Enhancement in reliability-constrained unit commitment considering state-transition-process and uncertain resources

Abstract The high penetration of uncertain resources challenges the security of power system operation. By taking the impact of rescheduling under contingencies into consideration, reliability‐constrained unit commitment (RCUC) is developed to address this challenge. Although several efforts have been made in modelling reliability constraints, the existing methods can only manage oversimplified low‐order temporal‐independent contingencies without considering wide‐range contingencies or their state‐transition‐process issue. To quantify the impact of rescheduling on the normal‐state scheduling process denoted by UC problem, this paper builds up a Bayesian inference method for encoding reliability constraints in wide‐range temporal‐dependent contingencies. Three predictors, for example, expected‐generator‐rescheduling‐power, expected‐energy‐not‐serviced and lost‐of‐load‐probability, are selected to describe the possible corrective behaviours in rescheduling process and quantified by using Bayesian inference method. Then, these predictors are reformatted as a set of linearized constraints to be incorporated into UC. The proposed RCUC comprehensively considers the effect of rescheduling in wide‐range temporal‐dependent contingencies. Therefore, it can reveal the influence of generator rescheduling in wide‐range contingencies and keep better reliability performance than those methods reported in previous RCUC studies. The modified IEEE 30‐bus test system and IEEE 118‐bus test system are used to show the proposed model's effectiveness

Unit Commitment with uncertainties - State of the art

International audienceThe increasing share of variable renewable energy generation (VG), as a response to environmental concerns,brings along new challenges to conciliate economics with security in power system supply. Nowadays, the more developed VGsources are wind and solar, which have low controllability and a variable output that is only partially predictable. This paperpresents an overview on recent developments in Unit Commitment (UC) problems in order to take into account theuncertainty in the demand-generation balance. This subject has been widely discussed in literature over the last years inhundreds of scientific publications, mainly related to the impact of deregulation and the management of forecast errors. Awide variety of approaches to include uncertainties in conventional generation day-ahead optimization, and to representthese uncertainties has been proposed in literature. These include modifications in the objective function, enhanced securityconstraints and solution methods that improve computational speed. In this paper we analyse the development presented inthe literature in order to identify evolution trends in UC models to achieve more robust solutionsL’insertion des énergies renouvelables (EnR) variables pose de nouveaux défis aux gestionnaires de réseaux (GR)pour concilier l’optimisation économique, la sécurité et la qualité de fourniture. Aujourd'hui, l’éolien et le photovoltaïque(PV) sont les EnR avec la plus forte croissance, mais elles s’accompagnent d’une variabilité peu contrôlable et d’uneimprévisibilité partielle de leur production, ce qui impacte, entre autres, la gestion du parc de production. Ce travail présentedifférentes méthodes pour la prise en compte de ces incertitudes dans le placement de la production à cout-terme, définicomme la solution d’un problème d’optimisation, souvent désigné Unit Commitment (UC). Au cours des dix dernières années,les travaux présentés dans la littérature ont eu pour objectif non seulement la représentation des incertitudes sous forme decontraintes supplémentaires dans la fonction objectif, mais également la conception d’une algorithmique avancée pouratteindre des temps acceptables de résolution de cette fonction objectif modifiée y compris pour les grands systèmes. Dans cepapier nous présentons une analyse des évolutions récentes du modèle UC de façon à identifier des nouvelles tendances

Generation dispatch considering wind energy and system reliability

This paper attempts to address the issues of integrating wind generations with bulk power system while maintaining the efficiency and reliability of system operation. The stochastic output of wind generations increases the difficulty of balance total supply and load in a timely manner, and violates the system reliability indices such as EENS and LOLP. An efficient mean is to increase the operating reserve to compensate this additional unpredicted imbalance. To solve this problem, ideally the wind power, reserve and reliability cost should be concerned and optimized simultaneously. However the current dispatch and planning models with wind energy are mostly stochastic and solved by Monte Carlo simulation or heuristic methods. Those models and methods may not satisfy the requirements for mid-term and short term system operations, and further on-line applications. In this paper we propose an analytical EENS and LOLP indices contributed by wind power uncertainties with application of Q-function approximation. These reliability indices are considered in the co-optimization model of energy market and reserve market. In the model, conventional units and wind units are dispatched with optimal reserve and reliability costs. The wind power incurred system operating costs are proposed and formulated by the sensitivities in the optimization model. Finally the numerical example based on IEEE-39 system shows validity and effectiveness of the proposed model. ©2010 IEEE.published_or_final_versionThe IEEE Power and Energy Society (PES) General Meeting, Minneapolis, MN., 25-29 July 2010. In Proceedings of PES, 2010, p. 1-

