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Abstract Historically, energy system tools were predominantly proprietary and not shared with others. In recent years, there has been an increase in developing open source tools by international research and development organizations. More than half of the open energy modeling (openmod) initiative listed tools are based on the freely available scripting language Python. Previous comparisons of energy and power system modeling tools focused on comparisons such as which tool category (e.g. optimization, simulation) or energy demand (e.g. electricity, cooling, and heating) can be considered. Until now, the assessment of incorporated functions such as unit commitment (UC) or optimum power flow (OPF) has been ignored. Therefore, this work assesses 31 mostly open source tools based on 81 functions for their maturity. The result shows that available open source tools such as Switch, TEMOA, OSeMOSYS, and pyPSA are mature enough based on a function comparison with commercial or proprietary tools for serious use. Nevertheless, future commercial, as well as open source energy system analysis tools, have to consider more functions such as the impact of ambient air conditions and part-load behavior to allow better assessments of including high shares or renewable energy sources and other flexibility measures in existing and new energy systems. Highlights • overview about history within Open Source energy model development; • assessment of 81 modeling details; • detailed analysis of 31 energy models (mainly open source); • identifies area of further research requirements; • supports to achieve an unbiased decision process to select the 'best' suitable model; [ABSTRACT FROM AUTHOR]

This paper proposes an integrated framework for operational flexibility assessment in power system planning with a significant share of intermittent renewable energy sources (RES). The framework proposed includes: (i) the formulation of an integrated generation expansion planning (GEP) and unit commitment (UC) model accounting for key short-term technical constraints, (ii) the elaboration of accurate and systematic horizon reduction methods to alleviate the computational burden of the resulting large-sized optimization problems and (iii) the definition of suitable metrics for the operational flexibility assessment of the obtained plans. The framework is applied to a ten year planning horizon of a realistically sized case study representing the national power system of France, under several scenarios of RES penetration levels and carbon limits, spanning levels of up to 50%. The importance of incorporating the detailed short-term constraints within long-term planning models is shown. The results of the assessment show that, under high renewable energy penetration, neglecting the short-term constraints may lead to plans significantly short on flexibility, reaching shortage levels of up to 50% in frequency and several GWs in magnitude. Also, the load not served reaches levels of up to 3% and carbon emission is underestimated by up to 60%. Furthermore, the results highlight the importance of relying on suitable quantitative metrics for operational flexibility assessment in power systems planning rather than solely relying on generic performance measures, such as system costs and mixes of power plants, which are shown not to sufficiently reflect the flexibility levels of the obtained plans. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal scheduling/allocation of energy and spinning reserves for a wind-thermal power system. There is a considerable need for the renewable energy resources in the modern power system; therefore, in this paper, wind energy generators are used. Here, two different market clearing models are proposed. One model includes reserve offers from the conventional thermal generators, and the other includes reserve offers from both thermal generators, and demand/consumers. The stochastic behavior of wind speed and wind power generation is represented by the Weibull probability density function. The objective function considered in this paper includes cost of energy provided by conventional thermal and wind generators, cost of reserves provided by conventional thermal generators and load demands. It also includes costs due to under-estimation and over-estimation of available wind power generation. Clustered adaptive teaching learning based optimization algorithm is used to solve the proposed optimal scheduling problem for both conventional and wind-thermal power systems considering the provision for spinning reserves. To show the effectiveness and feasibility of the proposed frame work, various case studies are presented for two different test systems. [ABSTRACT FROM AUTHOR]

SUMMARY Suppose a special district (group) that is composed of multiple companies (agents) imposed energy demand. We have proposed a distributed energy management system (DEMS) that optimizes energy consumption of a group through energy trading in the group. In this paper, we propose an efficient operation planning method for DEMS with storage device. At first, we formulate the optimization problem for agents. Second, we propose a method to search a unit commitment state that depends on group composition. We confirmed that the proposed method enables efficient use of generators in groups. [ABSTRACT FROM AUTHOR]

