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Optimisation and Analysis Toolbox for power Systems analysis (OATS) is an open-source simulation tool for steady-state analyses of power systems problems distributed under the GNU General Public License (GPLv3). It contains implementations of classical steady-state problems, e.g. load flow, optimal power flow (OPF) and unit commitment, as well as enhancements to these classical models relative to the features available in widely used open-source tools. Enhancements implemented in the current release of OATS include: a model of voltage regulating on-load tap-changing transformers; load shedding in OPF; allowing a user to build a contingency list in the security constrained OPF analysis; implementation of a distributed slack bus; and the ability to model zonal transfer limits in unit commitment. The mathematical optimisation models are written in an open-source algebraic modelling language, which offers high-level symbolic syntax for describing optimisation problems. The flexibility offered by OATS makes it an ideal tool for teaching and academic research. This paper presents novel aspects of OATS and discusses, through demonstrative examples, how OATS can be extended to new problem classes in the area of steady-state power systems analysis. [ABSTRACT FROM AUTHOR]

A novel machine learning based mixed integer programming model is developed for the optimal nonparametric prediction intervals (PIs) of electricity load, which minimizes interval width subject to target hit probability constraint. Binary variables are employed to analytically formulate PI coverage states and further impose calibration constraint. Considering the quantile interpretation on lower and upper bounds of PIs, an innovative binary variable reduction strategy is proposed to significantly accelerate model training process. Compared with traditional central PIs, the resultant optimal nonparametric PIs are featured with adaptive symmetric and asymmetric quantile proportion pairs for interval shortening. Numerical experiments based on actual substation data validate the remarkable superiority of the presented method with respect to both PI quality and computational efficiency. [ABSTRACT FROM AUTHOR]

Near-optimal unit commitment (UC) scheduling is a practical reality in wholesale electricity markets. This paper revisits previous work that has found that minor differences in cost among near-optimal schedules can result in a large redistribution of market payments (i.e., changes in generator profits and consumer surplus). It has been believed that this instability is unavoidable, but previous studies have only calculated prices using what we call the Restricted pricing model. This paper compares previous results to three additional models that are based on integer relaxation, and which we call the Partial, Tight, and Loose Dispatchable pricing models. Results are presented for a suite of test cases, including four ISO-scale cases. Similar to previous findings, the Restricted and Partial Dispatchable models both result in large payment redistributions among alternative solutions. In contrast, theoretical and experimental results for the Tight and Loose Dispatchable models show that pricing models with unconditional integer relaxation will have bounded payment redistributions, and, further, this bound can become quite small by tightening the UC problem's convex relaxation. In the presence of market power, stable financial outcomes may improve market efficiency by reducing incentives to bid strategically. [ABSTRACT FROM AUTHOR]

Efficient solution methods for large-scale unit commitment (UC) problems have long been an important research topic and a challenge, especially in market clearing computation. For large-scale UC, the Lagrangian relaxation methods (LR) and the mixed integer programming methods (MIP) are most widely adopted. However, LR usually suffers from slow convergence; and the computational burden of MIP is heavy when the binary variable number is large. In this paper, a variable reduction method is proposed. First, the time-coupled constraints in the original UC problem are relaxed, and many single-period UC problems (s-UC) are obtained. Second, LR is used to solve the s-UCs. Different from traditional LR with iterative subgradient method, the optimal multipliers and the approximate UC solutions of the s-UCs are obtained by solving linear programs. Third, a criterion for choosing and fixing the UC variables in the UC problem is established; hence, the number of binary variables is reduced. Finally, the UC with reduced binary variables is solved to obtain the final UC solution. The proposed method is tested on the IEEE 118-bus system and a 6484-bus system. The results show the method is very efficient and effective. [ABSTRACT FROM AUTHOR]

