsive cost is considered as the most important factor affecting DR scheduling. In addition, the first-stage DR is dispatched more frequently when transmission constraints congestion occurs. [ABSTRACT FROM AUTHOR]

This paper proposes a formulation for a stochastic security constrained unit commitment, dispatch and reserve scheduling, considering non-spinning reserves. We employ a randomized optimization technique that is based on scenario generation of the uncertain variables, in this case the wind power, and offers probabilistic certificates regarding the robustness properties of the resulting solution. To demonstrate the efficacy of the proposed methodology, we carry out a simulation based study on the IEEE 30 bus system comparing the proposed formulation to other variants, as well as examine its behavior on networks with different congestion levels. In all case studies, we verify empirically, via Monte Carlo simulations, the probabilistic performance of our solution methodology. [ABSTRACT FROM AUTHOR]

Classic unit commitment (UC) is an important and exciting task of distributing generated power among the committed units subject to several constraints over a scheduled time horizon to obtain the minimum generation cost. Large integration of distributed energy resources (DERs) in modern power system makes generation planning more complex. This paper presents the individual and collective impact of three distributed energy resources (DERs), namely, wind power generator as a renewable energy source, plug-in electric vehicles (PEVs) and emergency demand response program (EDRP) on unit commitment. In this paper, an inconsistent nature of wind speed and wind power is characterized by the Weibull probability distribution function considering overestimation and underestimation cost model of the stochastic wind power. The extensive economic analysis of UC with DERs is carried out to attain the least total cost of the entire system. To obtain the optimum solution, Teaching–learning based optimization (TLBO) algorithm is employed to solve the unit commitment problem considering IEEE standard 10 unit test system in this study. It is found that the combined effect of wind power generator, plug-in electric vehicles and emergency demand response program on UC significantly lessen the total cost of the system. [ABSTRACT FROM AUTHOR]

The increment in generation costs is one of the most important factors that characterizes the operation of insular power systems, and is related to the location of these systems and the type of fuel used to provide electricity. This situation motivates the integration of renewable generation at high rates, as well as energy storage systems (ESSs), to improve the utilization of these resources. In this paper, a new control strategy is presented for the day-ahead scheduling of insular power systems with a battery energy storage system. The method presented here incorporates the effects of the most relevant components such as thermal generators, wind power generation, power converter, charge controller and ESS, being integrated into the scheduling process of insular power systems as a new contribution to earlier studies. The results provided show a fuel saving of 2% and an improvement in the wind power use of 20%, which is significant. [ABSTRACT FROM AUTHOR]

The integration of wind power requires the power system to be sufficiently flexible to accommodate its forecast errors. In the market clearing process, the scheduling of flexibility relies on the manner in which the wind power uncertainty is addressed in the unit commitment (UC) model. This paper presents a novel risk-based day-ahead unit commitment (RUC) model that considers the risks of the loss of load, wind curtailment and branch overflow caused by wind power uncertainty. These risks are formulated in detail using the probabilistic distributions of wind power probabilistic forecast and are considered in both the objective functions and the constraints. The RUC model is shown to be convex and is transformed into a mixed integer linear programming (MILP) problem using relaxation and piecewise linearization. The proposed RUC model is tested using a three-bus system and an IEEE RTS79 system with wind power integration. The results show that the model can dynamically schedule the spinning reserves and hold the transmission capacity margins according to the uncertainty of the wind power. A comparison between the results of the RUC, a deterministic UC and two scenario-based UC models shows that the risk modeling facilitates a strategic market clearing procedure with a reasonable computational expense. [ABSTRACT FROM PUBLISHER]

The stochastic and intermittent characteristic of wind power present great challenge to the operation of power system. A novel unit commitment problem (UCP) model is proposed in this paper. As the status of thermal units need to be described by binary number, Binary Particle Swarm Optimization (BPSO) algorithm is proposed to find the optimum schedule scheme. Case studies on the 10 units system illustrate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Integrating wind power generation into bulk power grids imposes significant challenging to system operations due to the stochastic variation and limited predictability of wind power generation. One of the key issues is how to optimally schedule various units and determine a proper spinning reserve level to guarantee a reliable operation of the integrated system in a robust and economic manner. In this paper, we propose a two-level unit commitment (TLUC) for power systems facing large uncertainties in the context of adjustable robust optimization. The commitment decision acts as first-stage decisions that need to be robust against uncertainty, whereas the output decision acts as adjustable decisions that is decided after uncertainty is revealed. This decision mechanism naturally decomposes a conventional unit commitment (UC) problem into two levels, the upper level provides unit working status, whereas the lower level checks if there is at least one dispatch solution under uncertainty subject to the status. Unlike conventional UC, where the reserve level is fixed from operating experiences, in TLUC, the reserve level also acts as a decision variable in the upper level that controls the robustness of UC decisions. A heuristic algorithm is developed to solve this TLUC problem, providing the robust UC decision against uncertainty as well as the minimal spinning reserve level required to maintain reliability. Numerical experiments are carried out on the IEEE 39-bus system and large-scale realistic Guangdong power grid in China and demonstrate the effectiveness of our approach. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a two-stage stochastic unit commitment (UC) model, which integrates non-generation resources such as demand response (DR) and energy storage (ES) while including risk constraints to balance between cost and system reliability due to the fluctuation of variable generation such as wind and solar power. This paper uses conditional value-at-risk (CVaR) measures to model risks associated with the decisions in a stochastic environment. In contrast to chance-constrained models requiring extra binary variables, risk constraints based on CVaR only involve linear constraints and continuous variables, making it more computationally attractive. The proposed models with risk constraints are able to avoid over-conservative solutions but still ensure system reliability represented by loss of loads. Then numerical experiments are conducted to study the effects of non-generation resources on generator schedules and the difference of total expected generation costs with risk consideration. Sensitivity analysis based on reliability parameters is also performed to test the decision preferences of confidence levels and load-shedding loss allowances on generation cost reduction. [ABSTRACT FROM AUTHOR]

