Scheduling Of Thermal Units Research Articles

Renewable Energy Sources constrained Generation Scheduling of Thermal Units in a Regulated Power Industry

Renewable energy production technologies use resources directly from the environment to generate electric power. These energy sources such as sunshine, wind, tides and biomass are some of the more popular gifts from the nature. The renewable energy resources emit nothing into atmosphere or never produce green house gas or pollute the environment at all. Even if the renewable energy sources are safe to the atmosphere, these resources meet out smaller percent of power requirement internationally This paper a population based optimization algorithm of Corona Virus Herd Immunity Optimizer (CVHIO) is proposed to solve the renewable energy constrained generation scheduling problem. The objective function of the problem is to minimize the total operating cost of hybrid system with satisfying standard operating constrains of thermal, wind and solar power plants. Initially the variables of the problem are effectively optimized using CVHIO algorithm. . Numerical example with IEE 39 bus test system (ten thermal twenty four hour) with an equivalent Solar and Wind generators are considered to prove the performance of CVHIO approach.

Quantum Inspired Evolutionary Algorithm with a Novel Elitist Local Search Method for Scheduling of Thermal Units

The unit commitment problem is a complex and essential problem in the power generation field, which is solved to obtain the schedule of a large number of generating units to minimize the operating cost and the fulfillment of consumer load demand. The present work solves the unit commitment problem using quantum-inspired evolutionary algorithms with a novel elitist local search method (QIEA-ELS). The proposed algorithm solves the unit commitment problem efficiently and its applicability is verified on various unit test systems. The constraints are satisfied efficiently to find a feasible solution, the novel elitist search method is used to locally explore the search area around the fittest individual to find a better solution in its vicinity in genotype space represent by qubits. The solution of the unit commitment is carried out considering two small population sizes as suggested in earlier work by other authors using QIEA, though it can be extended using larger population size also. The computational time is also reduced by using the suggested method with a novel elitist local search (ELS) method. The results obtained after applying the proposed algorithm are found to better as compared to other well-known solution techniques.

Modeling of Electric Vehicles as Mobile Energy Storage Systems Considering Multiple Congestions

To realize the optimal operation of urban coupled transportation power systems underthe road, charging facilities, and transmission line congestions, a dynamic optimal traffic power flow (DOTPF) model was formulated under congestions. Based on the time space network (TSN) approach, a novel TSN with queues was proposed, considering the moving, parking, charging, and queueing state transitions. A vehicle routing problem was formulated for electric vehicles (EVs) and further incorporated into the dynamic traffic assignment problem (DTAP), reducing the traffic demand losses. With security and reserve constraints, a dynamic security-constrained carbon dioxide-oriented optimal power flow (OPF) problem was formulated to reduce the carbon emission and generation cost, by optimizing the scheduling of thermal units and energy storage systems. A multi-objective DOTPF problem was formulated, and further reformulated into a convex mixed-integer quadratic programming problem. The effectiveness of the proposed DOTPF was verified based on the simulation results on coupled IEEE-30 and Sioux Falls system.

Risk-Oriented Multi-Area Economic Dispatch Solution With High Penetration of Wind Power Generation and Compressed Air Energy Storage System

This paper investigates the risk-oriented multi-area economic dispatch (MAED) problem with high penetration of wind farms (WFs) combined with compressed air energy storage (CAES). The main objective is to help system operators to minimize the operational cost of thermal units and CAES units with an appropriate level of security through optimized WF power generation curtailment strategy and CAES charging/discharging control. In the obtained MAED model, several WFs integrated with CAES units are considered in different generation zones, and the probability to meet demand by available spinning reserve during N - 1 security contingency is characterized as a risk function. Furthermore, the contribution of CAES units in providing the system spinning reserve is taken into account in the MAED model. The proposed framework is demonstrated by a case study using the modified IEEE 40-generator system. The numerical results reveal that the proposed method brings a significant advantage to the efficient scheduling of thermal units' power generation, WF power curtailment, and CAES charging/discharging control in the power system.

Evolutionary Algorithm for Optimal Scheduling of Thermal Units in an Integrated Power System

Interconnection of power system is a key one to route power plant operation optimally with a diminishing rate of active power generation, in encountering the growing demand. Reduction of fuel consumption is one of the objective for the steam run power plants by assigning source power to every component (economic dispatch) focus to equal and unequal limitations optimally. This importance is due to power savings. ELD is the utmost significant issues to be resolved in the process and forecasting of a electrical system. This work presents an approach of Genetic Algorithm (GA) for finest load scheduling of steam run power plants to catch the universal optimum dispatch clarification. The genetic set of rules centered best approach explains the ELD problem. The core indication behind genetic set of rules is, all units should allocate the essential power load at lowest coal cost, though sustaining the coordination limitations. Simulation is executed on assessment system with six generators using Mat-lab. The results are shown for 24 hour loads with optimal generation of 6 generators including transmission losses and cost of real power generation.

