unit commitment

The mismatch between production and consumption and concern for providing the energy needed in the power grid has always been one of the problems of grid operators. Also, large-scale electricity production has always been a costly and polluting industry. Therefore, engineers and power generation companies have always been looking for a cheap and clean way to generate power so that they can overcome the power imbalance and ensure stability and the allowed level of pollutants in the network. The most common sources used in the power grid to generate power are gas and thermal units, these units enter the circuit faster and, of course, have a higher production cost. When to use thermal units and when to use gas units in the network is a complex issue, which depends on various factors such as peak time, fuel price and gas supply network conditions. None of these things are certain in their true state. Therefore, in this article, the uncertainty of gas sources and the variability of gas prices in the problem of the participation of power plant units in the power system have been modeled and analyzed by GAMS software. Also, the mixed integer non-linear programming solution method has been used to solve the problem. The results show that if there is uncertainty in the gas sources, there is a greater tendency to use other power plants in the network in order to maintain the security and stability of the network.

This paper presents a bi-level mathematical model for optimizing the unit commitment of energy generation units in the presence of worst-case scenarios of renewable resource failures. The main innovation of this research is the integration of optimal load management, intelligent scheduling of electric vehicle charging and discharging, and precise modeling of renewable resource failures within a comprehensive framework. The high-level model is designed to reduce operational costs, manage flexible loads, and minimize pollution, while the low-level model examines the effects of resource failures and ensures system stability under critical conditions. To solve the problem, the Karush-Kuhn-Tucker (KKT) optimality conditions are employed as an efficient tool to reduce the complexity of the bi-level problem, and the model is transformed into a Mixed-Integer Linear Programming (MILP) problem. The proposed model is evaluated on the standard IEEE network, and simulation results demonstrate that this approach significantly outperforms existing models. Specifically, operational costs are reduced by up to 18.5%, system reliability in critical conditions is improved by up to 35%, and energy losses are reduced by 22%. These achievements highlight the model's capability to provide resilient and efficient solutions for optimal energy resource management under uncertainty scenarios. Overall, this research offers a novel framework for enhanced productivity in renewable energy systems, which can serve as an effective tool in the development of future sustainable and resilient systems.

With penetration of renewable energy sources and increasing congestion in transmission networks, the issue of unit commitment (UC) problem has increased in importance, and power system operators demand effective approaches to find a suitable solution to this problem. In this paper, by considering a dynamic line rating (DLR) in the presence of wind power units, a model has been offeredfor the UC problem  to use the existing capacity of lines more effectively through adding smart-grid technologies to the power system. To adapt the proposed approach with real conditions, uncertainties of the problem parameters including wind speed, network load, and ambient temperature were modeled by a scenario-based method in form of a stochastic programming. A Monte-Carlo simulation was used to generate those scenarios, and K-means clustering algorithm was employed so as to reduce  scenarios . All problem limitations were taken into account, and transmission network constraints were considered by AC load flow relations. Numerical results on the IEEE RTS-24-bus system demonstrated the efficiency of the presented model in reducing transmission congestion, operating costs, wind power curtailment; and finally the enhancement of  the network’s technical criteria along with the reliable and stable planning for the power system. Rgarding simulation results, the presence of DLR increased the conductor temperature up to 7 oC by satisfying the maximum allowable temperature. This resulted in 34% higher loading of line compared to a static rating. In this way, the DLR brought  about a 15.33% reduction in total operating cost. In addition, the load shedding and wind power curtailment showed reductions of  88.9% percent  and 89.7% in the presence of DLR.

Using security-constrained unit commitment as a preventative method of supplying power system frequency response can increase the penetration of renewable energy sources while maintaining frequency response parameters within their allowable range. Moreover, it can reduce the need for corrective actions such as under-frequency load shedding and can prevent power blackouts. So far, there have been two views about the virtual inertia of grid-connected energy storage units, pessimistic (lack of virtual inertia) and optimistic (failure-free frequency response). However, in reality, the frequency response supply system may experience malfunction and incorrect operation, which is modeled and analyzed in this paper from a techno-economic standpoint. False sensor detection, control system malfunction, wrong power injection of energy storage, inverter failure, improper parameter setting, etc., can all be considered disruptive factors. However, in order to have a more general model, the article avoids dealing with the reason behind the poor performance of the storage system and focuses only on its effects, i.e., the possibility of the storage system not participating correctly. The proposed model takes into account the system's frequency stability by linearly integrating the nadir frequency and the rate of change of frequency limits to the robust mixed-integer linear programming model with YALMIP and MOSEK solvers. The results of implementing this model in the IEEE-RTS96 network show that neglecting energy storage system malfunctions can lead to violations of frequency constraints that must be compensated by increasing costs and keeping more online units with more free headroom.