The ability of load to respond to short-term variations in electricity prices plays an increasingly important role in balancing short-term supply and demand, especially during peak periods and in dealing with fluctuations in renewable energy supplies. However, price responsive load has not been included in standard models for defining the optimal scheduling of generation units in short-term. Here, elasticities are included to adjust the demand profile in response to price changes, including cross-price elasticities that account for load shifts among hours. The resulting peak reductions and valley fill alter the optimal unit commitment. Enhancing demand response also increases the amount of wind power that can be economically injected. Further, wind power uncertainty can be managed at a lower cost by adjusting electricity consumption in case of wind forecast errors, which is another way in which demand response facilitates the integration of intermittent renewables. [ABSTRACT FROM AUTHOR]

Highlights: [•] We quantify the benefits of controlled charging of plug-in hybrid electric vehicles. [•] Costs are determined using an economic dispatch and unit commitment model. [•] The model is based on New York ISO and allows for capacity expansion. [•] We find controlled charging can significantly lower system costs. [•] Controlled charging benefits are larger with high wind penetration. [Copyright &y& Elsevier]

In this paper we present a unit commitment model for studying the impact of large-scale wind integration in power systems with transmission constraints and system component failures. The model is formulated as a two-stage stochastic program with uncertain wind production in various locations of the network as well as generator and transmission line failures. We present a scenario selection algorithm for selecting and weighing wind power production scenarios and composite element failures, and we provide a parallel dual decomposition algorithm for solving the resulting mixed-integer program. We validate the proposed scenario selection algorithm by demonstrating that it outperforms alternative reserve commitment approaches in a 225 bus model of California with 130 generators and 375 transmission lines. We use our model to quantify day-ahead generator capacity commitment, operating cost impacts, and renewable energy utilization levels for various degrees of wind power integration. We then demonstrate that failing to account for transmission constraints and contingencies can result in significant errors in assessing the economic impacts of renewable energy integration. [ABSTRACT FROM AUTHOR]

Abstract: Unit commitment (UC) is one of the most important daily tasks that independent system operators or regional transmission organizations must accomplish in the electric power market. In the conventional UC problem, especially under a deregulated power system, the power schedule is usually taken as an energy schedule. However, this simplification may preclude the realization of the feasible energy delivery in real cases owing to the violation of ramping limits, as shown in the literature. If the power system integrates large-scale wind energy, the above “infeasible” energy delivery problem will be worsened, since wind power output will increase the variability of the “net-load” balanced by the thermal units. In this paper, a new UC model is provided that includes the consideration of “feasible” energy delivery under large-scale wind integration. The proposed model can give not only the optimal and feasible energy schedule to thermal units but also a precise ramping process for implementing this schedule. The problem is formulated as a mixed-integer linear programming problem; a 5-unit and a 36-unit system with 25% wind integration are used to test the proposed model. Finally, the numerical results support the conclusions above effectively. [Copyright &y& Elsevier]

This article presents a new algorithm for analyzing the effects of wind generation uncertainties on short-term power system operation. Monte Carlo simulation is used to obtain a set of wind generation scenarios, and then the scenario reduction algorithm is applied to obtain a reduced set of scenarios. These reduced scenarios are then incorporated into the unit-commitment problem formulation with a locational marginal price based electricity market settlement and a dispatch model to examine the effects of wind generation on electricity market prices, load cleared, social welfare, and system capacity. [ABSTRACT FROM AUTHOR]

In this paper, we present a unit commitment problem with uncertain wind power output. The problem is formulated as a chance-constrained two-stage (CCTS) stochastic program. Our model ensures that, with high probability, a large portion of the wind power output at each operating hour will be utilized. The proposed model includes both the two-stage stochastic program and the chance-constrained stochastic program features. These types of problems are challenging and have never been studied together before, even though the algorithms for the two-stage stochastic program and the chance-constrained stochastic program have been recently developed separately. In this paper, a combined sample average approximation (SAA) algorithm is developed to solve the model effectively. The convergence property and the solution validation process of our proposed combined SAA algorithm is discussed and presented in the paper. Finally, computational results indicate that increasing the utilization of wind power output might increase the total power generation cost, and our experiments also verify that the proposed algorithm can solve large-scale power grid optimization problems. [ABSTRACT FROM PUBLISHER]

Wind power generation as a green and cost-effective electric power resource is the best candidate for solution of energy and environmental crisis. Because of inherent intermittency of wind power generation, there are many concerns with the impacts on reliability indices. Hence, the basic aim of this work is the implementation of an intelligent procedure to solve reliability constrained unit commitment (RCUC) problem with wind penetration. Both economical and environmental purposes are considered in objective function formulation besides a constraint on reliability included to common formulation of UC problem. Numerical results show a considerable reduction in operation cost and emission, the risks of high levels of dependency on wind power generation should be also investigated deeply to prevent contingencies and reliability challenges in power system. [ABSTRACT FROM AUTHOR]