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Highlights • A multi-stage robust optimization-based formulation is proposed to model the TCUC problem with uncertainty. • Non-anticipativity of the ED decisions and multi-stage robustness of the UC decisions are guaranteed. • A decomposition framework with implicit decision rules is proposed and the formulation scale is small. • Subproblems in the decomposition framework have a time-decoupled structure and are easy to solve. • A numerical example is given to clarify the difference between two-stage robustness and multi-stage robustness. Abstract With the integration of large-scale renewable energy sources to power systems, many optimization methods have been applied to solve the stochastic/uncertain transmission-constrained unit commitment problem. Among all methods, two-stage and multi-stage robust optimization-based methods are the most widely adopted ones. In the two-stage methods, non-anticipativity of economic dispatch (ED) decisions are not considered. While in multi-stage methods, explicit decision rules (like affine decision rules) are adopted to guarantee non-anticipativity of ED decisions, the computational burden can be heavy and the optimality of the solution is affected. In this paper, a multi-stage robust TCUC formulation with implicit decision rules is proposed, as well as a decomposition framework to solve it. The solutions are proved to be multi-stage robust and non-anticipativity of ED decisions is guaranteed. Meanwhile, a computationally efficient time-decoupled solution method for the feasibility check subproblems is also proposed, such that the method is suitable for large-scale TCUC problems with uncertain power injections. Numerical tests are conducted on IEEE 118-bus system and Polish 2383-bus system. The proposed method outperforms the multi-stage robust method with explicit decision rules in solution's economic performances, and is more computationally efficient than the all-scenario-feasible method. The effectiveness of time-decoupled solution method is also verified. [ABSTRACT FROM AUTHOR]

Highlights • A hierarchical and fully distributed method based on ADMM is proposed. • Coupling constraints are decoupled by solving dual problem. • The proposed method can be implemented in master-slave distributed and parallel schema. Abstract The mechanism of energy supply has been greatly changed due to the liberalization of electricity, which creates major challenges in power system operations. As such, in this paper, a hierarchical alternating direction method of multipliers (ADMM) approach for the unit commitment (UC) problem is presented, which is proposed to solve the UC problem of each individual unit in parallel. This approach consists of two-layers ADMM. In outer layer, the x -update and v -update steps can be decoupled for each unit and executed in parallel after rearranging and grouping the variables and constraints of the UC mixed integer quadratic programming (MIQP) model according to each unit. The difficulty in this approach is the z-update steps, which couple all units because of system constraints. We used an inner consensus ADMM to solve their Lagrangian dual problems, which are decoupled for each individual unit. Then, the UC problem with a different number of units without transmission constraints is used to test the effectiveness of the algorithm. The simulation results show that the proposed hierarchical ADMM can obtain high-quality solutions in reasonable times and keep the units' information secret. It can also help to obtain a good speedup and is suitable for solving large-scale distributed unit commitment (D-UC) problems. [ABSTRACT FROM AUTHOR]

This paper proposes a new model for the Turkish Day Ahead Electricity Market including network equations. The existing one does not implicitly consider the power flow limitations, and these are considered separately that might result with a suboptimal day-ahead schedule. The dc network equations are added to the model, which is used to determine the Day Ahead System Sell and Buy Decisions and System Marginal Price in day-ahead planning. The impact of integration of network equations is exhibited using a small 6-bus network, mid-sized Turkish northwest sub-system, and full Turkish electric power system. [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]

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]

In some highly populated areas of the planet, air quality is an issue. Since power plants in those areas significantly contribute to air pollution due to SO 2 and NO x emissions, it is appropriate to operate such power plants enforcing admissible emission limits. Since emissions directly impact the air quality index, which is highly uncertain 24 h in advance, admissible emission limits should be modeled as stochastic parameters.To represent the stochastic nature of such admissible emission limits, we propose a two-stage stochastic programming model for the commitment of power plants. The first stage represents the day-ahead scheduling, and the second stage represents the real-time power system operation under different weather and thus admissible emission limit conditions. We show the effect of stochastic emission limits on power system operations using a simple example and a 118-node case study. [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]

