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Showing 7 results for Scheduling
Generation scheduling in a competitive environment
H. Monsef, N.t. Mohamadi,
Volume 1, Issue 2 (4-2005)
Abstract
Electric power restructuring offers a major change to the vertically integrated monopoly. The change manifests the main part of engineers’ efforts to reshape the three components of today’s vertically integrated monopoly: generation, distribution and transmission. In a restructured environment, the main tasks of these three components will remain the same as before, however, to comply with FERC orders, new types of unbundling, coordination and rules are to be established to guarantee competition and non-discriminatory open access to all users. This paper provides the generation schedule of a GENCO in a deregulated power system. It is shown that the goal of generation schedule in the new structure is different from the traditional centralized power systems. The modeling of generation scheduling problem in a competitive environment is demonstrated by taking into account the main purposes of GENCOs which are selling electricity as much as possible and making higher profit. The GENCOs of an area are introduced via a model whose objective function consists of hourly spot market price as income and different kinds of costs. The constraints are the general ones of such a problem e.g. minimum up/down time, minimum and maximum generation and ramp rate. Using one of the classical optimization methods, the hourly generation schedule of the generating units will be obtained in this competitive environment. The results of this section will be used by ISO. The ISO will finalize the schedules of GENCOs by taking into account the technical considerations like the power flow of transmission lines. The model and the optimization methods are implemented on IEEE-RTS benchmark with 24 buses and 32 generating units.
Static Security Constrained Generation Scheduling Using Sensitivity Characteristics of Neural Network
M. R. Aghamohammadi,
Volume 4, Issue 3 (10-2008)
Abstract

This paper proposes a novel approach for generation scheduling using sensitivity

characteristic of a Security Analyzer Neural Network (SANN) for improving static security

of power system. In this paper, the potential overloading at the post contingency steadystate

associated with each line outage is proposed as a security index which is used for

evaluation and enhancement of system static security. A multilayer feed forward neural

network is trained as SANN for both evaluation and enhancement of system security. The

input of SANN is load/generation pattern. By using sensitivity characteristic of SANN,

sensitivity of security indices with respect to generation pattern is used as a guide line for

generation rescheduling aimed to enhance security. Economic characteristic of generation

pattern is also considered in the process of rescheduling to find an optimum generation

pattern satisfying both security and economic aspects of power system. One interesting

feature of the proposed approach is its ability for flexible handling of system security into

generation rescheduling and compromising with the economic feature with any degree of

coordination. By using SANN, several generation patterns with different level of security

and cost could be evaluated which constitute the Pareto solution of the multi-objective

problem. A compromised generation pattern could be found from Pareto solution with any

degree of coordination between security and cost. The effectiveness of the proposed

approach is studied on the IEEE 30 bus system with promising results.


An Integrated and Optimal Joint Scheduling of Energy Resources to Feed Electrical, Thermal and Potable Water Demands in Remote Area
R. Ghaffarpour, B. Mozafari, A. Ranjbar,
Volume 12, Issue 4 (12-2016)
Abstract

The continuous spread of distributed energy resources (DERs) such as combined heating and power (CHP), diesel generators, boilers and renewable energy sources provide an effective solution to energy related problems to serve the power and heat demands with minimum cost. Moreover, the DERs may play a significant role for supplying power and heat in rural areas, where grid electricity is not available. Also, some dry areas may face water scarcity and salinity problems. So, one important solution is the use of DERs to drive desalination units in order to solve water scarcity and salinity problems. In this study, the optimal scheduling of DERs and reverse osmosis (RO) desalination unit that feed the required electric, thermal and potable water demands are determined. The present paper describes the operation constraints and cost function of components of the system in detail. Operation constraints of generation units as well as feasible region of operation CHP in dual dependency characteristic are taken into account. To confirm the performance of the proposed model the approach is tested on a realistic remote area over a 24-h period. The results show that the economical scheduling of DERs and desalination units can be obtained using proposed methodology by implementing the proposed formulation.


