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[en] Highlights: • Design based on a set of performance and robustness criteria can increase profit. • Time delay as uncertainty in smart grid decrease profit. • Design method applied in this paper deal with large structure of controllers. • INSGA-II optimization and FDM method is applied to specify the best solution. - Abstract: Expansion of power systems is accompanied by innovations in smart grid solutions to power system operation and control. Profit enhancement by power oscillation damping controllers (PODC) and acceleration based power system stabilizer (PSS), model PSS2B, designed by the idea of pseudo-spectra based on multi-objective optimization is presented. The contribution of multi-objective functions in respect of performance and robustness criteria in stability enhancement is evaluated by considering the control actions of PODC and PSS2B as an ancillary service (AS). The robustness requirement is achieved by using the idea of pseudo-spectra to handle the changes of power system parameters and time delay introduced by processing of remote signals in the wide-area supplementary damping controller (WASDC). The weighted sum of six objective functions as performance and robustness criteria is selected as low-frequency oscillation damping index (LFODI). Two scenarios for the evaluation of small signal stability as an AS provided by PODC and PSS2B are considered. A multi-objective optimization approach based on LFODI, generation costs is formulated and improved non-dominated sorting genetic algorithm-II (INSGA-II) is employed to solve this problem. Fuzzy decision making (FDM) is used to find the best compromise solution from the set of Pareto-solution obtained by INSGA-II. Comparative analysis of the results of the conventional method and the proposed design method is presented by case study on a modified 2-area 4-machine power system
[en] Highlights: • This paper presents a developed multi objective CIABC based on CLS theory for solving EED problem. • The EED problem is formulated as a non-convex multi objective optimization problem. • Considered three test systems to demonstrate its efficiency including practical constrains. • The significant improvement in the results comparing the reported literature. - Abstract: In this paper, a modified ABC based on chaos theory namely CIABC is comprehensively enhanced and effectively applied for solving a multi-objective EED problem to minimize three conflicting objective functions with non-smooth and non-convex generator fuel cost characteristics while satisfying the operation constraints. The proposed method uses a Chaotic Local Search (CLS) to enhance the self searching ability of the original ABC algorithm for finding feasible optimal solutions of the EED problem. Also, many linear and nonlinear constraints, such as generation limits, transmission line loss, security constraints and non-smooth cost functions are considered as dynamic operational constraints. Moreover, a method based on fuzzy set theory is employed to extract one of the Pareto-optimal solutions as the best compromise one. The proposed multi objective evolutionary method has been applied to the standard IEEE 30 bus six generators, fourteen generators and 40 thermal generating units, respectively, as small, medium and large test power system. The numerical results obtained with the proposed method based on tables and figures compared with other evolutionary algorithm of scientific literatures. The results regards that the proposed CIABC algorithm surpasses the other available methods in terms of computational efficiency and solution quality
[en] Highlights: • Modelling mathematical integration of the proposed central bidding strategy for microgrids. • Considering and modelling the intra-market for adjusting the energy imbalances. • Analyzing effect of uncertainty of demand response and imbalance prices in profit of MG components. - Abstract: Due to the uncertain nature and limited predictability of wind and PV generated power, these resources participating in most of electricity markets are subject to significant deviation penalties during market settlements. In order to balance the unpredicted wind and PV power variations, system operators need to schedule additional reserves. This paper presents the optimal integrated participation model of wind and PV energy including demand response, storage devices, and dispatchable distributed generations in microgrids or virtual microgrids to increase their revenues in the intra-market. This market is considered 3–7 h before the delivered time, so that the amount of the contracted energy could be updated to reduce the produced power deviation of microgrid. A stochastic programming approach is considered in the development of the proposed bidding strategies for microgrid producers and loads. The optimization model is characterized by making the analysis of several scenarios and simultaneously treating three kinds of uncertainty including wind and PV power, intra-market, and imbalance prices. In order to predict these uncertainty variables, a neuro-fuzzy based approach has been applied. Historic data are used to forecast future prices and wind and PV power production in the adjustment markets. Also, a probabilistic approach based on the error of forecasted and real historic data is considered for estimating the future IM and imbalance prices of wind and PV produced power. Further, a test case is applied to example the microgrid using the Spanish market rules during one week, month, and year period to illustrate the potential benefits of the proposed joint biding strategy. The simulations results, carried out by MATLAB/optimization toolbox