Intentional controlled islanding (ICI) and black start allocation (BSA) are two examples of problems in the power systems literature that have been formulated as mixed integer programs (MIPs) and allow reconfiguration of the power system topology. A key consideration in both of these problems is that each island that appears after a reconfiguration must have at least one energized generator. In this paper, we examine three alternative MIP formulations for this restriction, show their equivalence, and prove that two of them are stronger in terms of their linear programming relaxation than the formulation most commonly used in the power systems literature. Since the time to solve MIPs can vary significantly between equivalent formulations, we also present computational experiments on the IEEE test systems for the ICI and BSA problems. We observe that a polynomially separable, exponential in size, strong formulation yields the best performance for the BSA problem and exhibits a comparable performance to a linear in size, weak formulation for the ICI problem. [ABSTRACT FROM AUTHOR]

Equipment failures, operator errors, natural disasters and cyber-attacks can and have caused extended blackouts of the electric grid. Even though such events are rare, preparedness for them is critical because extended power outages endanger human lives, compromise national security, or result in economic losses of billions of dollars. Since most of the generating units cannot restart without connecting to an energized grid, the system operator relies on a few units with the ability to start autonomously, called Black Start (BS) units, to restore the power system. Allocating and maintaining these units is costly and can severely impact the restoration security and time. We formulate an optimization problem to optimally allocate BS units in the grid, while simultaneously optimizing over the restoration sequence. We extend existing optimal allocation models by including grid considerations such as active power nodal balance, transmission switching, nodal reactive power support and voltage limits. In order to aid the branch and bound tree that solves the resulting large scale mixed integer program, we propose a randomized heuristic that is executed multiple times in parallel on a high-performance computing environment to find feasible solutions. We proceed to solve the IEEE-39, the IEEE-118, and a simplified WECC system with 225 nodes and 136 generators to near optimality. [ABSTRACT FROM AUTHOR]

Clustered unit commitment (CUC) formulations have been proposed to provide accurate and fast approximations to the unit commitment (UC) problem. In these formulations, identical or similar plants are grouped into clusters. This way, the binary commitment variables of all the plants within a cluster can be replaced by a single integer variable. This approach has recently been mainly used for tractably integrating flexibility constraints in generation expansion planning problems. However, a thorough general validation is still missing. In addition, these formulations do not provide commitment schedules on a plant-by-plant level and hence cannot be used directly for operating actual systems or markets. A first contribution of this paper is to show that errors can be introduced both due to the problem formulation and the grouping of nonidentical units. A case study is presented in which these errors are quantified under different conditions. Overall, the error in approximating the total cost does not exceed 0.06%. A second contribution of this paper is the development of a hybrid approach, which sequentially uses a CUC and a traditional UC model. This approach allows to reduce the computational cost of solving the UC problem while providing a guaranteed feasible and near-optimal solution. [ABSTRACT FROM AUTHOR]

This paper presents a bilevel optimization model for the risk assessment of transmission systems. Specifically, the lower level model is expected to provide a generation redispatch by minimizing the total load shedding, and the upper-level model is to maximize the severity risk by constructing a binary optimization model to identify the worst N-k contingency. To further reduce the complexity of the proposed model, the logarithmic transformation and linearization techniques are utilized, leading to a general mixed integer linear programming. In addition, a recursive method is proposed for contingency ranking based on the bilevel optimization model. Compared with the benchmark analytic method, the proposed method does not need to enumerate every contingency, reducing the computational burden. In contrast to the traditional Monte Carlo Simulation method, the proposed method can give as precise a risk assessment result as the analytic method and has higher evaluation efficiency. Numerical results on three test systems verify the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined future uncertainties (i.e., fixed random outcomes with given probabilities). In several recent studies of generation assets’ planning (e.g., thermal versus renewable), new findings show that the investment decisions could affect the future uncertainties as well. To this end, this paper proposes a multistage decision-dependent stochastic optimization model for long-term large-scale generation expansion planning, where large amounts of wind power are involved. In the decision-dependent model, the future uncertainties are not only affecting but also affected by the current decisions. In particular, the probability distribution function is determined by not only input parameters but also decision variables. To deal with the nonlinear constraints in our model, a quasi-exact solution approach is then introduced to reformulate the multistage stochastic investment model to a mixed-integer linear programming model. The wind penetration, investment decisions, and the optimality of the decision-dependent model are evaluated in a series of multistage case studies. The results show that the proposed decision-dependent model provides effective optimization solutions for long-term generation expansion planning. [ABSTRACT FROM AUTHOR]

