Abstract
This paper proposes a two-phase approach for optimal short-term operational scheduling with intermittent renewable energy resources (RES) in an active distribution system. The first phase determines the amounts of purchased power from the market and the unit status of distributed generation (DG) and feeds the data into the second phase, a real-time scheduling coordination with hourly network reconfiguration. The two-phase proposed approach is applied to a case study of a sixteen-bus test system that uses synthetic data from renewable power generators and forecasts local user demands with a sampling time of five minutes.

Abstract
This paper discusses static transmission expansion planning (STEP) in terms of minimizing the costs of investment and operations. We propose a transmission expansion model that divides into investment and operations problems. We use a binary particle swarm optimization algorithm (BPSO) to solve the investment problem and a DC optimal power flow (DCOPF) to solve the operations problem. We model uncertainty as stochastic demand at each node. A simulated case study numerically evaluates the efficiency of the proposed method. © 2013 IEEE.

Abstract
This study presents a multi-objective framework to evaluate the integration of distant wind farms with associated transmission network upgrades on optimal power system planning. The presented approach also extends the technique to include the consideration of energy limitations associated with the installed hydro generation facilities. This study attempts to emphasise on the reliability implications rather than the production cost evaluation aspects. The decision making is based on hierarchal level II (HL-II) Expected Energy Not Served as an entire power system reliability assurance, and capital cost plus annual operational cost as an economical index. Non-dominated Sorting Genetic Algorithm is adopted to achieve the Pareto front of the aforementioned multi-objective problem. A fuzzy satisfying method, designated as the distance metric, is used to represents a trade-off between different objectives. To numerically evaluate the efficiency of the proposed method, simulation results on three case studies are provided. In spite of huge computation burden at HL-II reliability assessment, the results indicate high efficiency of the proposed method. © The Institution of Engineering and Technology 2013.

Abstract
This paper attempts to propose a fair solution in generation scheduling problem in the presence of inherent uncertainties in short-term power system operation. The proposed methodology incorporates probabilistic methodology in the uncertainties representation section, while harmony search algorithm is adopted as a fast and reliable soft computing algorithm to solve the proposed nonlinear, non-convex, large-scaled and combinatorial problem. As an indispensable step towards a more economical power system operation, the optimal generation scheduling strategy in the presence of mixed hydro-thermal generation mix, deemed to be the most techno-economically efficient scheme, comes to the play and is profoundly taken under concentration in this study. This paper devises a comprehensive hybrid optimisation approach by which all the crucial aspects of great influence in the generation scheduling process can be accounted for. Two-point estimation method is also adopted probabilistically approaching the involved uncertain criteria. In the light of the proposed methodology being implemented on an adopted test system, the anticipated efficiency of the proposed method is well verified. © 2014 Taylor & Francis.

Abstract
This paper presents a new adaptive scheme for doubly fed induction generators (DFIGs) in order to augment their flexibility confronting with unpredicted operational conditions. Due to large fluctuations in the wind velocity, the proposed scheme would handle system unreliable operational conditions. In such system, which has multi-input, multi-output and is also represented as a nonlinear control system, the uncertain parameters would affect the operational conditions. So, in order to have a robust controlling scheme, the mentioned characteristics should be considered in the proposed method. The adaptive control scheme proposed in this paper satisfies the expected constraints and could also be implemented in real-world platforms, especially in large-scale wind farms with DFIG turbines. © 2014 Taylor & Francis.

Abstract
In this paper, the problem of optimal economic scheduling of multi-reservoir cascaded hydrothermal units is investigated in the presence of an individual pumped-storage generating unit in the network. The proposed problem is modeled in a multi-objective framework comprising two objective functions. The goal of the first objective function is to minimize the operation costs and the second one is set to minimize the emissions caused by the thermal units while all the technical constraints are satisfied. Furthermore, the valve loading effect is included in the first objective function as a sinusoidal function. The problem is modeled and solved as a Mixed Integer Non-Linear Programming (MINLP). The augmented É"-constraint technique and lexicographic optimization are employed to solve the problem. Numerical results obtained from implementing the model on a case study are discussed. Also, the decision making procedure has been done using a fuzzy satisfying method to select the most preferred solution among the Pareto solutions derived through solving the multi-objective problem.