Distribution Systems
R. Afshan; J. Salehi
Abstract
This paper proposes a novel hybrid Monte Carlo simulation-genetic approach (MCS-GA) for optimal operation of a distribution network considering renewable energy generation systems (REGSs) and battery energy storage systems (BESSs). The aim of this paper is to design an optimal charging /discharging scheduling ...
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This paper proposes a novel hybrid Monte Carlo simulation-genetic approach (MCS-GA) for optimal operation of a distribution network considering renewable energy generation systems (REGSs) and battery energy storage systems (BESSs). The aim of this paper is to design an optimal charging /discharging scheduling of BESSs so that the total daily profit of distribution company (Disco) can be maximized. In this study, the power generation of REGSs such as photovoltaic resources (PVs) and the network electricity prices are studied through their uncertainty natures. The probability distribution function (PDF), is used to account for uncertainties in this paper. Also, the Monte Carlo simulation (MCS) is applied to generate different scenarios of network electricity prices and solar irradiation of PVs. Optimal scheduling of BESSs can be performed by genetic algorithm (GA). In this paper, firstly, the charging and discharging state of BESSs (positive or negative sign of battery power) is determined according to the variable amount of the electricity prices and power produced from PVs, which have been obtained from the Monte Carlo simulation. Then by using the GA, optimal amount of BESSs is determined. Therefore, a hybrid MCS-GA is used to solve this problem. Numerical examples are presented to illustrate the optimal charging/discharging power of the battery for maximizing the total daily profit.
A. Rastgou; J. Moshtagh; S. Bahramara
Abstract
In this paper, power distribution planning (PDP) considering distributed generators (DGs) is investigated as a dynamic multi-objective optimization problem. Moreover, Monte Carlo simulation (MCS) is applied to handle the uncertainty in electricity price and load demand. In the proposed model, investment ...
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In this paper, power distribution planning (PDP) considering distributed generators (DGs) is investigated as a dynamic multi-objective optimization problem. Moreover, Monte Carlo simulation (MCS) is applied to handle the uncertainty in electricity price and load demand. In the proposed model, investment and operation costs, losses and purchased power from the main grid are incorporated in the first objective function, while pollution emission due to DGs and the grid is considered in the second objective function. One of the important advantages of the proposed objective function is a feeder and substation expansion in addition to an optimal placement of DGs. The resulted model is a mixed-integer non-linear one, which is solved using a non-dominated sorting improved harmony search algorithm (NSIHSA). As multi-objective optimization problems do not have a unique solution, to obtain the final optimum solution, fuzzy decision making analysis tagged with planner criteria is applied. To show the effectiveness of the proposed model and its solution, it is applied to a 9-node distribution system.
Dynamics
M. Sadeghi; M. Kalantar
Volume 4, Issue 1 , June 2016, , Pages 1-15
Abstract
This study presents a dynamic way in a DG planning problem instead of the last static or pseudo-dynamic planning point of views. A new way in modeling the DG units’ output power and the load uncertainties based on the probability rules is proposed in this paper. A sensitivity analysis on the stochastic ...
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This study presents a dynamic way in a DG planning problem instead of the last static or pseudo-dynamic planning point of views. A new way in modeling the DG units’ output power and the load uncertainties based on the probability rules is proposed in this paper. A sensitivity analysis on the stochastic nature of the electricity price and global fuel price is carried out through a proposed model. Six types of clean and conventional DG units are included in the planning process. The presented dynamic planning problem is solved considering encouraging and punishment functions. The imperialist competitive algorithm (ICA) as a strong evolutionary strategy is employed to solve the DG planning problem. The proposed models and the proposed problem are applied on the 9-bus and 33-bus test distribution systems. The results show a significant improvement in the total revenue of the distribution system in all of the defined scenarios.
Power System Operation
S.M. Mohseni-Bonab; A. Rabiee; S. Jalilzadeh; B. Mohammadi-Ivatloo; S. Nojavan
Volume 3, Issue 1 , June 2015, , Pages 83-93
Abstract
Optimal Reactive Power Dispatch (ORPD) is a multi-variable problem with nonlinear constraints and continuous/discrete decision variables. Due to the stochastic behavior of loads, the ORPD requires a probabilistic mathematical model. In this paper, Monte Carlo Simulation (MCS) is used for modeling of ...
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Optimal Reactive Power Dispatch (ORPD) is a multi-variable problem with nonlinear constraints and continuous/discrete decision variables. Due to the stochastic behavior of loads, the ORPD requires a probabilistic mathematical model. In this paper, Monte Carlo Simulation (MCS) is used for modeling of load uncertainties in the ORPD problem. The problem is formulated as a nonlinear constrained multi objective (MO) optimization problem considering two objectives, i.e., minimization of active power losses and voltage deviations from the corresponding desired values, subject to full AC load flow constraints and operational limits. The control variables utilized in the proposed MO-ORPD problem are generator bus voltages, transformers’ tap ratios and shunt reactive power compensation at the weak buses. The proposed probabilistic MO-ORPD problem is implemented on the IEEE 30-bus and IEEE 118-bus tests systems. The obtained numerical results substantiate the effectiveness and applicability of the proposed probabilistic MO-ORPD problem.
K. Afshar; A. Shokri Gazafroudi
Volume 1, Issue 2 , November 2013, , Pages 96-109
Abstract
Wind power generation is variable and uncertain. In the power systems with high penetration of wind power, determination of equivalent operating reserve is the main concern of systems operator. In this paper, a model is proposed to determine operating reserves in simultaneous market clearing of energy ...
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Wind power generation is variable and uncertain. In the power systems with high penetration of wind power, determination of equivalent operating reserve is the main concern of systems operator. In this paper, a model is proposed to determine operating reserves in simultaneous market clearing of energy and reserve by stochastic programming based on scenarios generated via Monte Carlo simulation (MCS). This model considers the wind power, load and network uncertainties and includes the cost of involuntary load shedding and wind spillage. The proposed methodology is examined on an example and a case study to investigate various effects of wind power generation on the system operating reserves and costs.