As part of the day-ahead market clearing process, MISO solves one of the most challenging Security Constrained Unit Commitment (SCUC) models. With footprint expansions and increased activity from financial traders, the day-ahead market unit commitment encounters significant performance challenges from time to time. This paper reviews MISO's day-ahead market clearing process and identifies the factors contributing to these performance challenges. It then shares techniques that have been developed to overcome these challenges. Three alternative backup solving approaches were developed. An incremental solving algorithm is proposed to better handle the iterative market clearing process and to perform solution polishing and commitment reason identification. Numerical results based on MISO data are presented. [ABSTRACT FROM PUBLISHER]

Generation maintenance scheduling (GMS) plays an important role in power system operations. The restructuring of the power industry has forced changes to the traditional maintenance mechanism. On one hand, the generation companies seek to maximize their profit. On the other hand, the independent system operator (ISO) strives to maintain the operational reliability of the system while maximizing the social welfare. This paper proposes a coordination mechanism for generation maintenance scheduling in electricity markets. In order to solve the resulting large mixed integer programming (MIP) problem, a relaxation induced algorithm is utilized. This technique is based on the solution of the linear relaxed problem. The features of the coordination mechanism and the performance of the algorithm are demonstrated using the IEEE-118 bus system and a provincial power system in China. Case studies show that the proposed mechanism not only ensures the maintenance preference of the generating companies, but also maintains the operational reliability of the system. They also demonstrate that the algorithm is quite efficient at solving the optimization problem. [ABSTRACT FROM PUBLISHER]

Electricity markets are based on several sequential energy and reserve trading mechanisms to constantly maintain the balance between generation and demand. During the last years, reserve markets are getting much importance all around the world with the increasing social awareness of the renewable energy benefits. Additional reserve quantities and larger remunerations are being implemented by the regulators since this renewable energy is highly dependent on weather conditions uncertainties. Utilities are therefore demanding more and more powerful models to better optimize their reserve bidding curves to be sent to the system operators. This paper describes a new methodology to obtain an optimized real bidding curve for the secondary (spinning) reserve market. The approach is based on the maximization of a company profit, assuming a set of residual demand curves scenarios for its competitors' representation, and considering the day-ahead market opportunity cost to build the reserve cost curve. The case study validates the main features of the proposed methodology by its application to the Spanish secondary reserve market. This methodology is being daily used by one of the most important electricity companies in Spain. [ABSTRACT FROM PUBLISHER]

This paper presents an approach for integrated generation and transmission maintenance scheduling model (IMS) that takes into consideration N-1 contingencies. The objective is to maximize the maintenance preference of facility owners while satisfying N-1 security and other constraints. To achieve this goal, Benders decomposition is employed to decompose the problem into a master problem and several sub-problems. A Relaxation Induced (RI) algorithm is proposed to efficiently solve the large mixed integer programming (MIP) master problem. This algorithm is based on the solution of the linear relaxed problem. It is demonstrated that the proposed algorithm can efficiently reach a near-optimal solution that is usually satisfactory. If this near-optimal solution is not acceptable, it is used as the initial solution to fast start the solution of the original IMS problem. The performance of the proposed method is demonstrated using a modified version of the IEEE 30-bus system and a model of the power system of a Chinese province. Case studies show that the proposed algorithm can improve the computational efficiency by more than an order of magnitude. [ABSTRACT FROM AUTHOR]

