Now showing 1 - 6 of 6
  • Publication
    Nonconvex Dynamic Economic Power Dispatch Problems Solution Using Hybrid Immune-Genetic Algorithm
    (Institute of Electrical and Electronic Engineers (IEEE), 2013-05) ; ;
    The objective of dynamic economic dispatch (DED) problem is to determine the generation schedule of the committed generation units, which minimizes the total operating cost over a dispatch period, while satisfying a set of constraints. The effect of valve points and prohibited operating zones (POZs) in the generating units' cost functions makes the DED a highly nonlinear and nonconvex optimization problem with multiple local minima. Considering the ramp-rate limits and transmission losses makes the DED problem even more complicated. Hence, proposing an effective solution method for this optimization problem is of great interest. This paper presents a novel heuristic algorithm to solve DED problem of generating units by employing a hybrid immune-genetic algorithm. To illustrate the effectiveness of the proposed approach, four test systems that consist of different numbers of generating units are studied. The valve-point effects, POZs, and ramp-rate constraints along with transmission losses are also considered in simulation cases. The results obtained through the proposed method are compared with those reported in the literature. These results substantiate the applicability of the proposed method for solving the constrained DED problem with nonsmooth cost functions.
      1062Scopus© Citations 78
  • Publication
    Imperialist competitive algorithm for solving non-convex dynamic economic power dispatch
    Dynamic economic dispatch (DED) aims to schedule the committed generating units' output active power economically over a certain period of time, satisfying operating constraints and load demand in each interval. Valve-point effect, the ramp rate limits, prohibited operation zones (POZs), and transmission losses make the DED a complicated, non-linear constrained problem. Hence, in this paper, imperialist competitive algorithm (ICA) is proposed to solve such complicated problem. The feasibility of the proposed method is validated on five and ten units test system for a 24 h time interval. The results obtained by the ICA are compared with other techniques of the literature. These results substantiate the applicability of the proposed method for solving the constrained DED with non-smooth cost functions. Besides, to examine the applicability of the proposed ICA on large power systems, a test case with 54 units is studied. The results confirm the suitability of the ICA for large-scale DED problem.
      721Scopus© Citations 123
  • Publication
    Energy Hub Management with Intermittent Wind Power
    The optimal energy management in energy hubs has recently attracted a great deal of attention around the world. The energy hub consists of several inputs (energy resources) and outputs (energy consumptions) and also some energy conversion/storage devices. The energy hub can be a home, large consumer, power plant, etc. The objective is to minimize the energy procurement costs (fuel/electricity/environmental aspects) subject to a set of technical constraints. One of the popular options to be served as the input resource is renewable energy like wind or solar power. Using the renewable energy has various benefits such as low marginal costs and zero environmental pollution. On the other hand, the uncertainties associated with them make the operation of the energy hub a difficult and risky task. Besides, there are other resources of uncertainties such as the hourly electricity prices and demand values. Hence, it is important to determine an economic schedule for energy hubs, with an acceptable level of energy procurement risk. Thus, in this chapter a comprehensive multiobjective model is proposed to minimize both the energy procurement cost and risk level in energy hub. For controlling the pernicious effects of the uncertainties, conditional value at risk (CVaR) is used as risk management tool. The proposed model is formulated as a mixed integer nonlinear programming (MINLP) problem and solved using GAMS. Simulation results on an illustrative test system are carried out to demonstrate the applicability of the proposed method.
    Scopus© Citations 69  339
  • Publication
    Coordination of interdependent natural gas and electricity systems based on information gap decision theory
    (Institution of Engineering and Technology, 2019-08-06) ; ; ;
    The interactions of the natural gas (NG) network and the electricity system are increased by using gas-fired generation units, which use NG to produce electricity. There are various uncertainty sources such as the forced outage of generating units or market price fluctuations that affect the economic operation of both NG and electricity systems. This study focuses on the steady-state formulation of the integrated NG transmission grid and electricity system by considering the uncertainty of electricity market price based on information gap decision theory. The higher and lower costs than the expected cost originated from the fluctuations of electricity market price are modelled by the robustness and opportunity functions, respectively. The objective is to minimise the cost of zone one while satisfying the constraints of two interdependent systems, which can obtain revenue from selling power to its connected zones in short-term scheduling. The capability of the proposed method is demonstrated by applying it on a 20-node NG network and IEEE RTS 24-bus. The proposed short-term coordination between NG and electricity infrastructures is solved and discussed.
    Scopus© Citations 15  163
  • Publication
    Application of information gap decision theory in practical energy problems: A comprehensive review
    The uncertainty quantification and risk modeling are hot topics in operation and planning of energy systems. The system operators and planners are decision makers that need to handle the uncertainty of input data of their models. As an example, energy consumption has always been a critical problem for operators since the forecasted values, and the actual consumption is never expected to be the same. The penetration of renewable energy resources is continuously increasing in recent and upcoming years. These technologies are not dispatch-able and are highly dependent on natural resources. This would make real-time energy balancing more complicated. Another source of uncertainty is related to energy market prices which are determined by the market participants’ behaviors. To consider these issues, uncertainty modeling should be performed. Various approaches have been previously utilized to model the uncertainty of these parameters such as probabilistic approaches, possibilistic approaches, hybrid possibilistic-probabilistic approach, information gap decision theory, robust and interval optimization techniques. This paper reviews the research works that used information gap decision theory for uncertainty modeling in energy and power systems.
      218Scopus© Citations 86
  • Publication
    Corrective Voltage Control Scheme Considering Demand Response and Stochastic Wind Power
    (Institute of Electrical and Electronics Engineers, 2014-11) ; ; ;
    This paper proposes a new approach for corrective voltage control (CVC) of power systems in presence of uncertain wind power generation and demand values. The CVC framework deals with the condition that a power system encounters voltage instability as a result of severe contingencies. The uncertainty of wind power generation and demand values is handled using a scenario-based modeling approach. One of the features of the proposed methodology is to consider participation of demand-side resources as an effective control facility that reduces control costs. Active and reactive redispatch of generating units and involuntary load curtailment are employed along with the voluntary demand-side participation (demand response) as control facilities in the proposed CVC approach. The CVC is formulated as a multi-objective optimization problem. The objectives are ensuring a desired loading margin while minimizing the corresponding control cost. This problem is solved using $epsilon$-constraint method, and fuzzy satisfying approach is employed to select the best solution from the Pareto optimal set. The proposed control framework is implemented on the IEEE 118-bus system to demonstrate its applicability and effectiveness.
      980Scopus© Citations 142