Due to the significant growth of load in the power industry as well as the high penetration of wind turbines (WTs) in power systems, the issue of unit generation (UC) planning has become a critical issue. UC determines the problem of planning the production of units, the time when units are turned on and off, and how to distribute the load between the units that are turned on so that the cost is minimized; Therefore, the main focus of this article includes two scenarios that examine the production planning of units with different constraints, including the slope rate and the production prohibited area in the presence and absence of WTs; Therefore, at first, the production planning of the units is done by considering the load of future hours, which increases the reliability of the planning and also minimizes the costs. In the second part, a method for planning the production of units is presented, in this method, the minimum shutdown time of the units is also included in the objective function, in fact, the start-up costs are considered hourly. This proposed method changes the production planning pattern so that the costs are reduced. Finally, the implementation of the problem is done in two scenarios considering wind turbines and the absence of wind turbines.

This paper presents a new formulation and algorithm for the problem of unit commitment with security constraints, which can obtain the worst case of transmission network line outage and solve the problem under such conditions. Optimal charging and discharging of electric vehicles, optimal charging and discharging of energy storage systems, and flexible loads along with renewable energy resources are considered in the problem of unit commitment. Uncertainty of energy resources is modeled as a scenario-based method. In this paper, a multi-objective function that includes reduction of operating cost, no-load and unit start-up/shutdown, load shedding costs, load shifting, unit pollution, optimal charging and discharging of storages, and the power cut of renewable energy resources is considered. The proposed formulation is a mixed integer linear programming (MILP) model whose absolute optimal solution is guaranteed by powerful Gurobi solvers. To validate the proposed formulation, several study cases and test 6- and 24-bus networks are analyzed. The simulation results show that the proposed algorithm is effective in identifying the worst possible line exit of the transmission network so that the objective function of the problem increases by about 8% after the worst-case line exit from the network.

Self-Healing is the most essential feature for smart distribution network Restoration when a fault occurs. Islanding of the fault zone can be done both offline and online. Using the online islanding method to restoration the service in the fault zone, the boundary of islanding micro-grids and the number of islands can be determined optimally during the fault. In this study, a novel two-step mathematical method for self-healing restoration after the fault is presented. In the first layer, the optimal arrangement of the system in the faulty area is determined by a new mathematical model. In the first layer, the boundary of island-operating MGs is determined after the fault, which leads to decreasing load shedding and operation costs of the distribution system. Then, in the second layer, the unit commitment problem in the smart distribution system is solved. The load shedding or outage, non-dispatchable distributed generation (DG) resources rescheduling, and optimal planning energy storage systems (ESSs) are determined. Low execution time and the optimal solution are the most essential advantages of the pro po s ed scheme. Tools such as smart load shedding and demand response Programs (DRP) have also been used for optimal system restoration. The IEEE 33-bus distribution system is used to validate the prop osed method. The results of case studies demonstrate the effectiveness of the proposed methodology.

Security Constrained Unit Commitment is a practical way for the system operator to plan and operate in real time. Considering the transmission system and security constraints can lead to different responses to load distribution. One of the solutions to solve problems in the circuit of units with large-scale security constraints is to use the Benders analysis method. But strengthening the way the problem is written puts a big difference on the speed of solution and the computational volume the purpose of this research is existing N-K constraint might lead possibility to ignore some other constraints and minimize the calculation. In the proposed model, studied faults are identified in a loop, and power flow control are proceeded by adding necessary constraints. In each step, transmission system constrains are defined, and in case any overloading occurs, relating constrain is going to be activated in the following step. This strategy defines necessary constraints in each hour and line. Also, sensitive network lines which are overloaded due to lines subjected to fault exit could be defined. Considering the mentioned issues, the importance of Unit Commitment and optimal solution proposition in large scale, in the thesis we are going to analyze the unit commitment by using line outage distribution factor and its combination with N-K security constraint.  The calculation volume in large scale could be decreased by applying this strategy, thus, the computation is fast-speed. On the other hand, all the computations and comparisons are applied on IEEE 118-bus network using GAMS software and MATPOWER tool to analyze power distribution factors. A repeating constraint identification loop is employed to implement N-K constraint, and results show the loop reduces the computation time. In the proposed method, the sensitive lines and their operation conditions are identified that the appropriate response can be achieved in the fault conditions.