Unit Commitment (UC) is an optimization problem used to determine the operation schedule of the generating units at every hour interval with varying loads and generations under different generational, environmental and technical constraints. With the significant increase of Renewable Energy Sources (RES) integration into the power networks, effects posed by these system changes to the UC are actively being studied and investigated by global researchers and operation engineers. To this end, this paper firstly provides a literature survey of UC concept, objectives and constraints. Different UC models developed for addressing RES impacts are also reviewed. Moreover, many algorithms have been proposed in the past few decades to optimize the UC problem. This work explores the necessity for alternative optimization approaches for UC solution. In doing that, the work uncovers the advantages and disadvantages of the existing methodologies so that future algorithms could be designed in retaining the advantages of the existing methodologies while avoiding the presented weaknesses. In addition, installation of energy storage devices to balance the fluctuation in power generation and their associated impacts on UC models are reviewed. The contents of this paper provide ready-to-refer and ready-to-use information for the researchers working in the field of UC. [ABSTRACT FROM AUTHOR]

This paper presents a new approach with a three-stage optimization algorithm for the least-cost optimal solution of the unit commitment problem. In the proposed work, the optimal schedule is obtained by optimizing the lambda operator for the states that violate the inequality constraints. The objective of the work is to minimize the fuel cost when subjected to various constraints such as load balance, minimum up/down time, ramp limit, and spinning reserve. This method of committing the units yields the least-cost solution when applied to the IEEE 10-unit systems and 7-unit Indian utility practical systems scheduled for 24 h and the results obtained are compared with the existing methods. [ABSTRACT FROM AUTHOR]

System operators maintain sufficient reserve in power systems in order to respond to generation and transmission contingencies. Traditionally, the reserve requirements have been determined using deterministic criteria (e.g., $N - 1$). These amounts of reserve allocate sufficient spare generation capacity and transmission margins to protect the system against any contingency of magnitude lower or equal to the chosen criterion. However, these criteria ignore the probability of individual contingencies as well as energy redistribution and transmission limitations in post-contingency states. In this paper, we propose to optimize the amount, location, and chronological procurement of the reserve in a given power system using probabilistic criteria. The proposed approach factors the probability of individual contingencies in a cost/benefit analysis, which balances the pre-contingency operating costs against the post-contingency cost of interruptions. The effectiveness of the proposed approach is demonstrated using a modified IEEE Reliability Test System. [ABSTRACT FROM AUTHOR]

This paper has proposed a novel Hybrid modified Genetic – Imperialist Competitive Algorithm (HGICA) for solving thermal Unit Commitment Problem (UCP). The UCP is a mixed integer problem with many equality and inequality constraints like the minimum down and minimum up time, spinning reserve, and ramp rate so need to a complex optimization process. In this paper the constraint handling of the problem is realized without any penalizing of solutions so a wide range of feasible solutions will be available for final optimum response. The proposed modified genetic is a novel method which has better performance than its original version and helps ICA to find more optimized responses and escape for local minimum areas easily. The main advantages of HGICA are good quality of the solution and high computational speed, which make it a suitable method for solving optimization problems. This method is carried out for three case studies including 10 and 20 units systems to efficiency of it be proved. Also the obtained results is compared to other optimization methods represented in literature for different scenarios. [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]

This paper presents an advanced optimization technique to solve unit commitment problems and reliability issues simultaneously for thermal generating units. To solve unit commitment, generalized benders decomposition along with genetic algorithm to include minimum up/down time constraints are proposed, and for reliability issues consideration, a fuzzy stochastic-based technique is presented. To implement the problem into an optimization program, the MATLAB software, and CPLEX and KNITRO solvers are used. To verify the proposed technique and algorithm, two case studies that are IEEE 14 and 118 bus systems are implemented for optimal generation scheduling, and reliability issues. Finally, a comparison with other solution techniques has been given. [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]

