Solving Unit Commitment Problem Research Articles

Distributed scheduling for multi-energy synergy system considering renewable energy generations and plug-in electric vehicles: A level-based coupled optimization method

Multi-energy synergy systems integrating high-penetration large-scale plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems have great potential to reduce the reliance of the grid on traditional fossil fuels. However, the random charging characteristics of plug-in electric vehicles and the uncertainty of photovoltaics may impose an additional burden on the grid and affect the supply–demand equilibrium. To address this issue, judicious scheduling optimization offers an effective solution. In this study, considering charge and discharge management of plug-in electric vehicles and intermittent photovoltaics, a novel Multi-energy synergy systems scheduling framework is developed for solving grid instability and unreliability issues. This formulates a large-scale mixed-integer problem, which calls for a powerful and effective optimizer. The new binary level-based learning optimization algorithm is proposed to address nonlinear large-scale high-coupling unit commitment problems. To investigate the feasibility of the proposed scheme, numerical experiments have been carried out considering multiple scales of unit numbers and various scenarios. Finally, the results confirm that the proposed scheduling framework is reasonable and effective in solving unit commitment problems, can achieve 3.3% cost reduction and demonstrates superior performance in handling large-scale energy optimization problems. The integration of plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems is verified to reduce the output power of 192.72 MW units during peak periods to improve grid stability. Therefore, optimizing energy utilization and distribution will become an indispensable part of future power systems.

Novel Resolution of Unit Commitment Problems Through Quantum Surrogate Lagrangian Relaxation

Unit commitment (UC) problems faced by Independent System Operators on a daily basis are becoming increasingly complex due to the recent push for renewables and the consideration of sub-hourly UC to accommodate the increasing variability in the net load. A disruptive solution methodology to address the growing complexity is therefore required. Quantum computing offers a promise to overcome the combinatorial complexity through the use of the so-called “qubits.” To make the best use of quantum computers available currently or in the foreseeable future to solve UC problems with a much larger number of binary variables than the number of qubits available, this paper devises a novel solution methodology based on a synergistic combination of quantum computing and Surrogate Lagrangian Relaxation (SLR) to solve UC problems. Our new contributions include: 1) A Quantum-SLR (QSLR) algorithm incorporating quantum approximate optimization algorithm (QAOA) into the SLR method, which overcomes the fundamental difficulties of previous LR-based quantum methods such as zigzagging of multipliers and the need to know or estimate the optimal dual value for convergence; 2) A Distributed QSLR framework (D-QSLR) capable of coordinating local quantum/classical computing resources with those within neighborhoods and, in the meantime, protecting data privacy; 3) A Quantized UC model to obtain accurate commitment unit subproblems decision by using a quantum machine; and 4) A time-unit-decomposed quantum UC approach to overcoming the quantum resources’ limitations. Promising quantum test results validate the effectiveness of QSLR and the scalability of the UC-oriented D-QSLR algorithm, which demonstrate QSLR’s enormous potential in UC optimization.

Hybridizing genetic algorithm with grey prediction evolution algorithm for solving unit commitment problem

Hybridizing genetic algorithm with grey prediction evolution algorithm for solving unit commitment problem

Multi–dimensional firefly algorithm based on local search for solving unit commitment problem

The Unit Commitment problem (UC) is a complex mixed-integer nonlinear programming problem, so the main challenge faced by many researchers is obtaining the optimal solution. Therefore, this dissertation proposes a new methodology combining the multi-dimensional firefly algorithm with local search called LS-MFA and utilizes it to solve the UC problem. In addition, adaptive adjustment, tolerance mechanism, and pit-jumping random strategy help to improve the optimal path and simplify the redundant solutions. The experimental work of unit commitment with the output of 10–100 machines in the 24-hour period is carried out in this paper. And it shows that compared with the previous UC artificial intelligence algorithms, the total cost obtained by LS-MFA is less and the results are excellent.