Optimization models have been widely used in the power industry to aid the decision-making process of scheduling and dispatching electric power generation resources, a process known as unit commitment (UC). Since UC’s birth, there have been two major waves of revolution on UC research and real life practice. The first wave has made mixed integer programming stand out from the early solution and modeling approaches for deterministic UC, such as priority list, dynamic programming, and Lagrangian relaxation. With the high penetration of renewable energy, increasing deregulation of the electricity industry, and growing demands on system reliability, the next wave is focused on transitioning from traditional deterministic approaches to stochastic optimization for unit commitment. Since the literature has grown rapidly in the past several years, this paper is to review the works that have contributed to the modeling and computational aspects of stochastic optimization (SO) based UC. Relevant lines of future research are also discussed to help transform research advances into real-world applications. [ABSTRACT FROM PUBLISHER]

Economic transmission switching has been proposed as a new control paradigm to improve the economics of electric power systems. In practice, the transmission switching operation itself is a disruptive action to the system. Frequently switching lines into or out of service can create undesirable effects on the security and reliability of power systems and may require new investments in the automation and control systems. In this paper, we formulate an economic seasonal transmission switching model where transmission switching occurs once at the beginning of a time period (season) and then the transmission topology remains unchanged during that period. The proposed seasonal transmission switching model is a large-scale mixed integer programming problem. The objective of the optimization model is to minimize the total energy generation cost over the season subject to loads and N-1 reliability requirements. We develop a novel decomposition method that decomposes the seasonal problem into one-hour problems which are then solved efficiently. We demonstrate our model and the decomposition approach on the 14-bus, 39-bus, and 118-bus power systems and show potential cost savings in each case. [ABSTRACT FROM PUBLISHER]

Mixed integer linear programming (MILP) based formulations and solution methods for short-term hydro generation scheduling (HGS) have been widely adopted by researchers, hydropower producers, and system operators in recent years. This approach calls for the nonlinear forebay level, tailrace level, penstock loss, and hydropower production functions to be replaced with their piecewise linear approximations. However, the effects of the linearization of the nonlinear functions and related constraints on solution feasibility have not been fully discussed in the literature. In this paper, the issues concerning solution feasibility are discussed in detail and a method is presented to ensure that the solution obtained based on the approximated MILP formulation remains feasible for the original nonlinear formulation. Furthermore, it is found that the real number water delay can be handled in the formulation without destroying the linear structure of the water balance constraints. Numerical testing results show that the method presented in this paper is effective. [ABSTRACT FROM PUBLISHER]

Corrective transmission switching schemes are an essential part of grid operations and are used to improve the reliability of the grid as well as the operational efficiency. Today, the transmission switching schemes are established based on the operator's past knowledge of the system as well as other ad-hoc methods. In this paper, three topology control (corrective transmission switching) methodologies are presented along with the detailed formulation of robust corrective switching. By incorporating robust optimization into the corrective switching framework, the switching solution is guaranteed to be feasible for a range of system operating states. The robust model can be solved offline to suggest switching actions that can be used in a dynamic security assessment tool in real-time. The proposed robust topology control algorithm can also generate multiple corrective switching actions for a particular contingency. The robust topology control formulation is tested on an IEEE 118-bus test case with different uncertainty sets. [ABSTRACT FROM PUBLISHER]

Obtaining high-quality feasible solution is the core and the major difficulty in solving security-constrained unit commitment (SCUC) problems. This paper presents a systematic method for constructing feasible solutions to SCUC problem based on a group of analytical feasibility conditions. The feasibility check is performed based on the analytical necessary conditions such that most of infeasible UC states can be identified without solving LP problem. If a UC state is infeasible, it is adjusted with the possibly minimal operating cost increase based on the cost information. This UC adjusting issue is formulated as a zero-one programming problem and a branch and bound (B&B) method is established based on these feasibility conditions. Numerical testing is performed for a 31-bus system, an IEEE 24-bus system, and an IEEE 118-bus system. The testing results suggest that over 95% of infeasible UC states are identified by the analytical necessary conditions. The near-optimal feasible schedules for SCUC problem can be obtained efficiently by the proposed method. The feasible schedules obtained are compared with those obtained from mixed integer programming-based method in the IEEE 118-bus system. It is shown that the new method can produce competitive results in terms of solution quality and computational efficiency. [ABSTRACT FROM PUBLISHER]

