The unit commitment problem(UCP)corresponds to the planning of power generation *** objective of the fuel-based unit commitment problem is to determine the optimal schedule of power generators needed to meet the power...
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The unit commitment problem(UCP)corresponds to the planning of power generation *** objective of the fuel-based unit commitment problem is to determine the optimal schedule of power generators needed to meet the power demand,which also minimizes the total operating cost while adhering to different constraints such as power generation limits,unit startup,and shutdown *** this paper,four different binary variants of the Bald Eagle Search(BES)algorithm,were introduced,which used two variants using S-shape,U-shape,and V-shape transfer *** addition,the best-performing variant(using an S-shape transfer function)was selected and improved further by incorporating two binary operators:swap-window and *** variation is labeled Improved Binary Bald Eagle Search(IBBESS2).All five variants of the proposed algorithm were successfully adopted to solve the fuel-based unit commitment problem using seven test cases of 4-,10-,20-,40-,60-,80-,and *** comparative evaluation,34 comparative methods from existing literature were compared,in which IBBESS2 achieved competitive scores against other optimization *** other words,the proposed IBBESS2 performs better than all other competitors by achieving the best average scores in 20-,40-,60-,80-,and 100-unit ***,IBBESS2 demonstrated quicker convergence to an optimal solution than other algorithms,especially in large-scale unitcommitment *** Friedman statistical test further validates the results,where the proposed IBBESS2 is ranked the *** conclusion,the proposed IBBESS2 can be considered a powerful method for solving large-scale UCP and other related problems.
Power system operators are faced with the problem of unitcommitment belonging to mixed integer programming, which becomes very complicated, as units become large-scale and highly constrained. Because unitcommitment ...
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Power system operators are faced with the problem of unitcommitment belonging to mixed integer programming, which becomes very complicated, as units become large-scale and highly constrained. Because unit commitment problem is a binary problem with commitment and de-commitment, a discrete/binary optimization algorithm with superior performance is required. This paper proposes a novel hybrid binary bat algorithm for unit commitment problem, which consists of two process. To begin with, the proposed binary bat algorithm is applied to determining the commitment schedule of unit commitment problem. Specifically, an improved crossover operator based on exponential-logic-modulo map is proposed to enhance the convergence and maintain the diversity of populations. To prevent the algorithm from falling into a local optimum, a local mutation strategy performs local perturbation. Chaotic map is responsible for updating some parameters to increase the performance of the proposed algorithm. Furthermore, Lambda-iteration method is adopted to solve economic load dispatch in continuous space. Constraint handling is performed using the heuristic constraint produce. The effectiveness of the proposed algorithm is verified by benchmark functions and test systems. Additionally, the simulation results are compared with other well-established heuristic and binary approaches.
In power system studies the unit commitment problem (UC) is solved to support market decisions and assess system adequacy. Simplifications are made to solve the UC faster, but they are made without considering the con...
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In power system studies the unit commitment problem (UC) is solved to support market decisions and assess system adequacy. Simplifications are made to solve the UC faster, but they are made without considering the consequences on solution quality. In this study we thoroughly investigated the impacts of simplifications on solution quality and computation time on a benchmark set consisting of almost all the available instances in the literature. We found that omitting the minimum up- and downtime and simplifying the startup cost resulted in a significant quality loss without reducing the computation time. Omitting reserve requirements, ramping limits and transmission limits reduced the computation time, but degraded the solution significantly. However, the linear relaxation resulted in less quality loss with a significant speed-up and resulted in no difference when unserved energy was minimized. Finally, we found that the average and maximum capacity factor difference is large for all model variants.
This paper proposes a Lagrangian dual-based polynomial-time approximation algorithm for solving the single-period unit commitment problem,which can be formulated as a mixed-integer quadratic programming problem and pr...
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This paper proposes a Lagrangian dual-based polynomial-time approximation algorithm for solving the single-period unit commitment problem,which can be formulated as a mixed-integer quadratic programming problem and proven to be *** theoretical bounds for the absolute errors and relative errors of the approximate solutions generated by the proposed algorithm are *** results support the effectiveness and efficiency of the proposed algorithm for solving large-scale problems.
This paper presents an extensive comparison between public and private natural gas-fired units in managing the unit commitment problem in the context of the Greek electricity market. Using a unique hourly dataset from...
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This paper presents an extensive comparison between public and private natural gas-fired units in managing the unit commitment problem in the context of the Greek electricity market. Using a unique hourly dataset from 2015-2019, our ap-proach utilizes risk-weighted performance metrics-Cash Flows at Risk (CFaR) and Risk Weighted Return (RWR)-to analyze performance across the public and private units. Empirical findings indicate that publicly owned natural gas-fired units outperform privately owned natural gas-fired units in terms of operational ef-ficiency, however the efficiency of privately owned natural gas-fired units is grow-ing at a faster pace and is expected to surpass the efficiency of public units within 2 or 3 years.
Displacing combustion engine vehicles with electric ones has recently emerged for reducing adverse environmental impacts and dependencies on fossil fuels. However, high electric vehicle penetration might disrupt the s...