A Bilevel Scheduling Approach for Modeling Energy Transaction of Virtual Power Plants in Distribution Networks
F. Nazari, A. Zangeneh, A. Shayegan-Rad,
Volume 13, Issue 1 (3-2017)
Abstract

By increasing the use of distributed generation (DG) in the distribution network operation, an entity called virtual power plant (VPP) has been introduced to control, dispatch and aggregate the generation of DGs, enabling them to participate either in the electricity market or the distribution network operation. The participation of VPPs in the electricity market has made challenges to fairly allocate payments and benefits between VPPs and distribution network operator (DNO). This paper presents a bilevel scheduling approach to model the energy transaction between VPPs and DNO.  The upper level corresponds to the decision making of VPPs which bid their long- term contract prices so that their own profits are maximized and the lower level represents the DNO decision making to supply electricity demand of the network by minimizing its overall cost. The proposed bilevel scheduling approach is transformed to a single level optimizing problem using its Karush-Kuhn-Tucker (KKT) optimality conditions. Several scenarios are applied to scrutinize the effectiveness and usefulness of the proposed model. 


Stochastic Joint Optimal Distributed Generation Scheduling and Distribution Feeder Reconfiguration of Microgrids Considering Uncertainties Modeled by Copula-Based Method
M. Khajevand, A. Fakharian, M. Sedighizadeh,
Volume 16, Issue 3 (9-2020)
Abstract
Using distributed generations (DGs) with optimal scheduling and optimal distribution feeder reconfiguration (DFR) are two aspects that can improve efficiency as well as technical and economic features of microgrids (MGs). This work presents a stochastic copula scenario-based framework to jointly carry out optimal scheduling of DGs and DFR. This framework takes into account non-dispatchable and dispatchable DGs. In this paper, the dispatchable DG is a fuel cell unit and the non-dispatchable DGs with stochastic generation are wind turbines and photovoltaic cells. The uncertainties of wind turbine and photovoltaic generations, as well as electrical demand, are formulated by a copula-based method. The generation of scenarios is carried out by the scenario tree method and representative scenarios are nominated with scenario reduction techniques. To obtain a weighted solution among the various solutions made by several scenarios, the average stochastic output (ASO) index is used.  The objective functions are minimization of the operational cost of the MG, minimization of active power loss, maximization of voltage stability index, and minimization of emissions. The best-compromised solution is then chosen by using the fuzzy technique. The capability of the proposed model is investigated on a 33-bus MG. The simulation results show the efficiency of the proposed model to optimize objective functions, while the constraints are satisfied.

Design and Implementation of a High-Precision Position Controller for Permanent Magnet Synchronous Motor Based on a New Gain Scheduling Approach
S. H. Montazeri, A. Damaki Aliabad, F. Zare, S. Aghaei,
Volume 17, Issue 1 (3-2021)
Abstract
The direct drive permanent magnet synchronous motor (DD-PMSM) is a suitable choice for high-precision position control applications. Among various control methods of this motor, the vector control approaches especially the field oriented control has a high-performance in the industrial drives. In this method, the components of stator current are controlled independently and as a result, the torque and flux are controlled continuously. Since there are some limitations and constraints in the motor, inverter, and control system, a new anti-windup gain scheduling PID controller based on the adaptive control principles is proposed for the position control loop. In the proposed method, different values are assigned to coefficients of the PID controller according to the position error to achieve high precision. Also, a very high-accuracy encoder and an ARM processor are used for measuring the instantaneous position and implementation of the proposed method, respectively. The simulation and experimental results validate the effectiveness, high accuracy, and good dynamic behavior of the proposed control method.

An Information Gap Decision Theory Approach for Optimal Bidding of a Virtual Power Plant under System Uncertainties
A. Ghanuni, R. Sharifi, H. Feshki Farahani,
Volume 19, Issue 3 (9-2023)
Abstract
Operation scheduling of a Virtual Power Plant (VPP) includes several challenges for the system according to the uncertain parameters, and security requirements, which intensify the need for more efficient models for energy scheduling and power trading strategies. Making suitable decisions under uncertainties, related to Renewable Energy Resources (RES), loads, and market prices impose extra considerations for the problem to make a clearer insight for the system operators to participate in local markets. This paper proposes a new risk-based hybrid stochastic model to investigate the effects of wind turbine power fluctuations on profit function, energy scheduling, and market participating strategies. Also, an incentivized Demand Response Program (DRP) is used, to enhance the system’s efficiency. The results of the study indicate that the proposed model based on Information Gap Decision Theory (IGDT) approach makes a clearer environment for the decision-maker to be aware of the effects of risk-taking or a risk-averse strategy on financial profits. The results show that a 30% of robustness and opportunity consideration would change the profit function from -12.5% up to 14.5%, respectively. A modified IEEE 33 bus test system is used to simulate a technical VPP considering the voltage stability and thermal capacity of line requirements.

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