Application of Binary Slime Mould Algorithm for Solving Unit Commitment Problem

Application of Binary Slime Mould Algorithm for Solving Unit Commitment Problem

An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems

An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems

A Multi-Layer Data-Driven Security Constrained Unit Commitment Approach with Feasibility Compliance

Security constrained unit commitment is an essential part of the day-ahead energy markets. The presence of discrete and continuous variables makes it a complex, mixed-integer, and time-hungry optimization problem. Grid operators solve unit commitment problems multiple times daily with only minor changes in the operating conditions. Solving a large-scale unit commitment problem requires considerable computational effort and a reasonable time. However, the solution time can be improved by exploiting the fact that the operating conditions do not change significantly in the day-ahead market clearing. Therefore, in this paper, a novel multi-layer data-driven approach is proposed, which significantly improves the solution time (90% time-reduction on average for the three studied systems). The proposed approach not only provides a near-optimal solution (<1% optimality gap) but also ensures that it is feasible for the stable operation of the system (0% infeasible predicted solutions). The efficacy of the developed algorithm is demonstrated through numerical simulations on three test systems, namely a 4-bus system and the IEEE 39-bus and 118-bus systems, and promising results are obtained.

Implementation of coyote optimization algorithm for solving unit commitment problem in power systems

The aim of the Unit Commitment(UC) problem is to find the optimum scheduling of the total generating units at lower operating costs while achieving the constraints of system and units. The decision variables contain the binary UC variables that characterize the 1/0 cases through the total time intervals in the study era. The scale of this problem grows speedily with great size of the electric power system and longer planning time. Fixing this large scale problem is a challenging process and computationally expensive. It is the most complicated optimization process in the operation and planning of the power system. Meta heuristics methods are capable to outlast the demerits of traditional deterministic methods in solving UC problem. One of the most recent meta heuristics methods is known as Coyote Optimization Algorithm (COA). It is depended on the adaptation attitude of the coyote by the surroundings and the coyote's experiences exchanging. It has a motivating mechanisms to gain a balance between exploitation and exploration. Also, it is very easy in implementation as it has only two control variables. Moreover, its capability to keep larger diversity helps it to get the optimal cost so it is proposed to handle the UC problem in this paper. The election of the schedule and production size are performed by COA. Achievement of COA is examined for two IEEE systems. Outcomes establish that the elected algorithm is supreme to the recorded literature methods in terms of total cost, CPU, percentage reduction, and statistical analysis.

A hybrid data-driven robust optimization approach for unit commitment considering volatile wind power

This paper presents a data-driven hybrid method combining kernel density estimation (KDE) and Wasserstein metric to solve unit commitment problems considering volatile wind power. The proposed approach adopts KDE to deduce the underlying probability distribution function (PDF) of wind power forecasting errors, and utilizes the confidence interval of the estimated PDF to construct the support of uncertainties. Wasserstein metric is applied to measure the distances among distributions belonging to the established support. We take the PDF estimated by KDE as the center, and take a certain Wasserstein distance, so as to form a Wasserstein ball of probability distributions, namely ambiguity set. The unit commitment plan is obtained by minimizing the expected cost with regard to the worst-case distribution in the ambiguity set. As a fusion of Wasserstein technique and KDE, the proposed method inherits good abilities of conventional Wasserstein-based method, such as statistics, tractability, extensibility and data mining capacity. More importantly, since the PDF estimated by KDE can converge to the true distribution with the increase of sample data, the proposed method can effectively further narrow the conservatism of the model without losing robustness. Computational results on IEEE 57-bus, 118-bus and 145-bus systems from MATPOWER 6.0 numerically demonstrate the favorable features of the proposed method.