Stochastic security-constrained operation of wind and hydrogen energy storage systems integrated with price-based demand response

Because of highly increasing energy consumption, environmental issues and lack of common energy sources, the use of renewable energy sources especially wind power generation technology is increasing with significant growth in the world. But due to the variable nature of these sources, new challenges have been created in the balance between production and consumption of power system. The hydrogen energy storage (HES) system by storing excess wind power through the technology of power to hydrogen (P2H) and delivering it to the electricity network through hydrogen-based gas turbine at the required hours reduces not only wind alternation but can play an important role in balancing power production and consumption. On the other hand, power consumers by participating in demand response (DR) programs can reduce their consumption at peak load or wind power shortage hours, and increase their consumption at low-load or excess wind power hours to reduce wind power spillage and system energy cost. This paper proposes a stochastic security constrained unit commitment (SCUC) with wind energy considering coordinated operation of price-based DR and HES system. Price-based DR has been formulated as a price responsive shiftable demand bidding mechanism. The proposed model has been tested on modified 6-bus and 24-bus systems. The numerical results show the effect of simultaneous consideration of HES system and price-based DR integrated with wind energy on hourly generation scheduling of thermal units. As a result there is some reduction in wind generation power spillage and daily operation cost.

Optimal size allocation of superconducting magnetic energy storage system based unit commitment

Abstract SMES has gained developments in recent years because of its high efficiency with no site limitation, and faster response to meet the peak load demands. By incorporating high efficient Superconducting magnetic energy storage systems (SMES) has a greater impact on daily load scheduling of thermal units and pave the way for optimal unit commitment to meet the load demands with reduced load shedding. In this paper, IEEE 10 unit thermal unit system is incorporated with and without SMES and the results are analysed for studying the impact of SMES in unit commitment scheduling. For this purpose effective Modified Lagrangian relaxation based Particle swarm optimization (LR-PSO) algorithm is used for calculating the operating cost and the results are compared with existing algorithm using MATLAB.

A New Binary Variant of Sine–Cosine Algorithm: Development and Application to Solve Profit-Based Unit Commitment Problem

The profit-based unit commitment (PBUC) procedure is a binary natured operational planning problem of generation company (GENCO) in competitive electricity market. The GENCO solves PBUC problem in day ahead market of energy and reserve markets by commitment and scheduling of thermal units with an objective of profit maximization for the set of given price and load forecasts. Therefore, the solution quality and efficacy of PBUC optimization problem play a vital role in ensuring maximum returns to the GENCO. This paper presents a mathematical model-inspired heuristic approach called binary sine–cosine algorithm (BSCA) for solving PBUC problem. The SCA algorithm is a heuristic approach that uses the fluctuating nature of individual candidates/search agents in the search space around the global solution. The fluctuating mechanism is realized by using mathematical functions, i.e. sine and cosine functions. The proposed binary approach of SCA algorithm uses modified sigmoidal transformation function for binary mapping of continuous real-valued search space to binary counterpart. The efficacy of proposed approach in terms of solution quality and convergence is demonstrated using test system with different market participation policies. The results demonstrate the effectiveness of proposed BSCA approach over existing approaches for solving PBUC.

Hybrid Model Predictive Control Framework for the Thermal Unit Commitment Problem including Start-up and Shutdown Power Trajectories

This paper presents a generalized mixed logical dynamical (MLD) approach for modelling thermal units. A self-scheduling problem is formulated for a thermal unit including an accurate model of its start-up and shutdown power trajectories. This optimal self-scheduling problem is solved in a Model Predictive Control (MPC) framework. The problem formulation considers all the relevant constraints associated with the scheduling of thermal units. The efficacy of the proposed MLD approach is demonstrated through simulation results. These results are extended to a system of 5 units and the optimal scheduling problem is formulated and solved.

