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Penetration of wind generation into power systems in recent years has greatly affected optimal power flow (OPF) because of the uncertain behavior of this new energy resource. In this research work, at first, a novel scenario generation approach is proposed to model wind power (WP) uncertainty. The proposed scenario generation approach includes construction of probability density function (PDF) pertaining to WP forecast error, segmentation of the PDF by an efficient clustering approach to obtain both the optimal number and the optimal arrangement of the clusters, and the generation of WP scenarios using the optimized clusters through roulette wheel mechanism. Secondly, this paper presents a new OPF framework based on DC network modeling for wind generation integrated power systems. Thirdly, a new out-of-sample analysis is presented to evaluate the long-run performance of the proposed OPF approach encountering various realizations of uncertain WPs. Finally, the performance of the proposed method for solving WP-integrated OPF problem is extensively illustrated on the IEEE 30-bus and the IEEE 118-bus test systems and compared with the performance of the deterministic method and the Weibull PDF method. These comparisons illustrate better performance of the proposed method, while it has reasonable computation times. [ABSTRACT FROM AUTHOR]

This paper presents a novel model for two-stage adaptive robust transmission expansion planning (AR-TEP) problem considering the uncertainty of future load demand and future wind power production. Linear decision rules (LDRs) are utilized to reformulate the AR-TEP model as a tractable mixed-integer linear programming problem, which can be directly solved by off-the-shelf optimization packages. The proposed LDR-based AR-TEP model is illustrated using the Garver 6-bus test system, the IEEE 24-bus reliability test system, and a 236-bus test system under different conditions and compared with a decomposition-based AR-TEP model that uses primal cutting planes. Also, the robustness of the expansion plans obtained from these two AR-TEP models is evaluated by carrying out an ex-post out-of-sample analysis for different values of the budget of uncertainty. Simulation results demonstrate the effectiveness of the proposed LDR-based AR-TEP model. [ABSTRACT FROM PUBLISHER]

This paper presents an adaptive robust network-constrained AC unit commitment (AC-UC) model. This model is formulated as a min–max–min optimization problem, which allows representing the uncertain nature of wind power productions in terms of bounded intervals. A tri-level decomposition algorithm using primal and dual cutting planes is introduced to solve this AC-UC problem and to find a robust commitment schedule withstanding the worst realizations of uncertain wind power. To reduce the computation burden and to obtain a tractable formulation, the AC power flow equations are convexified using a Signomial transformation. The proposed AC-UC model is illustrated using the IEEE 24-bus reliability test system. Simulation results demonstrate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a tri-level reliability-constrained robust power system expansion planning (SEP) framework modeling the uncertainty of electricity demand, wind power generation, and availability of units and lines. In the proposed tri-level SEP model, electricity demand and wind power generation being continuous uncertain variables are modeled by bounded intervals and availability of units and lines being discrete uncertain variables are modeled by probability distributions. Simulation results on the Garver 6-bus, the IEEE 24-bus, and the IEEE 73-bus test systems confirm the effectiveness of the proposed tri-level framework to cope with multi-fold planning uncertainties. [ABSTRACT FROM AUTHOR]

This paper presents a new uncertainty handling framework for optimal generation expansion planning (GEP) amalgamating the notions of single-stage and two-stage robust optimization (RO). The proposed multiyear robust GEP methodology, as a tractable mixed integer linear programming optimization problem, copes with the inherent planning uncertainties associated with forecasted electricity load demand, as well as estimated investment and operation costs through distribution-free bounded intervals producing polyhedral uncertainty sets. The optimal generation expansion plan obtained from the proposed RO approach is immunized against worst-case planning uncertainties considering the adopted degree of conservatism for each uncertainty set. Therefore, the proposed methodology is capable of controlling the robustness of the optimal investment schedule regarding the enforced planning uncertainties. Simulation results demonstrate the efficacy and efficiency of the proposed RO framework throughout GEP studies. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a new security-constrained self-scheduling framework incorporating the transmission flow limits in both steady state conditions and post-contingent states is presented to produce efficient bidding strategy for generation companies (GENCOs) in day-ahead electricity markets. Moreover, the proposed framework takes into account the uncertainty of the predicted market prices and models the risk and profit tradeoff of a GENCO based on an efficient multi-objective model. Furthermore, unit commitment and inter-temporal constraints of generators are considered in the suggested model converting it to a mixed-integer programming (MIP) optimization problem. Sensitivity of the proposed framework with respect to both the level of the market prices and adopted risk level is also evaluated in the paper. Simulation results are presented on the IEEE 30-bus and IEEE 118-bus test systems illustrating the performance of the proposed self-scheduling model. [Copyright &y& Elsevier]

Abstract: Congestion management is an important part of power system operation in today deregulated electricity markets. However, congestion management is traditionally performed based on static analysis tools, while these tools may not correctly capture dynamic voltage stability limits of a power system. In this paper, a new congestion management framework considering dynamic voltage stability boundary of power system is proposed. For this purpose, precise dynamic modeling of power system equipment, including generators and loads, is incorporated into the proposed congestion management framework. The proposed method alleviates congestion with a lower congestion management cost and more dynamic voltage stability margin, resulting in a more robust power system, compared with the previous congestion management methods. The validity of proposed congestion management framework is studied based on the New England 39-bus power system. The obtained results confirm the validity of the developed approach. [Copyright &y& Elsevier]

