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This paper proposes a bi-level optimization problem to integrated transmission operation model with energy management of autonomous distribution substations. In this approach, independent distribution substation entities, in the lower levels, operate their own networks and send decisions to the upper level transmission market operator that clears the day-ahead market based on security-constrained unit commitment considering the worst-contingency state of transmission units and branches, and DC power flow models. The lower level is a mixed integer linear programming model of the distribution substations with the aim of increasing their benefit, considering optimal charge and discharge of large-scale energy storage systems by controlling the number of charging/discharging actions and demand side management model by controlling the number of load shifting actions. The upper level of the proposed model is security-constrained unit commitment considering a multi-objective function. In the upper level, the goal is to minimize production, no-load, startup, shutdown and active power curtailment costs, and also the unavailability of the generation units. An exact methodology based on reformulation-and-decomposition is proposed to solve this challenging bi-level program. The proposed model has been tested on the standard distribution substations and transmission network, which shows that the proposed model has an effective performance for both systems, and provides more realistic results. The simulation results showed that the proposed model with the approach of integrating distribution network substations in the transmission network increases the profitability of distribution networks and also reduces the costs of operating the transmission network. For example, it has been shown that with the proposed approach, the profitability of distribution networks can be increased by 30 percent, as well as operating costs in the transmission network has been reduced by up to 3 percent.

The issue of environmental emissions has forced the power systems to use cleaner energy sources such as renewable and hydroelectric technologies. However, during recent decades due to the limitations on the available water in many regions, the optimal water reservoir usage has been highlighted. In this regard, this paper proposes a multi-objective model for short-term hydrothermal scheduling problem in the presence of the pumped-storage technology. It is noted that the framework well models the cascaded configuration of hydro reservoirs. Besides, in order to more accurately model the mentioned problem, a Mixed-Integer Non-Linear Programming (MINLP) optimization framework is presented. In this respect, the valve-loading effects occurred in thermal power generation technologies have been taken into account which turns the existing convex optimization problem into a non-convex one. In order to solve the mentioned problem, the Normal Boundary Intersection (NBI) method has been used while the VIKOR decision maker is employed to choose the most compromise solution amongst the Pareto optimal solutions obtained by NBI method. Finally, the efficiency of the proposed model has been verified through implementing four case studies and comparing the obtained results with those obtained by different methods.

The presence of gas-to-power (G2P) and power-to-gas (P2G) systems in the power system has led to the interdependency of gas and electricity energies. Thus, to increase energy efficiency and achieve desirable financial profit to the mentioned elements, an energy market and ancillary services combined by gas and electricity energies are required so that interdependency of electrical and gas energies and vice versa and the dependency of price to energy can be observable. Hence, this paper presents the strategic offering method for gas and electricity producers (GPs and EPs) in the coupled gas and electricity market as the day-ahead (DA) energy and reserve model. This strategy includes a two-stage problem, where the first stage contains a bi-level model that minimizes the difference between the expected operation cost and expected revenue of all strategic EPs (SEPs) in DA electricity energy and reserve markets subjects to the SEPs model in the upper-level. The lower-level problem minimizes the expected non-SEPs operation cost limited to DC optimal power flow equations, reserve, and non-SEPs formulation. The same problem with the first stage formulation is repeated for GPs in the second stage. Strategic GPs (SGPs) participation model in the DA gas energy and reserve markets presents in the upper-level problem but the market clearing price (MCP) method in the gas network expresses in the lower-level problem. This scheme includes uncertainties of demand and renewable power that are modeled by stochastic programming. The Karush-Kuhn-Tucker (KKT) approach obtains a single-level formulation in each stage, and thus, the master/salve decomposition algorithm solves the proposed strategy to achieve the optimal solution. Finally, this method is simulated on a standard test system to examine its capabilities. The numerical results obtained by the algorithm highlights that it can find the optimal solution in the least possible time considering the least computational errors. Furthermore, units with inexpensive fuel generally participate in the energy market following the proposed strategy; however, all units participate in the reserve market at all operating hours, which aims to achieve high optimal financial profit for the units. Following this, SEPs and SGPs in the mentioned market succeeded to obtain a profit of 37.5% and 13.6%, respectively, in their operating cost.

In this paper, a hybrid approach for tuning and placement of power system stabilizers (PSS) in multi-machine power systems is provided with the aim of reducing low-frequency oscillations (LFO) and improving power system dynamic stability with wide range of changes in system parameters and operating point of the system. PSS parameters are adjusted using Particle Swarm Optimization algorithm (PSO), and using Takagi–Sugeno (TS) fuzzy, optimal location for the PSS is determined. The employed fuzzy system has two inputs, the real part and the damping coefficient of network eigenvalues. The results of test on a grid four machine-two area sample, show optimal performance of the proposed method in improving system stability and reducing low-frequency oscillations of local and inter-area modes.

In this paper, an algorithm is presented to obtain the parameters of reward and penalty schemes (RPSs) for each electric company using Data Envelopment Analysis (DEA). This algorithm is based on reliability indices and customers’ value of interruptions. The companies’ efficiency scores are calculated using a DEA methodology. The companies’ efficiency scores are based on their performance in quality and the best performer is utilized as a benchmark for other companies. DEA efficiency score and historical quality level are used to set a quality target for each electric distribution company. In this algorithm more than one reliability index can be included in RPS and weighting value for each reliability index is calculated based on their impacts on the customers. The performance of the proposed algorithm is demonstrated in a case study to design RPS for Iranian electricity distribution companies. The results of the algorithm include DEA efficiency score, parameters of RPS and weighting value for each reliability index.