A Novel Gradient Based Optimizer for Solving Unit Commitment Problem

Secure and economic operation of the power system is one of the prime concerns for the engineers of 21st century. Unit Commitment (UC) represents an enhancement problem for controlling the operating schedule of units in each hour interval with different loads at various technical and environmental constraints. UC is one of the complex optimization tasks performed by power plant engineers for regular planning and operation of power system. Researchers have used a number of metaheuristics (MH) for solving this complex and demanding problem. This work aims to test the Gradient Based Optimizer (GBO) performance for treating with the UC problem. The evaluation of GBO is applied on five cases study, first case is power system network with 4-unit and the second case is power system network with 10-unit, then 20 units, then 40 units, and 100-unit system. Simulation results establish the efficacy and robustness of GBO in solving UC problem as compared to other metaheuristics such as Differential Evolution, Enhanced Genetic Algorithm, Lagrangian Relaxation, Genetic Algorithm, Ionic Bond-direct Particle Swarm Optimization, Bacteria Foraging Algorithm and Grey Wolf Algorithm. The GBO method achieve the lowest average run time than the competitor methods. The best cost function for all systems used in this work is achieved by the GBO technique.

Integrated optimization algorithm: A metaheuristic approach for complicated optimization

This paper proposes an integrated optimization algorithm (IOA) designed for solving complicated optimization problems that are non-convex, non-differentiable, non-continuous, or computationally intensive. IOA is synthesized from 5 sub-algorithms: follower search, leader search, wanderer search, crossover search, and role learning. The follower search finds better solutions by tracing the leaders. The leader search refines current optimal solutions by approaching or deviating from the central point of the population and then executes a single-round coordinate descent. The wanderer search carries out comprehensive search space expansion. The crossover search generates offspring using solutions from superior parents. Role learning automates the process in which a search agent decides whether to become a follower or a wanderer. A global optima estimation framework (GOEF) is proposed to offer guidelines for designing an efficient optimization algorithm, and IOA is proved to attain global optima. A differentiable integrated optimization algorithm (DIOA) that extends gradient descent is put forward to train deep learning models. Empirical case studies conclude that IOA shows a much faster convergence speed and finds better solutions than the other 8 comparative algorithms based on 27 benchmark functions. IOA has also been applied to solve unit commitment problems in the power system and shows satisfactory results. A power line sub-image classification model based on a convolutional neural network (CNN) is optimized by DIOA. Compared with the pure gradient descent approach, DIOA converges significantly faster and obtains a high test set accuracy with much fewer training epochs.

Inclusion of frequency stability constraints in unit commitment using separable programming

In this paper we address the problem of frequency stability in the unit commitment (UC) optimisation process. We include a set of appropriately defined frequency stability constraints in the UC problem formulation for operational planning scenarios in advance of real-time operation. Consequently, we cover the system against the loss of the largest infeed under the N−1 security criterion.The main contribution of our work consists of using the method of separable programming to incorporate a linearised Frequency Nadir constraint into the UC problem. In our work, the UC problem is formulated as a mixed-integer linear program (MILP). This renders a fast convergence in the solution for a system such as the the three area IEEE RTS-96 system. Meanwhile, we have included the possibility of synthetic inertia provision from the wind farms, which helps to increase the available inertia in the system before a generation outage. The simulations are run using an extended version of MATPOWER tailored for solving UC problems (MOST) which is run in MATLAB.

Unit Commitment with Ancillary Services in a Day-Ahead Power Market

This paper integrates Discrete Particle Swarm Optimization (DPSO) and Sequential Quadratic Programming (SQP) to propose a DPSO-SQP method for solving unit commitment problems for ancillary services. Through analysis of ancillary services, including Automatic Generation Control (AGC), Real Spinning Reserve (RSR), and Supplemental Reserve (SR), the cost model of unit commitment was developed. With the requirements of energy balance, ancillary services, and operating constraints considered, DPSO-PSO was used to calculate the energy supply of each source, including the associated AGC, RSR, and SR, and the operating cost of a day-ahead power market was calculated. A study case using the real data from thermal units of Taipower Company (TPC) and Independent Power Producers (IPPs) demonstrated effective results for the “summer” and “non-summer” seasons, as classified by TPC for the two charging rates. According to the test cases in this research, costs without ancillary services in non-summer and summer seasons are higher than those with ancillary services. The simulation results are also compared with the Genetic Algorithm (GA), Evolutionary Programming (EP), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). DPSO-PSO shows effectiveness in solving unit commitment problems with enhanced sorting efficiency, and a higher probability of reaching the global optimum.