[en] Highlights: • Assessing the role and performance of SDC and PSS2B in deregulated power markets. • The profit allocation of WADC as an AS provider is involved in this work. • A dual-dimensional SDC scheme for UPFC is applied to damp the power system swings. • The high share of dual-dimensional SDC shows capability of it in enhancing security. - Abstract: The problem of profit allocation of Unified Power Flow Controller-Supplementary Damping Controller (UPFC-SDC) and accelerating power PSS model (PSS2B) is an important and update issue which has not been properly directed yet. The model of UPFC-SDC that has been used in this paper is a dual-dimensional controller that first dimension of control is resulted from local signals and the second dimension is covered by global signals as additional measuring data from appropriate remote network locations, where swings are well observable. Thus, in this paper the profit allocation of Wide Area Damping Controller (WADC) is also presented as an undefined problem in security subject of deregulated power system. Assuming control action by UPFC-SDC and WADC as an Ancillary Service (AS), the contribution of UPFC-SDC in stability enhancement is evaluated. It is important to appropriately choose a criterion to assess the performance of UPFC-SDC, so that a suitable allocation of profit can be achieved. The sum of deviations of damping ratios and real part of eigenvalues is selected as Oscillation Damping Criterion (ODC). Two scenarios for valuation of small signal stability as an AS provided by UPFC-SDC is considered. The first scenario without retuning of controllers and in the second scenario controllers is retuned due to response of the market situation. A multi-objective optimization approach based on ODC, generation costs and UPFC cost is considered and then Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is utilized for solving this problem. A two area four machine test power system is considered for investigation of results
[en] Highlights: • Presenting a hybrid CGSA-LSSVM scheme for price forecasting. • Considering uncertainties for filtering in input data and feature selection to improve efficiency. • Using DWT input featured LSSVM approach to classify next-week prices. • Used three real markets to illustrate performance of the proposed price forecasting model. - Abstract: At the present time, day-ahead electricity market is closely associated with other commodity markets such as fuel market and emission market. Under such an environment, day-ahead electricity price forecasting has become necessary for power producers and consumers in the current deregulated electricity markets. Seeking for more accurate price forecasting techniques, this paper proposes a new combination of a Feature Selection (FS) technique based mutual information (MI) technique and Wavelet Transform (WT) in this study. Moreover, in this paper a new modified version of Gravitational Search Algorithm (GSA) optimization based chaos theory, namely Chaotic Gravitational Search Algorithm (CGSA) is developed to find the optimal parameters of Least Square Support Vector Machine (LSSVM) to predict electricity prices. The performance and price forecast accuracy of the proposed technique is assessed by means of real data from Iran’s, Ontario’s and Spain’s price markets. The simulation results from numerical tables and figures in different cases show that the proposed technique increases electricity price market forecasting accuracy than the other classical and heretical methods in the scientific researches
[en] Transmission network expansion planning (TNEP) is a basic part of power system planning that determines where, when and how many new transmission lines should be added to the network. Up till now, various methods have been presented to solve the static transmission network expansion planning (STNEP) problem. But in all of these methods, lines adequacy rate has not been considered at the end of planning horizon, i.e. expanded network misses adequacy after some times and needs to be expanded again. In this paper, expansion planning has been implemented by merging lines loading parameter in the STNEP and inserting investment cost into the fitness function constraints using discrete particle swarm optimization (DPSO) algorithm. Expanded network will possess a maximum adequacy to provide load demand and also the transmission lines overloaded later. The proposed idea has been tested on the Garvers network and an actual transmission network of the Azerbaijan regional electric company, Iran, and the results are compared with the decimal codification genetic algorithm (DCGA) technique. The results evaluation shows that the network will possess maximum efficiency economically. Also, it is shown that precision and convergence speed of the proposed DPSO based method for the solution of the STNEP problem is superior to DCGA approach.