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Displacing combustion engine vehicles with electric ones has recently emerged for reducing adverse environmental impacts and dependencies on fossil fuels. However, high electric vehicle penetration might disrupt the smooth operation of power sectors due to increased peak loads. A thorough investigation is therefore required, considering the charging of multiple electric vehicles, as flexible loads, in the unit commitment problem. A novel approach for resolving this Operational Research problem is hereby presented, combining power flow and transmission constraints with various scenarios of electric vehicles' penetration. A variant of Differential Evolution, aided by heuristic repair mechanisms, Priority Lists and advanced State-of-the-Art constraint handling techniques, is implemented to obtain feasible, near-optimal solutions. Well-established power systems including transmission constraints were used as benchmarks for testing the method proposed. The results are compared with those of a Mixed Integer-Linear algorithm based on the same formulation. They indicated that low and average demand cases might be resolved efficiently using the evolutionary approach proposed. As for large scale fleets, they might be handled by power systems at near optimal states exhibiting viable and resilient production schedules.
unitcommitment is a traditional mixed-integer non-convex problem and an optimization task in power system scheduling. The traditional methods of solving the unit commitment problem have some problems, such as slow so...
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ISBN:
(纸本)9783031096778;9783031096761
unitcommitment is a traditional mixed-integer non-convex problem and an optimization task in power system scheduling. The traditional methods of solving the unit commitment problem have some problems, such as slow solving speed, low accuracy and complex calculation. Therefore, intelligent algorithms have been applied to solve the unit combination problem with continues and discrete feature, such as Particle Swarm Optimization, Genetic Algorithm. In order to improve the solution quality of unitcommitment, this paper proposes the adaptive binary Particle Swarm Optimization with V-shaped transfer function to solve the unit commitment problem, and adopts the policy of the segmented solution. By comparison with some classical algorithm in the same unit model, the experimental results show that solving the UC problem by using improved algorithm with segmented solution has higher stability and lower total energy consumption.
The unit commitment problem (UCP) is an operational research problem commonly encountered in energy management. It refers to the optimum scheduling of the generating units in a power system to efficiently meet the ele...
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The unit commitment problem (UCP) is an operational research problem commonly encountered in energy management. It refers to the optimum scheduling of the generating units in a power system to efficiently meet the electricity demand. UCP comprises two interrelated sub-problems: the unitcommitment for deciding the operating state of the units at each scheduling period and the Economic Dispatch (ED) for allocating the demand among them. Various Evolutionary Algorithms (EA) have been adopted for solving UCP, commonly assisted by the Lambda iteration method for solving the ED. In this study, an EA-based method is proposed for dealing with both sub-problems, avoiding binary variables through a simple transformation function. The method takes advantage of a repair mechanism utilizing the Priority List (PL) to steer the search towards adequate generating schedules. The impact of the cost metric chosen for creating the PL on the computational results is investigated and the use of a Plurality of PL is suggested to alleviate the biases introduced by employing constant cost metrics. Furthermore, an Elitist Mutation strategy is developed to enhance the performance of the proposed EA-based method. Simulation results on various power systems validate the beneficial effect of the proposed modifications. Compared to state of the art, the algorithm proposed has been at least equivalent, exhibiting consistently solutions of lower or competitive costs in all systems examined.
The blast wave of machine learning and artificial intelligence has also reached the power systems community, and amid the frenzy of methods and black-box tools that have been left in its wake, it is sometimes difficul...
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The blast wave of machine learning and artificial intelligence has also reached the power systems community, and amid the frenzy of methods and black-box tools that have been left in its wake, it is sometimes difficult to perceive a glimmer of Occam's razor principle. In this letter, we use the unit commitment problem (UCP), an NP hard mathematical program that is fundamental to power system operations, to show that simplicity must guide any strategy to solve it, in particular those that are based on learning from past UCP instances. To this end, we apply a naive algorithm to produce candidate solutions to the UCP and show, using a variety of realistically sized power systems, that we are able to find optimal or quasi-optimal solutions with remarkable speedups. To the best of our knowledge, this is the first work in the technical literature that quantifies how challenging learning the solution of the UCP actually is for real-size power systems. Our claim is thus that any sophistication of the learning method must be backed up with a statistically significant improvement of the results in this letter.
The discrete unit commitment problem with min-stop ramping constraints optimizes the daily production of thermal power plants, subject to an operational reactivity of thermal units in a 30-minute delay. Previously, mi...
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The discrete unit commitment problem with min-stop ramping constraints optimizes the daily production of thermal power plants, subject to an operational reactivity of thermal units in a 30-minute delay. Previously, mixed integer programming (MIP) formulations aimed at an exact optimization approach. This paper derives matheuristics to face the short time limit imposed by the operational constraints. Continuous relaxations guide the search for feasible solutions exploiting tailored variable fixing strategies. Parallel matheuristics are derived considering complementary strategies in parallel. Tests were performed on more than 600 real-life instances. Our parallel matheuristic provides high-quality solutions and outperforms the MIP approach in the time limits imposed by the industrial application. This paper illustrates a special interest for matheuristics in industrial highly constrained problems: many tailored neighborhood searches can be derived from an MIP formulation, and their combination in a parallel scheme improves the solution quality as well as the consistency of the heuristic.
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