Self-Adaptive Firefly Algorithm for solving Unit Commitment Problem in Power System

Unit Commitment is one of the crucial challenges in power system for selecting generation units to operate the system economically. It is the necessary to propose efficient solution techniques to solve unit commitment problem. In this research work a new Self-Adaptive Firefly Algorithm is planned to solve unit commitment problem. The SAFA identifies the best generating units to be functioning and offers the amount of load distribution among the units to minimize the operating cost. In order to validate SAFA an IEEE ten unit system is considered. The simulation is carried out using Matlab software and the solutions are presented. The solutions indicates that the efficiency of proposed SAFA than other solution techniques.

A novel hybrid grey wolf optimization algorithm and artificial neural network technique for stochastic unit commitment problem with uncertainty of wind power

The unit commitment (UC) is highly complex to solve the increasing integrations of wind farm due to intermittent wind power fluctuation in nature. This paper presents a hybrid methodology to solve the stochastic unit commitment (SUC) problem depending on binary mixed integer generator combination with renewable energy sources (RESs). In this combination, ON/OFF tasks of the generators are likewise included to satisfy the load requirement as for the system constraints. The proposed hybrid methodology is the consolidation of grey wolf optimization algorithm (GWOA) and artificial neural network (ANN), hence it is called the hybrid GWOANN (HGWOANN) technique. Here, the GWOA algorithm is used to optimizing the best combination of thermal generators depending on uncertain wind power, minimum operating cost and system constraints – that is, thermal generators limits, start-up cost, ramp-up time, ramp-down time, etc. ANN is utilized to capture the uncertain wind power events, therefore the system ensures maximal application of wind power. The combination of HGWOANN technique guarantees the prominent use of sustainable power sources to diminish the thermal generators unit operating cost. The proposed technique is implemented in MATLAB/Simulink site and the efficiency is assessed with different existing methods. The comparative analysis demonstrates that the proposed HGWOANN approach is proficient to solve unit commitment problems and wind integration. Here, the HGWOANN method is compared with existing techniques such as PSO, BPSO, IGSA to assess the overall performance using various metrics viz. RMSE, MAPE, MBE under 50 and 100 count of trials. In the proposed approach, the range of RMSE achieves 9.26%, MAPE achieves 0.95%, MBE achieves 1% in 50 count of trials. Moreover, in 100 count of trials, the range of RMSE achieves 7.38%, MAPE achieves 1.91%, MBE achieves 2.87%.

Heuristic based binary grasshopper optimization algorithm to solve unit commitment problem

The unit commitment problem in power system is a highly nonlinear, nonconvex, multiconstrained, complex,highly dimensional, mixed integer and combinatorial generation selection problem. The phenomenon of committing anddecommitting represents a discrete problem that requires binary/discrete optimization techniques to tackle with unitcommitment optimization problem. The key functions of the unit commitment optimization problem involve decidingwhich units to commit and then to decide their optimum power (economic dispatch). This paper confers a binarygrasshopper optimization algorithm to solve the unit commitment optimization problem under multiple constraints.The grasshopper optimization algorithm is a metaheuristic, multiple solutions-based algorithm inspired by the naturalswarming behavior of grasshopper towards food. For solving the binary unit commitment optimization problem, thereal/continues value grasshopper optimization algorithm is mapped into binary/discrete search-space by using an S-shaped sigmoid function. The proposed algorithm is tested on IEEE benchmark systems of 4, 5, 6, 10, 20, 26, 40, 60,80, and 100 generating units including the IEEE 118-bus system and the results are compared with different classical,heuristics, metaheuristics, quantum, and hybrid approaches. The results confer better performance of binary grasshopperoptimization algorithm to solve the unit commitment optimization problem.