Abstract: Frequency stability is a crucial consideration in power system operation and planning, particularly as a consequence of recent increase in load demand. There is an urgent need in each power system to consider load–generation balance to prevent system from breaking up into islands. In this paper, a system frequency response model has been presented and a new method has been offered to analytically calculate the border of frequency stability of power system. It can be realized, whether system is stable from frequency viewpoint or not. Moreover, the threshold of UFLS (under frequency load shedding) relays can more effectively be determined, compared to the previous works in the area. The proposed method is examined on a four unit test system. Obtained results confirm the validity of the developed approach. [Copyright &y& Elsevier]

Abstract: This paper proposes a novel Adaptive Real Coded Genetic Algorithm (ARCGA) to solve the nonconvex and nonsmooth economic dispatch (ED) problem considering valve loading effects and multiple fuel source options. Considering valve effects and multiple fuel options change ED into nonlinear, nonconvex and nonsmooth optimization problem with multiple minima. These characteristics challenge analytical and heuristic methods in finding optimal solution in reasonable time. The proposed ARCGA technique is composed of new genetic operators including arithmetic-average-bound crossover (AABX) and B-Spline wavelet mutation (BWM). Moreover, to enhance the computational efficiency of the suggested solution method, an adaptation process is also included in the ARCGA. To show the superiority of the ARCGA, it is compared with several most recently published methods proposed to solve the ED problem. [Copyright &y& Elsevier]

Abstract: The fundamental requirement of power system hydrothermal scheduling is to determine the optimal amount of generated powers for the hydro and thermal units of the system in the scheduling horizon of 1 day or few days while satisfying the constraints of the hydroelectric system, thermal plants and electrical power system. Daily Hydrothermal Generation Scheduling (DHGS) is a complicated non-linear, non-convex and non-smooth optimization problem with discontinuous solution space. To deal with this complicated problem, a new Modified Adaptive Particle Swarm Optimization (MAPSO) is proposed in this paper. The inertia weight and acceleration coefficients of the PSO are adaptively changed in the MAPSO owning tree topology. We split-up the cognitive behavior of PSO into the best and not-best parts. The proposed not-best cognitive component, unlike recent methods, retains its dynamic behavior throughout the search process. Personal best position exchanging method is proposed to increase activities of particles to explore broad space. New velocity limiter is also proposed in this paper to enhance exploration capability and convergence behavior of the MAPSO. The proposed MAPSO is tested on six test systems and compared with some recent research works in the area. [Copyright &y& Elsevier]

In this paper, a new hybrid intelligent system is proposed for transient stability prediction. This intelligent system is composed of a preprocessor, an array of neural networks (NN) and an interpreter. The preprocessor partitions the whole set of synchronous machines into subsets, each one including only two generators. Each subset is assigned to one NN, which extracts the input/output mapping function of that subset. Then, the interpreter combines the responses of the NNs in a voting procedure to determine the transient stability status of the power system. This mechanism can cover the probable errors of the NNs, increasing the accuracy of the final response of the hybrid intelligent system. In addition to the transient stability status, this intelligent system can determine tripped generators and islanded parts of the power system for unstable cases. The proposed method has been examined on the PSB4 and New England test systems. The obtained results indicate the efficiency of the hybrid intelligent system for transient stability prediction. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid method is proposed for short-term bus load forecasting of power systems. The method is composed of the forecast-aided state estimator (FASE) and the multilayer perceptron (MLP) neural network. The FASE forecasts hourly loads of each bus by means of its previous data. Then the inputs and outputs of the FASE are fed to the MLP neural net- work. In other words, the MLP is trained to extract the mapping function between the inputs and outputs of the FASE (as input features) and real loads as output features. The proposed hybrid method has been examined on a real power system, a part of Iran's power network. The obtained results, discussed comprehensively, show that the hybrid method has better prediction accuracy than the other methods, such as MLP, FASE, and the periodic auto-regression (PAR) model. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a stochastic multiobjective model for generation maintenance scheduling (GMS) problem and a solution method to solve it. The proposed model properly considers both competing objectives and uncertainty sources of GMS problem. Three competing objective functions including total cost, risk measure, and total emission are simultaneously minimized in the proposed model. Moreover, forced outages of generating units throughout the GMS horizon are characterized by externally generated scenarios. Stochastic programming as an efficient approach to model uncertainty sources has been used in the proposed stochastic multiobjective GMS. Augmented normalized normal constraint (A‐NNC) method is developed as an efficient multiobjective mathematical programming approach to obtain Pareto optimal solutions for the proposed model. Further, a stochastic decision maker based on out‐of‐sample analysis is suggested to find the most preferred solution for GMS problem. The IEEE 118‐bus test system is used to investigate the effectiveness of the proposed model and solution method. [ABSTRACT FROM AUTHOR]