The deregulation of electricity markets has transformed the unit commitment and economic dispatch problem in power systems from cost minimization approach to profit maximization approach in which generation company (GENCO)/independent power producer (IPP) would schedule the available generators to maximize the profit for the forecasted prices in day ahead market (DAM). The PBUC is a highly complex optimization problem with equal, in equal and bound constraints which allocates scheduling of thermal generators in energy and reserve markets with no obligation to load and reserve satisfaction. The quality of the solution is important in deciding the commitment status and there by affecting profit incurred by GENCO/IPPs. This paper proposes a binary coded fireworks algorithm through mimicking spectacular display of glorious fireworks explosion in sky. In deregulated market GENCO/IPP has the freedom to schedule its generators in one or more market(s) based on the profit. The proposed algorithm is tested on thermal unit system for different participation scenarios namely with and without reserve market participation. Results demonstrate the superiority of the proposed algorithm in solving PBUC compared to some existing benchmark algorithms in terms of profit and number of iterations. [ABSTRACT FROM AUTHOR]

Saving of fuel cost can be done through proper commitment of available generating units. This paper presents a novel technique to solve the problem of unit commitment through sorting of units into different clusters based on Imperialistic Competition Algorithm (ICA). This sorting is implemented in order to decrease the overall operating cost and to assure the various constraints that involve minimum up/down. The technique of unit commitment is a significant assignment in the normal working of power systems which can actually be represented as a large scale minimization problem that involves non linear mixed integers. A new technique employing the concept of cluster algorithm called as additive and divisive hierarchical clustering has been used based on a new technique called as Imperialistic Competition Algorithm in order to carry out the technique of unit commitment. Proposed methodology involves two individual algorithms. Additive cluster algorithm has been employed while the load is increasing while divisive cluster algorithm has been used when the load is decreasing. The technique that has been developed has been tested on system with generating units in range of 10–100 and the superior performance of the technique has been reported through simulation results. [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]

Unit commitment (UC) problem on a large scale with the ramp rate and prohibited zone constraints is a very complicated nonlinear optimization problem with huge number of constraints. This paper presents a new hybrid approach of ’Gaussian Harmony Search’ (GHS) and ’Jumping Gene Transposition’ (JGT) algorithm (GHS-JGT) for UC problem. In this proposed hybrid GHS-JGT for UC problem, scheduling variables are handled in binary form and other constants directly through optimum conditions in decimal form. The efficiency of this method is tested on ten units, forty units and hundred units test system. Simulation results obtained by GHS-JGT algorithm for each case show a better generation cost in less time interval, in comparison to the other existing results. [ABSTRACT FROM AUTHOR]

A coupled optimization of the electricity and gas systems is presented in this paper. The electricity problem involves a unit commitment with co-optimization of energy and reserves under a power pool, considering all system operational and unit technical constraints. The gas problem involves a medium-scale highly non-convex and non-linear problem structure, which is modeled as a Mixed Integer Non-Linear Programming model. The decomposition of the overall problem is based on the Augmented Lagrangian method. An iterative process is implemented, coordinating the two interdependent systems using an alternating minimization method, in which the Lagrange multipliers are updated using the subgradient method. The gas problem is solved in two phases in order to avoid numerical instabilities; first, the direction of flow is defined, and then the gas flow is derived in the second phase. The solution algorithm is evaluated using the Greek power and gas system, comprising thirteen gas-fired units and fifty-three gas network nodes. The test results indicate the strong interdependence of the two systems, and demonstrate the efficiency of the presented algorithm in coordinating them. [ABSTRACT FROM AUTHOR]

The unit commitment problem (UCP) for thermal units consists of finding an optimal electricity production plan for a given time horizon. In this paper we propose hybrid approaches which combine Variable Neighborhood Search (VNS) metaheuristic and mathematical programming to solve this NP-hard problem. Four new VNS based methods, including one with adaptive choice of neighborhood order used within deterministic exploration of neighborhoods, are proposed. A convex economic dispatch subproblem is solved by Lambda iteration method in each time period. Extensive computational experiments are performed on well-known test instances from the literature as well as on new large instances generated by us. It appears that the proposed heuristics successfully solve both small and large scale problems. Moreover, they outperform other well-known heuristics that can be considered as the state-of-the-art approaches. [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]