[en] Transmission network expansion planning (TNEP) is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. Its task is to minimize the network construction and operational cost, while meeting imposed technical, economic and reliability constraints. Up till now, various methods have been proposed for solution of the static transmission network expansion planning (STNEP) problem. But, in all of them, the effect of two important parameters i.e., inflation rate and load growth factor on network losses has not been investigated. Thus, in this paper, STNEP is being studied considering the effect of inflation rate and load growth factor on the network losses in a transmission network with different voltage levels using a decimal codification genetic algorithm (DCGA). The effectiveness of the proposed idea is tested on the Garver's six-bus network. The results evaluation reveals that the inflation rate and load growth factor have important effect on the network losses and subsequent network arrangement. In addition, considering the effect of two above-mentioned parameters (inflation rate and load growth factor) in expansion planning of transmission networks with various line voltage levels is caused that the total expansion cost of the network (expansion costs and the operational cost) is calculated more exactly and therefore the network satisfies the requirements of delivering electric power more safely and reliably to load centers
[en] The paper develops a new design procedure for simultaneous coordinated designing of the thyristor controlled series capacitor (TCSC) damping controller and power system stabilizer (PSS) in multi-machine power system. The coordinated design problem of PSS and TCSC damping controllers over a wide range of loading conditions is converted to an optimization problem with the time domain-based objective function that is solved by a particle swarm optimization (PSO) technique which has a strong ability to find the most optimistic results. By minimizing the proposed fitness function in which oscillatory characteristics between areas are included and thus the interactions among the TCSC controller and PSS under transient conditions in the multi-machine power system are improved. To ensure the robustness of the proposed stabilizers, the design process takes a wide range of operating conditions into account. The effectiveness of the proposed controller is demonstrated through the nonlinear time-domain simulation and some performance indices studies. The results of these studies show that the proposed coordinated controllers have an excellent capability in damping power system inter-area oscillations and enhance greatly the dynamic stability of the power system. Moreover, it is superior to both the uncoordinated designed stabilizers of the PSS and the TCSC damping controller.
[en] Transmission network expansion planning (TNEP) is an important component of power system planning. It determines the characteristic and performance of the future electric power network and influences the power system operation directly. Up till now, various methods have been presented for the solution of static TNEP (STNEP) problem. However, in all of them, the role of bundle lines in TNEP problem considering the expansion of substations from the voltage level point of view has not been investigated. Thus, in this paper, the role of bundle lines in STNEP problem is being studied considering expansion cost of substations from the voltage level point of view using decimal codification genetic algorithm (DCGA). The effectiveness of the proposed idea is tested on an actual transmission network of the Azerbaijan regional electric company, Iran. The results reveal that bundle lines have effective role in transmission expansion planning and subsequent determining the network arrangement. In addition, considering the bundle lines in a power system is caused that the expansion cost of lines and substations decreases and therefore the total expansion cost of network is minimized. Also, it can be said, although cost of bundle lines are more than those which have not bundle conductor, constructing this type of lines in a transmission network with different voltage levels prevents useless expansion of unbundled lines in separate corridors and therefore the network expansion planning is optimized.
[en] In this paper, a new approach by accomplishing dynamic transmission expansion planning (DTEP) problem, the optimum generation level of generators is determined for annual load peak using a genetic algorithm (GA) based quadratic programming (QP) method. This study is carried out in order to achieve a better prospect from the generation network and consequently the suitable planning for its future expansion. Another important aspect of this paper is taking the advantage of line outage distribution factors (LODFs) or sensitivity analysis for the evaluation of network reliability instead of the direct current load flow (DCLF), that the computations and performance of DTEP problem considerably speeds up in comparison with previous researches. Also, the applied coding for problem solution using GA is more flexible and is significantly improved the performance of the proposed method. The proposed method is successively applied to a realistic system of the 18-buses Azerbaijan regional electric network, which is located in the northwest of Iran, and the results are extensively analyzed. The results evaluation reveals that the generators with high capacity operated in full capacity almost the whole of the planning period, because the generation cost coefficients is decreased as the generator capacity increased and vice versa.