Shuffled Frog Leaping Algorithm (SFLA) for Short Term Optimal Scheduling of Thermal Units with Emission Limitation and Prohibited Operational Zone (POZ) Constraints

Objectives: The objective of this paper is to decide the startup and shut down status of thermal generating units to meet the fluctuating load over a limited period at a lowest cost and also with lowest emission. Methods/Analysis: This paper presents Shuffled Frog Leaping Algorithm to explain short term Unit Commitment Problem solution with regard to Emission limitation and Prohibited Operating Zone (POZ) constraint. Findings: Fuel cost savings can be obtained by proper commitment of the available generating units. The total operating cost includes both the fuel cost and cost associated with the startup, shut down and maintenance of units. A variety of constraints like spinning reserve, generation limit constraint, minimum up time, minimum down time, system power balance and response rate constraints like ramp up constraint, ramp down constraint and prohibited operating zone constraints are considered for investigation. Novelty /Improvement: The problem is solved using an integer coded Shuffled Frog Leaping Algorithm which offers a practical unit commitment problem. A MATLAB code has been developed to explain the unit commitment problem using SFLA. The results are extensively validated for standard IEEE 39 bus with 10 units system. The results obtained are compared with existing method.

Ant Lion Optimizer based Approach for Optimal Scheduling of Thermal Units for Small Scale Electrical Economic Power Dispatch Problem

A novel nature inspired algorithm ant lion optimizer (ALO) is recently developed which is motivated from the hunting mechanism of ant lions .Inherit steps of hunting prey such as the random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building are simulated to find the optimal solution of real life problems . Intelligent and Optimization techniques based on evolutionary computing, metaheuristic,biological base,nature inspired, search method establish their applications in the area of electrical economic power dispatch planning(EEPDP) to reach global optimal solution for this multi scale, multi-decision, multi-objective combinatorial problem subjected to different constraints.An application of ALO to solve non linear electric economic power dispatch problem(EEPDP) is proposed in this paper. Efficient and optimal planning of economic electrical power dispatch problem is an integral part of economic electrical energy generation planning and it is the need of time for the electrical engineers to browse this area in multi-scale planning scenarios.. The performance of s ant lion optimizer (ALO) to solve electrical economic power dispatch problem is tested on three and six unit system.Test results are compared with other techniques grey wolf optimization(GWO),cuckoo search(CS),artificial bee colony(ABC),firefly algorithm(FA),particle swarm optimization(PSO),shuffled frog leap (SFL) ,bacteria foraging algorithm(BFO),harmony search(HS) applied in literature. Simulation results proved that the ALO technique is better as compared to other nature inspired,heuristic,metaheuristic techniques to find global minima and maintain the solution quality in terms of low fuel cost.

Optimal power scheduling of thermal units considering emission constraint for GENCOs’ profit maximization

In this paper, authors propose a novel method to determine an optimal solution for profit based unit commitment (PBUC) problem considering emission constraint, under a deregulated environment. In a restructured power system, generation companies (GENCOs) schedule their units with the aim of maximizing their own profit by relaxing demand fulfillment constraints without any regard to social benefits. In the new structure, due to strict reflection of power price in market data, this factor should be considered as an important ingredient in decision-making process. In this paper a social-political based optimization algorithm called imperialist competitive algorithm (ICA) in combination with a novel meta-heuristic constraint handling technique is proposed. This method utilizes operation features of PBUC problem and a penalty factor approach to solve an emission constrained PBUC problem in order to maximize GENCOs profit. Effectiveness of presented method for solving non-convex optimization problem of thermal generators scheduling in a day-ahead deregulated electricity market is validated using several test systems consisting 10, 40 and 100 generation units.

Thermal Unit Scheduling for CO2Reduction Including Wind Power and Electric Vehicles

In this paper, we present a method to determine unit commitment schedules, while considering CO2emissions and costs along with the frequency regulation capability of the units, in order tomitigate fluctuations in wind power. We developed an extended procedure that obtains a trade-off solution of cost versus CO2emissions, including a significant wind power penetration, and developed Plug-in Electric Vehicles (PEVs) as additional reserves. The proposed method was tested on a 10-unit, 24-hour model system using the estimated wind power curve derived from an actual wind farm. The results, such as shadow prices of CO2obtained using the trade-off analysis, may provide a basis of evaluating the equivalent cost of wind farms and PEVs, and their contributions to CO2reduction.

Refined augmented Lagrange Hopfield network-based Lagrange relaxation for hydrothermal scheduling

This paper proposes a refined augmented Lagrange Hopfield network-based Lagrange relaxation (ALHN-LR) for solving short term hydrothermal scheduling (HTS) problem with pumped-storage hydro units. ALHN-LR consists of improved Lagrange relaxation (ILR) and augmented Lagrange Hopfield network (ALHN). For solving the HTS problem, the proposed method applies enhanced ILR for finding thermal unit scheduling, ALHN for solving constrained economic dispatch (CED), and heuristic search-based algorithms in earlier papers for committing hydro and pumped-storage units, repairing ramp rate, emission, and transmission constraint violations, and refining the obtained result. The proposed ALHN which is a continuous Hopfield network with its energy function based on augmented Lagrangian function can properly handle both equality and inequality constraints in CED problem. The proposed ALHN-LR is tested on a hydrothermal system with 17 thermal, two hydro and two pumped-storage units, and the IEEE 24-bus reliability test system over the 168-hour schedule time horizon. Test results indicate that the proposed method can obtain less total costs than those from augmented Hopfield network (AHN), hybrid enhanced Lagrangian relaxation and quadratic programming (hybrid LRQP), and augmented Lagrangian relaxation (ALR).