Machine learning approaches to the unit commitment problem: Current trends, emerging challenges, and new strategies

Traditional power system operation and control decision-making processes, such as the unit commitment (UC) problem, primarily rely on the physical models and numerical calculations. With the growing scale and complexity of modern power grids, it becomes more complicated to accurately formulate the physical power system and more difficult to efficiently solve the corresponding UC problems. As a matter of fact, plenty of historical power system operation records as well as real-time data could provide useful information and insights of the underlying power grid. To this end, machine learning methods could be valuable to help understand the relationship of UC performance to power system parameters, reveal the rationality behind such relationship, and finally address UC problems in a more efficient and accurate way. This article discusses the current practices of using machine learning approaches to solve the mixed-integer linear programming based UC problems. The associated challenges are analyzed, and several promising strategies for adopting machine learning approaches to effectively solve UC problems are discussed in this article. In addition, we will also explore machine learning approaches to promptly solve steady-state nonlinear AC power flow and dynamics differential equations, so that they can be integrated into the UC problems to guarantee AC power flow security and dynamic stability of system operations, as compared to the current DC power flow constrained UC practice. Our studies show that machine learning, as model-free methods, is a valuable alternative or addition to the existing model-based methods. As a result, the effective combination of machine learning based approaches and physical model based methods are expected to derive more efficient UC solutions that can improve the secure and economic operation of power systems.

Solving Unit Commitment Problem in Deregulated Power Systems Environment. (Dept. E. )

With the fast-paced changing technologies in the power industry, new power references addressing new technologies are coming to the market. So there is an urgent need to keep track of international experiences and activities taking place in the field of modern unit-commitment (UC) problem. UC is a nonlinear mixed integer optimization problem to schedule the operation of the generating units at minimum operating cost while satisfying the demand and other equality and inequality constrains. The UC problem has to determine the on/off state of the generating units at each hour of the planning period and optimally dispatch the load among the committed units. UC is the most significant optimization task in the operation of the power systems. Solving the UC problem for large power systems is computationally expensive. The complexity of the UC problems grows exponentially with the number of generating units especially by applying the deregulated rules in power system. Where in this environment the objective function is maximizing the profit while satisfying the regular unit commitment constrains in addition to new constrains such as bilateral and multilateral contracts. So in this paper, an exact mathematical optimization procedure called “dynamic programming.” is presented to solve of the UC problem in the deregulated environment. The proposed algorithm is implemented in MATLAB environment.

Unit commitment based on modified firefly algorithm

Optimization technologies have drawn considerable interest in power system research. The success of an optimization process depends on the efficient selection of method and its parameters based on the problem to be solved. Firefly algorithm is a suitable method for power system operation scheduling. This paper presents a modified firefly algorithm to address unit commitment issues. Generally, two steps are involved in solving unit commitment problems. The first step determines the generating units to be operated, and the second step calculates the amount of demand-sharing among the units (obtained from the first step) to minimize the cost that corresponds to the load demand and constraints. In this work, the priority list method was used in the first step and the second step adopted the modified firefly algorithm. Ten generators were selected to test the proposed method, while the values of the cost function were regarded as criteria to gauge and compare the modified firefly algorithm with the classical firefly algorithm and particle swarm optimization algorithms. Results show that the proposed approach is more efficient than the other methods in terms of generator and error selections between load and generation.

Stability-Constrained Power System Scheduling: A Review

Power system scheduling mainly concerns economic optimization issues of the power system, which is also commonly known as the unit commitment (UC) problem. However, improper planning in the generation schedule may pose a negative impact on power system stability. Additionally, the trend of large-scale integration of renewable energy in the future power system brings critical challenges to power system stability. In consequence, it is necessary to integrate the stability constraints into power system scheduling. According to the classic classification of power system stability (i.e. voltage stability, frequency stability, and rotor angle stability), stability constraints can be constructed accordingly to guarantee system stability when solving UC problems, which ensures both the economic efficiency and technical feasibility of the UC solutions. This paper reviews typical stability constraints and how to apply these constraints in solving UC problems. Representative works are summarized to provide a guidance for addressing the stability constrained scheduling problems in the future power system operation.