Reliability Constrained Unit Commitment Considering the Effect of DG and DR Program

Due to increase in energy prices at peak periods and increase in fuel cost, involving Distributed Generation (DG) and consumption management by Demand Response (DR) will be unavoidable options for optimal system operations. Also, with high penetration of DGs and DR programs into power system operation, the reliability criterion is taken into account as one of the most important concerns of system operators in management of power system. In this paper, a Reliability Constrained Unit Commitment (RCUC) at presence of time-based DR program and DGs integrated with conventional units is proposed and executed to reach a reliable and economic operation. Designated cost function has been minimized considering reliability constraint in prevailing UC formulation. The UC scheduling is accomplished in short-term so that the reliability is maintained in acceptable level. Because of complex nature of RCUC problem and full AC load flow constraints, the hybrid algorithm included Simulated Annealing (SA) and Binary Particle Swarm Optimization (BPSO) has been proposed to optimize the problem. Numerical results demonstrate the effectiveness of the proposed method and considerable efficacy of the time-based DR program in reducing operational costs by implementing it on IEEE-RTS79

Improving the deterministic reserve requirements method to mitigate wind uncertainty

Les réseaux électriques sont sujets aux aléas divers pouvant éventuellement mettre en péril leur sûreté. Des évènements résultants de l’aléa météorologique ou de la défaillance stochastique des composants du réseau tels qu’une fluctuation de températures hors saison ou la perte d’une unité de production, peuvent causer des déséquilibres inattendus entre l’offre et la demande et entraîner des délestages. Pour faire face à ces aléas, des marges de puissance ou "réserve" sont ménagées par rapport au strict équilibrage de l’offre et de la demande prévisionnelle. Cependant, déterminer la quantité de réserve suffisante pour une opération fiable et rentable est un problème difficile à résoudre, particulièrement en présence d’incertitude croissante due à la libéralisation du marché de l’électricité et à la pénétration à grande échelle des éoliennes sur le réseau. L’approche déterministe considère un niveau de réserve statique du jour pour le lendemain. L’énergie éolienne étant faiblement prévisible, de la réserve supplémentaire est requise pour pallier l’intermittence du vent. Parce que les éoliennes ne sont pas distribuables, les générateurs conventionnels ont été laissés sous pression en répondant aux variations larges et rapides de la charge nette du réseau. Étant données les contraintes de rampe qui limitent leur flexibilité, le bon fonctionnement du marché de l’électricité peut être altéré parce que les transactions d’énergie qui y sont contractées risquent de ne pas être réalisées en temps réel comme convenu pour des raisons de sécurité. Dans ce contexte, l’utilisation de l’approche déterministe à elle seule comme c’est le cas aujourd’hui, pourrait ne pas être économique ou fiable pour contenir les risques encourus; d’où la nécessité des méthodes sophistiquées basées sur une représentation plus complexe de l’incertitude. Cette thèse propose des solutions viables et efficientes à l’incertitude croissante dans l’opération à court terme des réseaux électriques en présence d’éoliennes à grande échelle et dans un contexte de compétition. Le caractère conservatif de la méthode déterministe a été grandement amélioré par une génération de réserve supplémentaire, contrôlable, et qui tient en compte l’aspect stochastique des éoliennes. La mutualisation des capacités via l’interconnexion permet d’alléger la contrainte d’équilibrage du réseau et de réduire les secousses autour des générateurs conventionnels. Afin de faciliter les transactions d’énergie sur le marché, des règles ont été élaborées pour inciter la mise en disponibilité des générateurs à larges paliers de rampes. Un problème combiné de la programmation des centrales et de transit optimal de puissance incorporant tous les objectifs sus-cités a été formulé. Traduit en programmation mixte quadratique car générant des solutions faisables dont le niveau d’optimalité est connu, celui-ci a été utilisé pour investiguer divers effets de l’interconnexion sur la réduction des coûts d’exploitations associés à plus d’éoliennes sur le réseau. Enfin et surtout, la capacité de notre modèle à résister aux contingences a été validée avec un modèle qui tient compte de l’aspect aléatoire des composants du réseau à tomber en panne. Ce qui nous a permis d’ajuster notre stratégie du marché du jour pour le lendemain par rapport à celui du temps réel. Notre modèle se distingue par sa rapidité et sa capacité `a révéler les coûts cachés de l’intégration des éoliennes dans les réseaux électriques.Power grids are subject to a variety of uncertainties that may expose them to potential safety issues. Interruptions in electricity supply for instance, may result from an unseasonable temperature fluctuations or a power station outage, which are events of stochastic nature involving the weather or the failure of a component in the grid. The result may be sudden imbalances in supply and demand, leading to load interruptions. To plan for such unforeseen events, the grid carries ’reserve’, i.e., additional capacity above that needed to meet actual demand. However, scheduling the appropriate amount of reserve needed for a reliable and cost-effective grid operation is very challenging, especially in the context of increased uncertainties due to liberalization and the large-scale wind electric generators (WEGs) penetration to grid. Traditional grids assume a fixed knowledge of system conditions for the next day. Wind power being very poor to predict, an extra reserve generation to accommodate its uncertainty is required. Because WEGs aren’t built around spinning turbines, conventional units have been left stressed while responding to large and fast variations in the system net load. Given the temporal operating restrictions that limit their flexibility, the properly functioning of the electricity market can be altered as the energy transactions may not be carried out in realtime, exactly as agreed for security reasons. In this context, the use of the deterministic criteria alone as is the case today, may not be economical or reliable in limiting the risk of uncertainty; calling for sophisticated methods based on more-complex characteristics of uncertainty. This thesis proposes reliable and sound solutions to the increased variability and uncertainty in short-term power grid operations emanating from increasing the share of WEGs in the generation mix and competition from electricity markets. The conservativeness of the deter ministic method has been greatly improved with an adjustable extra generation reserve that accounts for the stochastic feature of WEGs. An inherent flexibility–design that attempts to reduce the onus placed on conventional units to balance the system has been considered. In doing so, the jerkings around these units while responding to large and fast variations in the system net load have been considerably mitigated. Adequate market policies that incentivize flexible resources to make their units with higher ramp rates available to follow dispatch signals have been crafted, thereby avoiding potential reliability degradation or costly out-ofmarket actions. A combined Security Constrained Unit Commitment (SCUC) and Optimal Power Flow (OPF) optimization problem that encompasses all the above mentioned goals has been formulated. Translated into a Mixed Integer Quadratic Programming (MIQP) problem that can return a feasible solution with a known optimality level, the SCUC-OPF engine has been used to investigate various effects of grids integration on reducing the overall operating costs associated with more wind power in the system. Last but not least, the effectiveness of our model to withstand contingencies has been done with a probabilistic model benchmark that accounts for the random nature of grid failure. This allows the adjustment of the Day- Ahead Market (DAM) strategy with respect to the Real-Time Market (RTM). Our model is proven to be more acceptable as it is time-saving, and has particular implications for wind integration studies as it can reverse the hidden cost of integrating WEGs to grid