This paper attempts to provide an optimum loading schedule of power generating units with the least cost by solving a unit commitment (UC) problem and to present good estimates of cost differences when UC problem is not applied. UC is a fundamental optimization problem of power generation systems which determines the optimum schedule of generating units which minimizes generation costs. However, for small power generation firms which are situated in developing countries, UC-based problems are poorly understood if not implemented and the scheduling of generating units is based on some methodologies which may provide results that are not optimal. Thus, a case study in a small power generation firm in central Philippines is carried out to elucidate these objectives. The case requires a solution of the mixed-integer nonlinear programming (MINLP) problem. Results show that the proposed UC-based problem yields optimal costs and the cost disparity from the current scheduling scheme is approximately at 27% which may be considered as potential cost savings. This shows that UC-based problem provides a reliable platform in achieving minimal generation costs. These results are significant to decision-makers particularly in small power generation firms and to engineering practitioners in the Philippines and in some developing countries as these provide an overview of the disparity of cost figures of not implementing UC. [ABSTRACT FROM AUTHOR]

This paper addresses the study of symmetry in Unit Commitment (UC) problems solved by Mixed Integer Linear Programming (MILP) formulations, and using Linear Programming based Branch & Bound MILP solvers. We propose three sets of symmetry breaking constraints for UC MILP formulations exhibiting symmetry, and its impact on three UC MILP models are studied. The case studies involve the solution of 24 instances by three widely used models in the literature, with and without symmetry breaking constraints. The results show that problems that could not be solved to optimality within hours can be solved with a relatively small computational burden if the symmetry breaking constraints are assumed. The proposed symmetry breaking constraints are also compared with the symmetry breaking methods included in two MILP solvers, and the symmetry breaking constraints derived in this work have a distinct advantage over the methods in the MILP solvers. [ABSTRACT FROM AUTHOR]

Short-term optimization models, usually applied to traditional problems like UC (unit commitment) and economic dispatch problem, are essential tools for the planning and operation of power systems. However, the large number of variables and restrictions, necessary for a good and more accurate representation of any electricity system, require high computational resources, frequently resulting in high computation times. This study proposes a simplified approach of a model for the electricity planning of power plants allocation based on the available resources. The model resources to quadratic penalty functions and avoid on/off binary variables. The approach is then supported on a non-linear optimization model able to solve this electricity planning problem in shorter computation times, with solutions close to the ones obtained with more complex models. The model is fully described and tested under different scenarios of an electricity system comprising thermal, wind, and hydropower plants. The results were compared to the ones obtained with a more complex model, analysing the main differences obtained for cost, CO 2 emissions and of wind power impacts on this electricity system. The most remarkable advantage of the simplified model comes from the significant reduction on computational time needed for state-of-the-art optimization solvers to provide an optimal solution, comparatively to mixed integer models. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid approach based on a novel binary artificial bee colony (NBABC) algorithm and local search (LS) is developed to solve the unit commitment problem (UCP). Also, the ramp rate constraints are taken into account in the solution of UCP by performing conventional economic dispatch with modifying unit generating capacities over the entire scheduling time horizon. The proposed NBABC-LS method differs from its counterparts in three main aspects: (i) it utilizes a novel strategy which measures the dissimilarity between two binary strings for generating the new binary strings for UCP; (ii) it uses an intelligent scout bee phase; (iii) A LS module is hybridized with the NBABC algorithm. These modifications result in three major advantages: (i) it avoids the problem of slow and premature convergence and thus does not fall into the local optimum solutions; (ii) it can quickly find the near global optimum solution for UCP; (iii) accuracy and robustness of the solution are achieved. The proposed approach is successfully applied to the test systems up to 100 thermal units over 24-h scheduling time horizon and the real Turkish interconnected power system consisting of eight thermal units over 8-h scheduling time horizon. The obtained results confirm the quality solution in terms of total generation cost compared with the other methods reported in the literature. [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]

