Static Transmission Expansion Research Articles

Robust Static Transmission Expansion Planning Considering Contingency and Wind Power Generation

This paper proposes a framework based on the Benders decomposition to obtain a scenario-based robust static transmission expansion planning by considering N-1 security criterion, transmission losses and uncertainties in wind power generation. The model is solved by a bi-level approach that seeks to minimize investment cost as well as penalty costs of wind spill and load curtailment. The wind uncertainty is modeled by grouped historical wind series through k-means clustering technique maintaining the wind correlation between different geographic regions. Case studies are performed in the well-known power systems: IEEE-RTS 24-bus test system and an equivalent Brazilian Southern 46-bus system. In addition, a detailed tutorial case is also presented with a modified version of Garver 6-bus test system.

Optimal static and dynamic transmission network expansion planning

In this paper, a novel approach is proposed to solve the problem of static and dynamic Transmission expansion planning (TEP) in electrical power systems. The proposed method is the execution of an Adaptive salp swarm optimization algorithm (ASSO). The ASSO is a meta-heuristic algorithm, which depends on the swarming behavior and population of salps. In the proposed approach, ASSO is the charge of acquiring the best candidate solution for the expansion planning and locate the optimal solution for both the static and the dynamic transmission network expansion in perspective of the minimum error objective function. The objective is formulated to minimize the cost of investment in transmission systems by satisfying the constraints such as active power balance, expansion parameter, and load shedding restrictions. The proposed ASSO technique ensures the system with less complexity, reduced computational time and hence the efficiency of the system is raised. Finally, the proposed model is executed in MATLAB/Simulink working stage and the execution is compared with the existing techniques.

Evaluation of GA Performance in TNEP Considering Voltage Level, Network Losses and Number of Bundle Lines

Many models were presented to solve the static transmission network expansion planning (STNEP) problem by previous research. However, in these models, lines’ voltage level and losses were not studied in STNEP. Therefore, in present paper, static transmission expansion planning is investigated by considering lines’ voltage, losses and bundles using decimal codification genetic algorithm (DCGA). The DCGA is better than mathematical methodologies to solve large-scale, nonlinear and mixed-integer optimization problems, like the TNEP. The proposed method is tested on the real transmission network of Azarbaijan regional electric company, Iran. The results show that operation costs decreases considerably and the transmission system delivered more safe and reliable electric power to customers if the network losses, voltage levels and the number of bundle lines are considered in transmission expansion planning.

Investigation of static transmission expansion planning using the symbiotic organisms search algorithm

ABSTRACTTransmission expansion planning (TEP) has become a complex problem in restructured electricity markets. This article presents the symbiotic organisms search (SOS) algorithm, a novel metaheuristic optimization technique for solving TEP problems in power systems. The SOS algorithm is inspired by the interactions among organisms in an ecosystem. The TEP problem is formulated here as an optimization problem to determine the cost-effective expansion planning of electrical power systems. Several constraints, such as power flow of the lines, right-of-way validity and maximum line addition, are taken into consideration. First, the SOS algorithm is tested with several benchmark functions. Then, it is applied on three standard power system networks (IEEE 24-bus system, Brazilian 46-bus system and Brazilian 87-bus system) in a TEP study to demonstrate the optimization capability of the proposed SOS algorithm. The results are compared with those produced by other state-of-the-art algorithms.

Static transmission expansion planning for realistic networks in Egypt

Transmission network planning (TEP) is very important task in electric power systems. It begins with the establishment of power demand growth scenarios, in accordance with forecasts along the time and to obtain the optimal expansion plan, while fulfilling power systems constraints. This paper proposes a two-stage TEP procedure for two realistic transmission Egyptian networks, Western Delta Network (WDN) and 500kV of Extra High Voltage Network (EHVN). In the first stage, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is employed to obtain the predicted long-term load forecasting (LTLF) up to 2030. In the second one, the Integer Based Particle Swarm Optimization (IBPSO) technique is developed for solving the static TEP problem. The proposed TEP aims at finding the optimal transmission routes, at least capital investment costs, to meet the forecasted load. The TEP problem is formulated as non-linear, large scale, mixed-integer and non-convex optimization problem. The static TEP problem is employed using DC power flow model. The proposed TEP methodology is tested on standard Garver 6-bus test systems. The load forecasting methodology is dependent on the historical (Actual) peak load data for the UEN from 1993 to 2015. The location and capacity of new site of generation station are selected to meet the demand required. Also, the AC load flow method is emerged with the TEP solution employed by IBPSO for Garver and EHVN to assess the voltage, reactive power and security constraints. Numerical results show the capability of the proposed procedure to solve TEP problem at acceptable economical and technical benefits.

An MILP model for the static transmission expansion planning problem including HVAC/HVDC links, security constraints and power losses with a reduced search space

This paper presents a mixed-integer linear programming (MILP) model to solve the transmission network expansion planning (TNEP) problem, considering HVDC and HVAC proposals with security constraints and the system operation cost. The use of an MILP model guarantees convergence to optimality by using existing classical optimization methods. The operation cost is associated with the cost of transmission losses in the system. In this model, the HVDC lines can be either monopolar or bipolar type. Due to the size and complexity of the problem, a new method to reduce the search space solution is proposed, taking into account line losses, HVDC links, and contingencies. The set of HVDC lines in the proposal may use either line-commutated converters or voltage source converters. The model was implemented using the AMPL modeling language and solved using the CPLEX commercial solver. The Southern Brazilian electrical system was used to demonstrate the effectiveness of the proposed MILP model; tests were performed for cases with and without HVDC links, and with and without the system operation cost. The results show high quality solutions, and high performance when the search space reduction methodology (SSRM) is used. The computational time with the SSRM (the study case with HVDC and operation cost) was reduced by 85.9% with respect to the case that uses the complete search space, and also, the optimal solution was found. The importance of including the operation cost as an objective to be minimized is shown. The cost of losses is reduced by 55.7% when comparing the cases with and without the cost of operation included in the objective function and with HVDC lines as proposals.