This paper presents a new approach using swarm intelligence algorithm called Fireworks Algorithm applied to determine Unit Commitment and generation cost (UC) by considering prohibited operating zones. Inspired by the swarm behaviour of fireworks, an algorithm based on the explosion (search) process and the mechanisms of keeping the diversity of sparks has been developed to minimize the total generation cost over a given scheduled time period and to give the most cost-effective combination of generating units to meet forecasted load and reserve requirements, while adhering to generator and transmission constraints. The primary focus is to achieve better optimization while incorporating a large and often complicated set of constraints like generation limits, meeting the load demand, spinning reserves, minimum up/down time and including more realistic constraints, such as considering the restricted/prohibited operating zones of a generator. The generating units have certain ranges where operation is restricted based upon physical limitations of machine components or instability, e.g., due to steam valve or vibration in shaft bearings. Therefore, prohibited operating zones as a prominent constraint must be considered. In this paper the incorporating of complicated constraints of an optimization problem into the objective function is not considered by neglecting the penalty term. Numerical simulations have been carried out on 10 – unit 24 – hour system. [ABSTRACT FROM AUTHOR]

Harmony Search (HS) is a population based metaheuristics search algorithm inspired from the musical process of searching for a perfect state of harmony and has ability to escape from local minima, does not require differential gradients and initial value setting for the variables and free from divergence and has strong ability on exploring the regions of solution space in a reasonable time. However, it has lower exploitation ability in later period and it easily trapped into local optima and converges very slowly. To improve the exploitation ability of HS algorithm in later stage and provide global optimal solution, a memetic algorithm approach considering Harmony Search and Random Search is presented in the proposed research to solve unit commitment problem of electric power system. The proposed memetic algorithm is tested for IEEE benchmarks consisting of 4, 10, 20 and 40 generating units. The effectiveness of proposed hybrid algorithm is tested for unimodal and multimodal benchmark functions and is compared with others well known evolutionary, heuristics and meta-heuristics search algorithms and it has been found that performance of proposed hybrid algorithm is much better than classical Harmony Search Algorithm and Improved Harmony Search Algorithm as well as recently developed algorithms. [ABSTRACT FROM AUTHOR]

A stochastic model for day-ahead Micro-Grid (MG) management is proposed in this paper. The presented model uses probabilistic reconfiguration and Unit Commitment (UC) simultaneously to achieve the optimal set points of the MG’s units besides the MG optimal topology for day-ahead power market. The proposed operation method is employed to maximize MG’s benefit considering load demand and wind power generation uncertainty. MG’s day-ahead benefit is considered as the Objective Function (OF) and Particle Swarm Optimization (PSO) algorithm is used to solve the problem. For modeling uncertainties, some scenarios are generated according to Monte Carlo Simulation (MCS), and MG optimal operation is analyzed under these scenarios. The case study is a typical 10-bus MG, including Wind Turbine (WT), battery, Micro-Turbines (MTs), vital and non-vital loads. This MG is connected to the upstream network in one bus. Finally, the optimal set points of dispatchable units and best topology of MG are determined by scenario aggregation, and these amounts are proposed for the day-ahead operation. In fact, the proposed model is able to minimize the undesirable impact of uncertainties on MG’s benefit by creating different scenarios. [ABSTRACT FROM AUTHOR]

For Generation Companies (GENCOs) one of the most relevant issue is the commitment of the units, the scheduling of them over a daily (or longer) time frame, with the aim of obtaining the best profit. It strongly depends on the plant operational generation costs, which depend in turn on the choices taken at the design stage; it follows that design technical choices should also aim at determining the best generation cost structure of generating units with respect to the market opportunities. In the paper the unit commitment (UC) problem has been considered, with highlights on changes in the market scenario. The paper analyzes the relevance of some design choices (structure, size, regulation type) on the economics of the operation of gas–steam combined cycle generating units. To solve the UC problem, a recently proposed method for mixed integer nonlinear programming problems, with the use of a derivative free algorithm to solve the continuous subproblems, has been considered. The results for two GENCOs are reported: one managing a single unit and the other managing three units. Numerical examples show the sensitivity of the UC solutions to the market conditions and to the design choices on the regulation type in the evolving scenario of the Italian Electricity Market. [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]