The issue of unit commitment (UC) is one of the most important issues in the electricity market, and with the smartening and restructuring of networks which its goals and variables have undergone changes. In this paper, solving and modeling the problem of unit commitment by considering smart networks is proposed. As we know, in smart grids, there are new issues such as demand side management and energy storage systems (batteries) as well as new goals such as reducing the environmental pollution of the units that must be considered. In this paper, the issue of unit commitment with demand side management modeling along with optimal charging and discharging of battery storage system with the aim of minimizing the economic costs of units and batteries and environmental pollution is presented. The proposed model is a Mixed Integer Linear Programming (MILP) model that is solved using powerful commercial software such as Gurobi and guarantees optimal global solutions. The proposed model is implemented on two standard IEEE 6 and 118 bus networks, the results of which show the efficiency of the proposed model.

The problem of unit commitment (UC) or in other words, the issue of placing the units in orbit is a very important optimization problem, the exact solution of which can lead to a significant reduction in operation costs. In general, UCchr('39')s goal is to minimize the total cost of production units by considering the constraints of the units and the system during the intended period. In fact, UC is divided into two issues. First, there is the issue of determining the on or off mode of production units for each hour, which is usually 24 hours, and the second is the distribution of economic dispatch between production units. Considering both problems at the same time will complicate the solution. It should also be noted that the difficulty of the problem also increases in proportion to the number of production units as well as the constraints considered. As mentioned, unit cost is typically the primary goal of minimizing UC issues. This paper considers smart grids, one of whose goals is reducing environmental pollution in addition to reducing costs. Therefore, this paper considers UC problem-solving in smart grids by considering the pollution of production units, so a multi-objective function is selected for minimization. On the other hand, with the introduction of smart grids, the existence of energy storage systems (ESS) in the network is also considered. This paper suggests optimal charging and discharging of energy storage systems, too. Another issue modeled in this paper is the issue of demand response in smart grids. Demand response program modeling allows grid demands to be shifted within the specified range at different times of the day. The proposed model is a mixed integer linear programming model (MILP) that has been tested to validate the performance of the proposed model, i.e., 4-unit and 10-unit systems with storage resources. To solve the proposed model, a powerful Gurobi solver is used. This solver also guarantees optimal global solutions and can get optimal solutions in the shortest time. In the end, the simulation results showed that modeling the problem of optimizing the charge and discharge of the energy storage system, along with the problem of demand-response management in the issue of unit participation, is very effective in reducing operating costs and reducing pollution. On the other hand, due to the linear nature of the problem, the proposed model can be solved in larger dimensions of the power systems.

System resilience as a new concept in the power industry becomes important recently. This paper with consider to the rate of change of frequncey as a new system resilience index, introduces a resilient constraint unit commitment model to increase resilience against rare and severe events that ultimately lead to outages and frequency instabilities. This model offers an effective approach to solve unit commitment problem to prevent frequency instability by using optimal distributed generation switching problem, emergency interruptible load contracts and adaptive automatic frequency load shedding and when the event occurs, it places the system’s operating point in a stable state close to its optimal point. All simulation steps in the MATLAB software were conducted on 57-bus IEEE standard system. Simulation results of the proposed method, when compared with the conventional unit commitment approach without optimal distributed generation switching, imply the effectiveness of this innovative approach in the power system frequency stability enhancement.

One of the most important problems for power system operation is unit commitment (UC), for which different constraints should be satisfied. UC is a nonlinear and large-scale problem; thus, using the evolutionary algorithms has been considered for solving the problem. In this paper, the solution of the UC problem was investigated using Modified Imperialistic Competition Algorithm (MICA).  Simulations were performed for a 10, 60 and 100-unit IEEE test system to produce the demand energy during a period of 24-hour. The obtained results were compared with those of some pervious algorithms such as GA, ICGA, PSO and their modified versions, and Cuckoo searching. The comparisons demonstrated the economic advantage of the presented method.

In this paper, the thermal unit commitment is solved by using combined fuzzy logic and Shuffled Frog Leaping Algorithm, in which the minimum and maximum generation constraints, minimum up/down-time constraints, starting time, spinning reserve,and so on are considered. Fuzzy logic is used to reduce the production time of the ON/OFF cycle durations of each unit in the feasible solutions. Shuffled Frog Leaping Algorithm is used to optimize the results. Using the proposed method reduces the computation time and also the generation cost. The simulation has been done in the MATLAB software environment, and as its results compared with some other intelligent algorithms including Lagrange Relaxation (LR), Integer-Coded Genetic Algorithm (GA) and Bacterial Foraging (BF) for improvements in generation cost and problem solving time is shown. This method has the ability to develop solving unit commitment in different dimensions with considering constraints and limits.