Power Generation Scheduling of Thermal Units Considering Gas Pipelines Constraints

Power Generation Scheduling of Thermal Units Considering Gas Pipelines Constraints

SOLUTION OF UNIT COMMITMENT USING MODIFIED GENETIC ALGORITHM CONSIDERING THE EFFECT OF UNIT OUTAGE UNCERTAINTY

This paper deals with the Genetic Algorithm (GA) based solution technique for generation scheduling of thermal units considering the effect of unit uncertainty on unit commitment solution. Because of the random failures of generating units, there is uncertainty in unit availability and therefore the unit commitments as well as economic dispatch problems are stochastic in nature. GA based proposed technique focuses on finding a feasible status of units for 24 h in order to reduce the risk of unavailability of the committed units and simultaneously satisfy several constraints like ramp rate, minimum up and down time etc. This paper shows a comparison between classical deterministic approach and stochastic approach for unit commitment problem. The results obtained validate the proposed technique by ensuring quality power in terms of reliability as well as enhancing the searching ability of simple GA.

Short‐term scheduling of thermal units: emission constraints and trade‐off curves

AbstractThis paper provides an approach to short‐term scheduling of thermal units, designed to simultaneously address the economic issue of the fuel cost incurred on the commitment of the units and the environmental consideration due to emission allowance trading. The simultaneous address of the fuel cost with the emission is modeled by a multi‐objective optimization problem, which is solved by a combination of the weighted sum method with the ε‐constraining method. A numerical example for different values of a scaling factor is considered in order to obtain the non‐dominated solutions of the trade‐off curve between fuel cost and emission. Our approach presents a new parameter, ratio of change, and the corresponding gradient angle, to enable the proper selection of a compromise commitment for the units. Copyright © 2007 John Wiley & Sons, Ltd.

An ant colony system approach for unit commitment problem

Ant colony system (ACS) model is more suitable for solving combinatorial optimization problem, so ACS has been applied to the hard combinatorial Unit commitment problem (UCP). Here, a parallel can be drawn of ants finding the shortest path from source (nest) to its destination (food) and solving UCP to obtain the minimum cost path (MCP) for scheduling of thermal units for the demand forecasted. Multi-stage decisions give ant search a competitive edge over other conventional approaches like dynamic programming (DP) and branch and bound (BB) integer programming techniques. Before the artificial ants starts finding the MCP, all possible combination of states satisfying the load demand with spinning reserve constraint are selected for complete scheduling period which is called as the ant search space (ASS). Then the artificial ants are allowed to explore the MCP in this search space. The proposed model has been demonstrated on a practical ten unit system and a brief study has been performed with respect to generation cost, solution time and parameter settings on a numerical example with four unit system.

Scheduling of thermal units with a nonlinear load flow network model

We describe a formulation of the thermal unit commitment problem that includes AC network constraints, which allows taking into account VAr production as a criterion for generator commitment for the first time. The formulation makes it possible to use the Lagrangian Relaxation technique as a solution algorithm even though the network constraints are not linear. Although the solution method is computationally intensive, the problem exhibits a separation structure and a price coordination schedule that suggest several ways of improving the performance. Preliminary test results are reported.

Short-term generation scheduling of thermal units with voltage security and environmental constraints

A dynamic and linear programming-based approach is investigated for the short-term scheduling of thermal power plant units considering the security and environmental constraints of electric power systems. In addition to the basic constraints (i.e. real power limits of the generators, real power demand, spinning reserve, minimum up and down lime of units) a comprehensive set of constraints is considered including: transmission line capacity limits; generators dynamic ramp rate; voltage security margin; and environmental issues. An optimal multiplier continuation method is adopted to effectively calculate the voltage security margin. The constraint relating to the voltage security margin is considered as a transition constraint in the dynamic programming process. The environmental issue is considered as a restriction on the hourly SO/sub 2/ emission allowances and other constraints are considered in the dynamic/linear programming procedures. Results of the application of the proposed algorithm to a test system are presented to illustrate the effectiveness of the approach.