Small-scale power systems mainly present two particular problems when it comes to their modeling and solution. Due to their size, it is important to consider and meet various types of reserve requirements in order to have a reliable operation of the power system. It is also important to consider variable start-up costs in order to obtain a more accurate unit commitment. In this paper six different types of reserve requirements are considered for the unit commitment problem: spinning reserve, regulation reserve (AGC per unit), ten-minute reserve, ten-minute non-synchronized reserve, ten-minute operational reserve, and ten-minute distributed reserve. Additionally, a Mixed Integer Programming formulation is introduced to represent variable start-up costs. The model introduced here is currently in use by the Baja California (México) power system operator. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for reducing the operating cost of microgrids. The proposed algorithm determines the day-ahead microgrid scheduling and builds a fuzzy expert system to control the power output of the storage system. To perform such tasks, two genetic algorithms were employed. One of them generates the microgrid scheduling and determines the fuzzy rules of the expert system, whereas the other is used to tune the membership functions. In this way it is possible to optimize the expert system according to load demand, wind power availability and electricity prices. Simulations were carried out in a microgrid comprising a diesel generator, a microturbine, a fuel cell, a wind turbine and a battery. Both interconnected and island operation modes were considered. Simulation results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Demand response (DR) has recently become an important resource in both system operation and market operation. The focus of this paper is to investigate and quantify the cost impact of various demand response modelings on unit commitment and dispatch in a day-ahead market regime. We have used mixed integer programming unit commitment model, in the market operation framework. Day-ahead market is modeled with a typical test system. Our research results show that DR can exert downward pressure on electricity prices, causing significant implications on social welfare. Results from this work will help policy makers, resource planners, and market designers to make more informed decisions with the goal of better accommodating more demand response resources in the future. [ABSTRACT FROM AUTHOR]

The thermal unit commitment (UC) problem is a large-scale mixed integer quadratic programming (MIQP), which is difficult to solve efficiently, especially for large-scale instances. This paper presents a projected reformulation for UC problem. After projecting the power output of unit onto [0,1], a novel MIQP reformulation, denoted as P-MIQP, can be formed. The obtained P-MIQP is tighter than traditional MIQP formulation of UC problem. And the reduced problem of P-MIQP, which is eventually solved by solvers such as CPLEX, is compacter than that of traditional MIQP. In addition, two mixed integer linear programming (MILP) formulations can be obtained from traditional MIQP and our P-MIQP of UC by replacing the quadratic terms in the objective functions with a sequence of piece-wise perspective-cuts. Projected MILP is also tighter and compacter than the traditional MILP due to the same reason of MIQP. The simulation results for realistic instances that range in size from 10 to 200 units over a scheduling period of 24 h show that the projected reformulation yields tight and compact mixed integer programming UC formulations, which are competitive with currently traditional ones. [ABSTRACT FROM AUTHOR]

This paper presents an improved priority list and neighborhood search (IPL-NS) method for ramp rate constrained unit commitment (RUC). The IPL-NS consists of two steps. At the first step, an IPL based on a mixed integer linear programming (MILP) formulation for UC is proposed to get unit status for UC without ramp rate constraints. At the second step, a neighborhood search (NS) algorithm based on three neighborhoods is produced to get unit status for RUC. The IPL-NS approach is applied to solve several popular test systems of up to 1000 units and the simulation results confirm the superiority of the proposed method. At the same time, the numerical results show that the proposed IPL can commit the most economic unit first much better than other existing PL methods. The results also indicate that the presented NS can handle the ramp rate constraints efficiently. [ABSTRACT FROM AUTHOR]

Optimal operation of power systems with high integration of renewable power sources has become difficult as a consequence of the random nature of some sources like wind energy and photovoltaic energy. Nowadays this problem is solved using the Monte Carlo Simulation (MCS) approach, which allows the consideration of important statistical characteristics of wind and solar power production, such as the correlation between consecutive observations, the diurnal profile of the forecasted power production, and the forecasting error. In this paper, a new model of the unit scheduling of power systems with significant renewable power generation based on the scenario generation/reduction method combined with the priority list (PL) method is proposed that finds the probability distribution function (PDF) of a determined generator be committed or not. This approach allows the recognition of the role of each generation unit on the day-ahead unit commitment (UC) problem with a probabilistic point of view, which is important for acquiring a cost-effective and reliable solution. The capabilities and performance of the proposed approach are illustrated through the analysis of a study case, where the spinning reserve requirements are probabilistically verified with success. [ABSTRACT FROM AUTHOR]