Security constrained unit commitment (SCUC) is one of the most important daily tasks that independent system operators (ISOs) or regional transmission organizations (RTOs) must accomplish in daily electric power market. Security constraints have long been regarded as difficult constraints for unit commitment problems. If the inactive security constraints can be identified and eliminated, the SCUC problem can be greatly simplified. In this paper, a necessary and sufficient condition for a security constraint to be inactive is established. It is proved that all inactive constraints can be identified by solving a series of small-scale mixed integer linear programming (MILP) problems. More importantly, an analytical sufficient condition is established and most of the inactive constraints can be quickly identified without solving MILP or linear programming (LP) problems. A very important feature of the conditions obtained is that they are only related to the load demands and parameters of the transmission network. Numerical testing is performed for three power grids and the results are impressive. Over 85% of the security constraints are identified as inactive and the crucial transmission lines affecting the total operating cost are among those associated with the remaining security constraints, providing useful information for transmission planning. [ABSTRACT FROM PUBLISHER]

The proposed model solves the coordinated generation and transmission maintenance scheduling with security-constrained unit commitment (SCUC) over the scheduling horizon of weeks to months. The model applies the Lagrangian relaxation technique to decompose the optimization problem into subproblems for generation maintenance scheduling, transmission maintenance scheduling, and short-term SCUC. The decomposition and cooperation strategy is applied to the first two subproblems for the scheduling of generation and transmission maintenance. The SCUC solution is based on the mixed integer programming (MIP) technique. The optimal hourly results for maintenance scheduling, generation unit commitment, and transmission flows are obtained using a chronological load curve. Effective strategies are applied for accelerating the convergence of the hourly solution. The numerical examples demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic coordination of generation and transmission expansion planning model in a competitive electricity market. The Monte Carlo simulation method is applied to consider random outages of generating units and transmission lines as well as inaccuracies in the long-term load forecasting. The scenario reduction technique is introduced for reducing the computational burden of a large number of planning scenarios. The proposed model assumes a capacity payment mechanism and a joint energy and transmission market for investors' costs recovery. The proposed approach simulates the decision making behavior of individual market participants and the ISO. It is an iterative process for simulating the interactions among GENCOs, TRANSCOs and ISO. The iterative process might be terminated by the ISO based on a pre-specified stopping criterion. The case studies illustrate the applications of proposed stochastic method in a coordinated generation and transmission planning problem when considering uncertainties. [ABSTRACT FROM AUTHOR]