MULTI-OBJECTIVE POWER SYSTEM SCHEDULING USING EVOLUTIONARY ALGORITHMS

Ph.DDOCTOR OF PHILOSOPH

Unit commitment under Uncertainty

Ph.DDOCTOR OF PHILOSOPH

Co-Optimization of Gas-Electricity Integrated Energy Systems Under Uncertainties

In the United States, natural gas-fired generators have gained increasing popularity in recent years due to low fuel cost and emission, as well as the needed large gas reserves. Consequently, it is worthwhile to consider the high interdependency between the gas and electricity networks. In this dissertation, several co-optimization models for the optimal operation and planning of gas-electricity integrated energy systems (IES) are proposed and investigated considering uncertainties from wind power and load demands. For the coordinated operation of gas-electricity IES: 1) an interval optimization based coordinated operating strategy for the gas-electricity IES is proposed to improve the overall system energy efficiency and optimize the energy flow. The gas and electricity infrastructures are modeled in detail and their operation constraints are fully considered. Then, a demand response program is incorporated into the optimization model, and its effects on the IES operation are investigated. Interval optimization is applied to address wind power uncertainty in IES. 2) a stochastic optimal operating strategy for gas-electricity IES is proposed considering N-1 contingencies in both gas and electricity networks. Since gas pipeline contingencies limit the fuel deliverability to gas-fired units, N-1 contingencies in both gas and electricity networks are considered to ensure that the system operation is able to sustain any possible power transmission or gas pipeline failure. Moreover, wind power uncertainty is addressed by stochastic programming. 3) a robust scheduling model is proposed for gas-electricity IES with uncertain wind power considering both gas and electricity N-1 contingencies. The proposed method is robust against wind power uncertainty to ensure that the system can sustain possible N-1 contingency event of gas pipeline or power transmission. Case studies demonstrate the effectiveness of the proposed models. For the co-optimization planning of gas-electricity IES: a two-stage robust optimization model is proposed for expansion co-planning of gas-electricity IES. The proposed model is solved by the column and constraint generation (C&CG) algorithm. The locations and capacities of new gas-fired generators, power transmission lines, and gas pipelines are optimally determined, which is robust against the uncertainties from electric and gas load growth as well as wind power