Social spider algorithm for solving the transmission expansion planning problem

In this paper, an efficient metaheuristic technique is proposed for solving the static transmission expansion planning problem. This technique is known as the social spider algorithm. A suitable methodology for handling the problem constraints is also proposed. In the proposed methodology, a DC power flow sub-problem is solved for each network resulting from adding a potential solution developed by the social spider algorithm to the base network. The proposed algorithm and the accompanying constraints handling methodology have been tested through solving the transmission expansion planning problem of three benchmark systems having 6-buses, 46 buses, and 87 buses. Excellent performance and new solutions are demonstrated in the results section. The new solutions achieve a remarkable reduction in the total investment cost.

A Strategy to Solve the Multistage Transmission Expansion Planning Problem

In this letter, a heuristic to reduce the combinatorial search space (CSS) of the multistage transmission expansion planning (MTEP) problem is presented. The aim is to solve the MTEP modeled like a mixed binary linear programming (MBLP) problem using a commercial solver with a low computational time. The heuristic uses the solution of several static transmission expansion planning problems to obtain the reduced CSS. Results using some test and real systems show that the use of the reduced CSS solves the MTEP problem with better solutions compared to other strategies in the literature.

A Multi-Objective Framework for Transmission Expansion Planning in Deregulated Environments

Deregulation of power system has introduced new objectives and requirements for transmission expansion problem. In this paper, a static transmission expansion methodology is presented using a multi-objective optimization framework. Investment cost, reliability (both adequacy and security), and congestion cost are considered in the optimization as three objectives. To overcome the difficulties in solving the nonconvex and mixed integer nature of the optimization problems, the genetic based NSGA II algorithm is used followed by a fuzzy decision making analysis to obtain the final optimal solution. The planning methodology has been demonstrated on the IEEE 24-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 northeastern part of Iranian national 400-kV transmission grid.

Differential evolution algorithm for static and multistage transmission expansion planning

A novel differential evolution algorithm (DEA) is applied directly to the DC power flow-based model in order to efficiently solve the problems of static and multistage transmission expansion planning (TEP). The purpose of TEP is to minimise the transmission investment cost associated with the technical operation and economical constraints. Mathematically, long-term TEP using the DC model is a mixed integer nonlinear programming problem that is difficult to solve for large-scale real-world transmission networks. In addition, the static TEP problem is considered both with and without the resizing of power generation in this research. The efficiency of the proposed method is initially demonstrated via the analysis of low, medium and high complexity transmission network test cases. The analysis is performed within the mathematical programming environment of MATLAB using both DEA and conventional genetic algorithm and a detailed comparative study is presented.

A Metaheuristic to Solve the Transmission Expansion Planning

In this letter, a genetic algorithm (GA) is applied to solve the static and multistage transmission expansion planning (TEP) problem. The characteristics of the proposed GA to solve the TEP problem are presented. Results using some known systems show that the proposed GA solves a smaller number of linear programming problems in order to find the optimal solutions and obtains a better solution for the multistage TEP problem.

Closure to discussion of "voltage stability analysis: V-Q power flow simulation versus dynamic simulation"

of each allele represents the number of blocks of fixed capacity that are being added to the existing circuit. The representation of other situations, such as replacing the existing circuit for another one with different technology, or different voltage, would require the use of more than one allele for the respective circuit (let call them as “sub-alleles”) as well as the implementation of a rule that tells the GA to turn off the effect of one sub-allele to let the other take place. For instance, if one wants to analyze the expansion of a given circuit at two different voltage levels, say 230 kV and 500 kV, two sub-alleles should be set up: one representing the number of blocks of expansion at 230 kV and the other at 500 kV. The aforementioned rule should ask the GA to fix to zero either one of the sub-alleles to let the other influence in the chromosome’s fitness. Even though we did not implement such an option, we believe that it can be successful. Concerning the second issue, even though a GA for finding optimum dynamic expansion plans could be developed and implemented, it should be mentioned that considering the uncertainties, the amount of information that is no longer available for the public and the decentralization of decisions in a competitive market of electricity supply, the effort of implementing a GA-based, dynamic expansion model, would not be worthwhile. In our opinion, the dynamic of the expansion (dating of new installations) is defined by complementary studies such as stability and reliability assessment and short-circuit analysis. However, among the set methodologies, methods and programs for the expansion planning, a static integer transmission expansion model is necessary to pick up major interconnections and facilities that will be present in most of the scenarios. A short-run, market-driven expansion model will never foresee those large-scale projects. Regarding the third issue, in a market based on physical bilateral contracts with short-term transactions, the long-term view about the problem should be replaced by a short-term view, whose emphasis is to manage congestion through prices. Nevertheless, if the interest is focused on the long-term analysis of the transmission expansion under such a market of contracts, a GA-based algorithm could be used, if multiple scenarios of load, dispatch and bilateral transactions are considered.