This paper presents a model for the simulation of the optimal medium-term operation of a hydrothermal system. System stochastic parameters are modeled by Monte Carlo scenarios, which are solved on distributed processors. For each scenario a yearly hydrothermal scheduling (HTS) problem with hourly time resolution is formulated and solved as a large mixed integer linear program (MILP). HTS modeling includes unit commitment, start-up costs and minimum up/down time constraints. The model is applied to the Greek Power System, comprising 29 thermal units and 13 hydroplants; 100 simulation scenarios are generated and solved on 18 distributed processors. Test results include both medium-term objectives, such as reservoir water management, and short-term decisions such thermal unit start-up decisions. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization-based method of planning reactive power control for electric transmission systems to endow them with the capability of being reconfigured to a secure configuration under a list of contingencies. The overall objective function is to minimize the installation cost of new controls such as mechanically switched capacitors, while satisfying the requirements of voltage stability margin and voltage magnitude under a contingency list. The backward/forward search algorithm with linear complexity is used to select candidate locations for switched capacitors. Optimal locations and amounts of new switch controls are obtained by solving a sequence of mixed integer programming problems. The modified New England 39-bus system and a North American power system with 6358 buses are adopted to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Spinning reserve (SR) allows system operators to compensate for unpredictable imbalances between load and generation caused by sudden outages of generating units, errors in load forecasting or unexpected deviations by generating units from their production schedules. As the proportion of power produced by wind farms increases, it becomes more difficult to predict accurately the total amount of power injected by all generators into the power system. This added uncertainty must be taken into account when setting the requirement for SR. This paper proposes a technique to calculate the optimal amount of SR that the system operator should provide to be able to respond not only to generation outages but also to errors in the forecasts for load and wind power production. Using a Monte Carlo simulation, the proposed technique for setting the SR requirements is then compared with the traditional deterministic criterion (i.e., the capacity of the largest online infeed), an approach to cope with wind imbalances and an approach that combines the traditional criterion with the approach to cope with wind imbalances. The results show that, contrary to what is commonly believed, an increased wind power penetration does not necessarily require larger amounts of SR. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic midterm risk-con- strained hydrothermal scheduling algorithm in a generation company (GENCO). The objective of a GENCO is to maximize payoffs and minimize financial risks when scheduling its midterm generation of thermal, cascaded hydro, and pumped-storage units. The proposed schedule will be used by the GENCO for bidding purposes to the ISO. The optimization model is based on stochastic price-based unit commitment. The proposed GENCO solution may be used to schedule midterm fuel and natural water inflow resources for a few months to a year. The proposed stochastic mixed-integer programming solution considers random market prices for energy and ancillary services, as well as the availability of natural water inflows and generators in Monte Carlo scenarios. Financial risks associated with uncertainties are considered by applying expected downside risks which are incorporated explicitly as constraints. Variable time-steps are adopted to avoid the exponential growth in solution time and memory requirements when considering midterm constraints. A single water-to-power conversion function is used instead of several curves for representing water head and discharge parameters. Piecewise linearized head-dependant water-to-power conversion functions are used for computational efficiency. Illustrative examples examine GENCOs' midterm generation schedules, risk levels, fuel and water usage, and hourly generation dispatches for bidding in energy and ancillary services markets. The paper shows that GENCOs could decrease their financial risks by adjusting expected payoffs. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated model for assessing the impact of interdependency of electricity and natural gas networks on power system security. The integrated model incorporates the natural gas network constraints into the optimal solution of security-constrained unit commitment. The natural gas network is modeled by daily and hourly limits on pipelines, sub-areas, plants, and generating units. The application of fuel diversity (e.g., generating units with fuel switching capability) is presented as an effective peak shaving strategy for natural gas demand which could hedge price volatilities of natural gas and electric power. The pro- posed model can be used by a vertically integrated utility for the commitment and dispatch of generating units and the allocation of natural gas for the next day utilization. The proposed model can also be used for measuring the security of social services by modeling the interdependency of natural gas and electric power system infrastructures. If the proposed model is used by GENCOs, gas constraints will be submitted to electricity markets as energy constraints. Illustrative examples show the impact of natural gas supply infrastructure on the economic operation of a vertically integrated utility. The examples also discuss the impact of generating units with fuel switching capability on the power system security when the supply of natural gas is limited. [ABSTRACT FROM AUTHOR]

In this paper, we continue to analyze optimal dispatch of generation and transmission topology to meet load as a mixed integer program (MIP) with binary variables representing the state of the transmission element (line or transformer). Previous research showed a 25% savings by dispatching the IEEE 118-bus test case. This paper is an extension of that work. It presents how changing the topology affects nodal prices, load payment, generation revenues, cost, and rents, congestion rents, and flowgate prices. Results indicate that changing the topology to cut costs typically results in lower load payments and higher generation rents for this network. Computational issues are also discussed. [ABSTRACT FROM AUTHOR]