An efficient method to solve the long-term security constrained unit commitment (SCUC) is a requisite for power system operators in many countries, particularly those with many coal-fired generating units, which require a long time to start up and shut down. However, the complexity that is introduced by the energy or fuel constraints in the long-term SCUC problem often results in exorbitant computation time. In this paper, a new type of approach, which is termed the inducing-objective-function (IOF)-based method, is proposed. This method significantly improves the computational efficiency. In the proposed method, the inducing factors are introduced into the objective function of the SCUC relaxation problem to induce more UC state variables to satisfy the integrality requirement after several iterations. According to the relaxation solution, the objective function of the original SCUC model is modified. Thus, the process of solving the modified model using the branch-and-cut algorithm can be more efficient. Because the solution of this modified model can be near-optimal for the original model, the gap between the near-optimal and the optimal solution is estimated to decide whether the near-optimal solution is acceptable for practical application. If the near-optimal solution is not acceptable, it will be used as the initial solution to solve the original model. Because the initial solution is of high quality, it can significantly speed up the branch-and-cut process. Two case studies with different scales demonstrate the robustness, the efficiency and the prospect of the proposed method for practical applications. [ABSTRACT FROM AUTHOR]

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]

This paper presents a new adaptive bi-level optimization approach to solve short-term hydrothermal coordination (STHTC) problem with AC network constraints. The proposed bi-level approach is composed of an integer coded genetic algorithm (ICGA) to solve unit commitment (UC) in the upper level and a new modified chaotic simulated annealing (MCSA) technique to determine economic dispatch (ED) for each time interval in the lower level. Moreover, a new composite constraint handling method is also presented, which enhances the efficiency of the proposed approach to solve STHTC problem with AC constraints. The proposed methodology is tested on the 9-bus and IEEE 118-bus test systems. The obtained results confirm the validity of the developed approach. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper presents QOTLBO based algorithm to solve UC problems. [•] Presents quasi oppositional based learning strategy to improve TLBO algorithm. [•] Valve-point effects, ramp rate, spinning reserve and up/down time constraints are considered. [•] The robustness of the QOTLBO is demonstrated in five standard UC problems. [•] Performance of QOTLBO is found to be better than other techniques. [Copyright &y& Elsevier]

Highlights: [•] The combinatorial complexity of unit commitment problem was greatly reduced. [•] Time dependent costs and constraints of thermal generators are not linearized. [•] Achieved near optimal unit commitment schedule which is better than MILP solution. [Copyright &y& Elsevier]

Highlights: [•] An energy and reserve market based on two-stage stochastic programming is proposed. [•] Spinning and demand side reserves are considered as operating reserve services. [•] Demand side (DS) reserve is modeled as emergency demand response program. [•] Demand side reserve causes reducing cost and increasing reliability significantly. [•] Increasing incentive price and electricity price would increase DS participation. [ABSTRACT FROM AUTHOR]

Highlights: [•] A tighter relaxation method is proposed for unit commitment based on SOCP and VIs. [•] All the MCIs for minimum on/off time constraints are obtained by simple formulas. [•] The proposed method has good convergence properties. [Copyright &y& Elsevier]

Highlights: [•] Solution to unit commitment problem using IWO algorithm. [•] We tested the algorithm with 10 units 24h systems. [•] Economic dispatch for each hour was also solved using IWO. [•] Five cycles has been considered because of five peaks in the demand. [•] Operating cost is considerably less when compared to other methods. [Copyright &y& Elsevier]

Highlights: [•] Symmetry breaking method is applied to MILP based UC problems with identical units. [•] Elements that generate symmetry in UC are identified. [•] The B&C algorithm under the presence of symmetry is evaluated. [•] Addition of constraints to set generator status variable priorities is proposed. [•] Results show that this method leads to a substantial reduction in computation cost. [Copyright &y& Elsevier]

Highlights: [•] Unit commitment based GSA technique is introduced. [•] Seven test systems are used in this article. [•] Spinning reserve constrains, ramping constraints, minimum up and down time constraints are also considered. [•] The performance of GSA is compared with other optimization techniques. [•] Robustness of the proposed method is checked. [ABSTRACT FROM AUTHOR]

Abstract: One of the main needs that power system operators around the world have is to solve complex Unit Commitment models for large-scale power systems in an acceptable computation time. This Paper presents an alternative Heuristic algorithm that successfully addresses this need. The Heuristic algorithm makes use of various optimization techniques such as Mixed Integer Linear Programming (MILP), Quadratic Programming (QP), Quadratically Constrained Programming (QCP), and Dynamic Programming (DP). CPLEX 12.2 is used as the main optimization engine for MILP, QP, and QCP. DP is an in-house algorithm used to obtain the commitment of Combined Cycle Plants (CCPs) when represented with the component-based model. This Heuristic algorithm combines the global optimality capabilities of MI (L) P formulations with the highly detailed models available for CCPs using LR–DP formulations. The Heuristic algorithm introduced in this Paper is capable of solving up to 1-week scenarios with a 1-hour time window for the complex Mexican Power System. [Copyright &y& Elsevier]