The Unit Commitment (UC) problem is a nonlinear and complex optimization problem used to determine the start-up and shutdown scheduling of power-generating units to meet the forecasted load demand and spinning reserve over a specified time horizon so that the target of production cost minimization is achieved while satisfying various system and generator-based constraints. The increase in computational burden with the system size requires more efficient meta-heuristic approaches for solving the UC problem. This paper proposes a modified differential evolution (DE) approach for both discrete and real part of the UC problem. The infeasibility of the solutions is also handled by incorporating some repairing mechanisms in DE, which forcefully satisfy the system constraints and speed up the search process. The experimentation is carried out on various standard and large power systems, starting from the 10-unit base case and then going up to 100 units over a 24 -h time period. The obtained results indicate the effectiveness of the proposed approach as compared to the previous work reported in the literature. [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: [•] A novel deterministic optimization approach for the Unit Commitment (UC) problem, involving thermal generating units is proposed. [•] The mathematical mixed-integer quadratic programming (MIQP) formulation of the problem is detailed. [•] Valid integer cutting planes are proposed. [•] A Branch and Cut search for finding the global optimal solution is developed. [•] Computational results and comparisons with other mathematical formulations are presented. [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]

In this paper, unit commitment (UC) problem with POZ constraint is proposed. A simple dynamic programming method is used to UC. Various constraints such as power balance, minimum up time & down time, ramp up & down, next hour load and most specifically prohibited operating zone constraint is also considered. The results are extensively validated for IEEE 10 units and 6 units. The results obtained are compared without POZ constraint. The promising results obtained are presented. [ABSTRACT FROM AUTHOR]

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]

This paper examines the polytope of feasible power generation schedules in the unit commitment (UC) problem. We provide computational results comparing formulations for the UC problem commonly found in the literature. We introduce a new class of inequalities, giving a tighter description of feasible operating schedules for generators. Computational results show that these inequalities can significantly reduce overall solution times. [ABSTRACT FROM PUBLISHER]

This paper presents a new approach via a new evolutionary algorithm known as imperialistic competition algorithm (ICA) to solve the unit commitment (UC) problem. In ICA the initial population individuals (countries) are in two types: imperialists and colonies that all together form some empires. Imperialistic competitions among these empires converge to a state in which there exists only one empire. In the proposed ICA for the UC problem, the scheduling variables are coded as integers; therefore, the minimum up/down-time constraints can be handled directly. A new method for initializing the countries is proposed. To verify the performance of the proposed algorithm, it is applied to systems with number of generating units in range of 10 up to 100 in one-day scheduling period. [ABSTRACT FROM PUBLISHER]

This paper presents a new approach to solving the short-term thermal unit commitment scheduling problem in utility system using Simulated Annealing Embedded Evolutionary Programming Method. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. A 7-Unit utility power system with twelve generating units in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different IEEE test systems consist of 10, 26 and 34 Units. Numerical results are shown comparing the cost solutions and computation time obtained by using the Evolutionary Programming method and other conventional methods like Dynamic Programming, Legrangian Relaxation and Simulated Annealing in reaching proper unit commitment. [ABSTRACT FROM AUTHOR]

This paper presents an efficient approach for solving the unit commitment problemusing an hybrid approach based on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Unit Commitment Problem (UCP) used for achieving minimum operating cost in the scheduling operation of power system generating units subjected to needed demand and reserve constraints is a nonlinear mixed integer optimization problem. The on/off state of the generating units at each hour interval of the planning period that optimally transmits load and reserve among the committed units is determined by the UC problem. The significance of the need for a more competent optimal solution to the UCP problem is increasing with the routinely changing demand. To accomplish this, hereby, we propose a hybrid approach that yields optimal commitment of the units by solving the unit commitment problem subjected to essential constraints. To verify the effectiveness of the proposed method numerical studies have been performed for the NTPS 7 unit system for a period of 24 hours. The results show that the proposed method outperforms the other state-of-the-art algorithms and the conventional methods in solving the unit commitment problems. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the authors discuss various reports within the issue on topics including the methods for the Unit Commitment problem, the optimization of shared mobility systems for cars and bicycles, and the multi-trip vehicle routing problem and related issues.