We propose an ISO model for coordinating transmission expansion planning with competitive generation capacity planning in electricity markets. The purpose of the model is a holistic simulation of generation and transmission capacity expansion in the market environment. The solution represents an iterative process for simulating the interactions among GENCOs, TRANSCOs, and the ISO, which might be terminated by the ISO based on a pre-specified stopping criterion. The proposed model adopts a joint energy and transmission auction market and a capacity mechanism. The joint auction market enables competition to occur among generation and transmission resources. The capacity mechanism offers incentives for market participant investments that reflect the locational values of additional capacity. Transmission capacity expansion decisions are made by merchant transmission lines that recover their capacity investments through transmission marginal pricing and capacity payments. Transmission network security is reflected in the proposed competitive resource planning model. The examples illustrate a coordinated planning of generation and transmission in restructured power systems. [ABSTRACT FROM AUTHOR]

The proposed approach to the optimal coordination of midterm planning can be used by vertically integrated utilities as well as the ISOs in electricity markets. The approach coordinates the optimal maintenance scheduling of generating units and transmission lines, within the outage scheduling horizon of weeks to months, with the security-constrained unit commitment of available components. The proposed model provides hourly schedules for maintenance outages, generation unit commitment, and transmission flows based on hourly load curves. In addition, the approach calculates the optimal allocation of fuel and emission allowance for thermal units in a vertically integrated utility, and energy allocation in electricity markets. This paper discusses that a seamless coordination of midterm and short-term optimization schemes will effectively improve the security of electricity services and reduce the chance of blackouts in aging power systems. The proposed approach highlights a trade-off between calculation speed and accuracy for large-scale implementations. A six-bus system is used for detailed analyses, and further results presented for the IEEE-118 system exhibit the effectiveness of the proposed midterm planning approach. [ABSTRACT FROM AUTHOR]

We propose a security-based competitive generation resource planning model in electricity markets. The objective of the model is to introduce the impact of transmission security in a multi-GENCO generation resource planning. The proposed approach is based on effective decomposition and coordination strategies. Lagrangian relaxation and Benders decomposition are applied to decompose the original planning problem into tractable optimization subproblems. Locational price signal and security signal are defined for the simulation of competition among GENCOs and the coordination of security between GENCOs and the regulatory body (ISO). The numerical examples exhibit the effectiveness of the proposed generation planning model in electricity markets. [ABSTRACT FROM AUTHOR]

This paper develops optimal bidding strategies based on hourly unit commitment in a generation company (GENCO) that participates in energy and ancillary services markets. The price-based unit commitment problem with uncertain market prices is modeled as a stochastic mixed integer linear program. The market price uncertainty is modeled using the scenario approach, Monte Carlo simulation is applied to generate scenarios, scenario reduction techniques are applied to reduce the size of the stochastic price-based unit commitment problem, and postprocessing is applied based on marginal cost of committed units to refine bidding curves. The financial risk associated with market price uncertainty is modeled using expected downside risk, which is incorporated explicitly as a constraint in the problem. Accordingly, the proposed method provides a closed-loop solution to devising specific strategies for risk-based bidding in a GENCO. Illustrative examples show the impact of market price uncertainty on GENCO's hourly commitment schedule and discuss the way GENCOs could decrease financial risks by managing expected payoffs. [ABSTRACT FROM AUTHOR]

The solution of a long-term security-constrained unit commitment (SCUC) problem, which often spans several months to a year, may consider multiple long-term fuel and emission constraints in addition to operating constraints embedded in short-term SCUC. The size and the complexity of long-term SCUC are often beyond reasonable computing time and resources. Hence, Lagrangian relaxation is applied in this paper to manage coupling constraints over the entire period. Based on dual relaxation, the large-scale optimization problem is decomposed into many tractable short-term SCUC subproblems without long-term fuel and emission constraints. The resource penalty prices are linking signals for the coordination of subproblems. The short-term SCUC may be solved by any numerical optimization methods, including mixed integer programming and Lagrangian relaxation. A hybrid subgradient and Dantzig-Wolfe decomposition approach is presented for managing Lagrangian multipliers in the large-scale dual optimization of long-term SCUC problem. The proposed hybrid approach is a tradeoff between calculation speed and accuracy of the long-term SCUC solution. A modified IEEE 118-bus system is analyzed to exhibit the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]