Abstract: This work presents a hybrid Taguchi-ant colony system (HTACS) algorithm to solve the unit commitment (UC) problem. The proposed algorithm integrates the Taguchi method and the conventional ant colony system (ACS) algorithm, providing a powerful global exploration capability. The Taguchi method is incorporated into the ACS process before its global pheromone update mechanism. Based on the systematic reasoning ability of the Taguchi method, improved UC solutions are selected quickly to represent potential UC schedules, subsequently, enhancing the ACS algorithm. Therefore, the proposed HTACS algorithm can be highly robust, statistically sound and quickly convergent. Additionally, feasibility of the proposed algorithm is demonstrated on a 10-unit system. Analysis results demonstrate that the proposed algorithm is feasible, robust, and more effective in solving the UC problem than conventional ACS methods. [Copyright &y& Elsevier]

Abstract: Unit Commitment (UC) is a combinatorial optimization problem that can be posed as minimizing a quadratic objective function under quadratic constraints. This paper presents a solution to UC based on Semidefinite Programming (SDP). In particular, it shows that an approximate solution can be obtained by using Shor’s semidefinite relaxation scheme together with a rank constraint enforced via convex iteration. The approximate solution has the majority of Boolean variables set by the SDP solver to either 0 or 1; it is modified by a simple heuristic to yield a feasible schedule. The proposed SDP formulation employs 3×3 semidefinite matrices and therefore requires computational effort that increases only moderately with problem size. Numerical results on test systems with up to 100 units dispatched over a period of 24h show that the method is robust and produces schedules that are comparable with those from previous techniques. [Copyright &y& Elsevier]

Abstract: Unit-commitment (UC) as a complicated problem needs powerful methods to solve. This paper presents the use of harmony search algorithm (HSA), a recently developed meta-heuristic algorithm, in order to obtain optimal solution for the UC problem. The proposed algorithm has simple implementation and provides optimal solutions in a reasonable time. The method is tested using small and large scale test cases in the literature. Numerical results show that the proposed algorithm can find better solutions in comparison with conventional methods and it is an efficient way to solve UC problems especially in large-scale power systems. [Copyright &y& Elsevier]

Abstract: In this paper, the Shuffled Frog Leaping Algorithm is proposed to solve the Profit Based Unit Commitment problem under deregulated environment with emission limitation. The bi-objective function optimization problem is formulated as a maximization of the Generation Companies profit and a minimization of the emission output of the thermal units, while all of the constrains should be satisfied. This work, considers the new softer demand constraint to allocate fixed and transitional cost to the scheduled hours. The IEEE 10 unit 39 bus test system with 24h data is taken as the input for simulation using MATLAB 7.10 version. From the results obtained, it is observed the proposed algorithm achieves maximum profit and minimum emission level with less computational time compared to traditional unit commitment. [Copyright &y& Elsevier]

Abstract: We describe a new model for the hydro unit commitment and loading (HUCL) problem that has been developed to be used as a support tool for day-ahead operation in the Brazilian system. The objective is to determine the optimal unit commitment and generation schedules for cascaded plants with multiple units and a head-dependent hydropower model. In this paper, we propose a new mathematical model for the hydropower function where the mechanical and electrical losses in the turbine-generator are included. We model the HUCL problem as a nonlinear mixed 0–1 programming problem and solve it with a strategy that includes a two-phase approach based on dual decomposition. The computational tool allows the model to effectively schedule hydro units for the problem in the Brazilian regulatory framework. Application of the approach is demonstrated by determining a 24-time step HUCL schedule for four cascaded plants with 4170MW of installed capacity. [Copyright &y& Elsevier]

Abstract: This paper addresses the self-scheduling problem of generation companies owning thermal power units considering bilateral contracts and day-ahead market. This approach allows precise modelling of variable costs, start-up costs and comprehensive system of constraints. The self-scheduling model is formulated as deterministic optimization problem in which expected profit is maximized by 0/1 mixed-integer linear programming technique. Solution is achieved using the homogeneous and self-dual interior point method for linear programming, with a branch and bound optimizer for integer programming. The effectiveness of the proposed model for optimizing the thermal generation schedule is demonstrated through the case study with detailed discussion. [Copyright &y& Elsevier]