In this paper a new framework is presented for Multi–objective Transmission Expansion Planning (TEP). This framework is based on a Multiple criteria decision making whose fundamental elements are REliability and MARKet (REMARK). Investment cost, congestion cost, Users' Benefit (UB) and Expected Customer Interruption Cost (ECOST) are considered in the optimization as four objectives. The proposed model is a complicated non-linear mixed-integer optimization problem. A hybrid Genetic Algorithm (GA) and Quadratic Programming (QP) is used, followed by a Fuzzy Sets Theory (FST) to obtain the final optimal solution. The planning methodology has been demonstrated on the 6-machine 8-bus test system to show the feasibility and capabilities of the proposed algorithm. Also, in order to compare the historical Expansion plan and the Expansion plan developed by the proposed methodology, it was applied to the real life system of the northeastern part of Iranian national 400-kV transmission grid.

Due to the increasing web, there are many challenges to establish a general framework for data mining and retrieving structured data from the Web. Creating an ontology is a step towards solving this problem. The ontology raises the main entity and the concept of any data in data mining. In this paper, we tried to propose a method for applying the "meaning" of the search system, But the problem for these methods is building a knowledge base that can be used for semantic search. The previous work interprets the query in three ways:’semantic relation in ontology’, ‘co-occurrence in the document’, and ‘semantic relation from Thesaurus’. The proposed method has two parts. The first part, using domain ontology for classified web pages based on keyword and the concept in each domain and builds Fuzzy ontology as Knowledge Base and the next section offers a method for expanding the query using built fuzzy ontology. In this paper, we tried to create knowledge base with WordNet as a comprehensive dictionary and extracted Sub string (phrases include Multi words) from WordNet for each keyword in each domain ontology. The created Search engine was applied to an experimental system to evaluate the "precision -Recall” and it was revealed that applying the proposed method can improve query Expansion 11% better in our experiments for precision.

This paper describes a methodology for transmission Expansion planning (TEP) within a deregulated electricity market. Two objective functions including investment cost (IC) and congestion cost (CC) are considered. The proposed model forms a bi-level optimization problem in which upper level problem represents an independent system operator (ISO) making its decisions on investment while in the lower level, the market clearing problem is formulated. ISO tries to minimize the investment cost on new transmission capacity to be installed and to minimize the congestion cost. Minimizing the CC can facilitate the competition between market participants. Locational marginal prices (LMPs) which are necessary to be calculated for the congestion cost are obtained at the lower level. The LMP of buses are dual variables of the corresponding active power balance equation. Lower problem is replaced by its Karush-Kuhn-Tucker (KKT) conditions resulting in a one-level optimization problem which can be efficiently solved by commercial existing solvers. The formulated Multi-objective mathematical programming is solved by augmented ε-constraint method which is able to produce the Pareto-optimal solutions. The presented framework is applied to a simple 3-bus power system and also IEEE 24-bus reliability test system (RTS). Results from these illustrative examples are reported and thoroughly discussed. The results show the effectiveness of the presented work.

In this paper, the probabilistic evaluation of the AC-DC distribution network planning has been discussed. Uncertainty in load demand and the power of renewable distribution generation (DG) leads to the probabilistic behavior of the network. This behavior causes the violation of bus voltage and line loading constraints. On the other hand, violation of the constraints is also the main factor of risk in the problem. Therefore, risk factors have been identified in the problem. The existing uncertainties are modeled through the clustering method. The scenarios with the highest cost are defined as severe events. For risk management, the expected shortfall (ES) index has been used to obtain the cost of severe events. Using the Multi-period method, long-term planning has been done. Therefore, the entire planning horizon is divided into several consecutive periods. The optimization problem has been solved concurrently for all periods. Therefore, the type of existing/new buses (AC or DC), the type of existing/new network lines and the optimal scheme of the network have been determined for all periods. The objectives of the problem are the minimization of the total planning costs (investment and operation costs) and the cost of severe events for all periods. Finally, the proposed method has been implemented on a sample Expansion network and the results have been analyzed.

Environmental pollution and rapid depletion are among the chief concerns about fossil fuels such as oil, gas, and coal. Renewable energy sources do not suffer from such limitations and are considered the best choice to replace fossil fuels. The present study develops a mathematical model for optimal allocation of regional renewable energy to meet a country-wide demand and its other essential aspects. The ultimate purpose is to minimize the total cost by planning, including power plant construction and maintenance costs and transmission costs. Minimum-cost flow equations are embedded in the model to determine how regions can supply energy to other regions or rely on them to fulfill annual demand. In order to verify the applicability of the model, it is applied to a real-world case study of Iran to determine the optimal renewable energy generation-transmission decisions for the next decade. Results indicate that the hydroelectric and solar power plants should generate the majority of the generated renewable electricity within the country, according to the optimal solution. Moreover, regarding the significant population growth and waste generation in the country’ s large cities, biomass power plants can have the opportunity to satisfy a remarkable portion of electricity demand.

This paper presents a Multi-year scenario-based methodology for transmission Expansion planning (TEP) in order to enhance the available transfer capability (ATC). The ATC is an important factor for all players of electricity market who participate in power transaction activities and can support the competition and nondiscriminatory access to transmission lines among all market participants. The transmission Expansion planning studies deal with many uncertainties, such as system load uncertainties that are considered in this paper. The Latin hypercube sampling (LHS) method has been applied for generating different scenarios related to the load uncertainty. The objective function in the TEP model is to minimize the sum of investment costs (IC) and the expected operation costs (OC). Both ATC and TEP models are represented based on AC power flow constraints which are more accurate compared with the widely-used DC approach. In this respect, the nonlinear terms in power flow equations are linearized in order to obtain the efficient solutions by existing commercial solvers that can guarantee the achievement to the global optimal solution using branch and bound technique. The proposed model is applied to the IEEE 24-bus Reliability Test System and the results obtained show the efficiency, tractability and applicability of the proposed model.

This study presents a convex formulation for generation-transmission Expansion planning in the presence of phase shifter transformers and aims at maximizing social welfare. By changing the voltage angle, the phase shifter transformer can control the transmission power of the line. Therefore, by installing a phase shifter transformer, one can reduce the investment cost of new lines and use the nominal capacity of available transmission lines. Accordingly, the planning of the generation-transmission Expansion in this paper is formulated with the assumption that there is a pool electricity market. The problem is formulated in the form of mixed integer programming and the CPLEX solver is used to solve it in the YALMIP Toolbox environment. In the proposed method, the location, capacity and year of installation of generators, transmission lines and new phase shifter transformers will be determined simultaneously. In order to validate the proposed planning, the Expansion planning of the IEEE 24-bus system has been simulated in MATLAB software. Simulation results show the efficiency of the proposed method.