Dynamic Transmission Network Expansion Research Articles

Techno-economic based static and dynamic transmission network expansion planning using improved binary bat algorithm

This paperproposes an improved binary bat algorithm (IBBA) for solving static and dynamic transmission network expansion planning (TNEP) problems for standard and realistic networks considering different objective functions (OFs).The proposed IBBA has two modifications to enhance the solution quality based on multi V-shaped transfer function and adaptive search space (ASS). The IBBAis applied to solve the static TNEP problem. A two-stage procedure is employed to solve the dynamic TNEP problem. In stage-1, the adaptive neuro-fuzzy inference system (ANFIS) is utilized to find the long-term load forecasting (LTLF) up to 2039. In stage-2, the IBBA is used to solve the dynamic TNEP problem.Two OFs are considered for solving TNEP problems. The first OF achieves the investment cost reduction. The second OF aims to minimize the total costs, which include the investment cost and the total costs of energy losses and reactive power compensation (RPC). The proposed procedure is applied to Garver’s 6-bus system and the West Delta Network (WDN) as a part of the Unified Egyptian Transmission Network (UETN) to solve TNEP problems. The obtained results are compared with other methods to show the robustness of the proposed procedure for solving TNEP problems.

Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA

This paper introduces a multi-stage dynamic transmission network expansion planning (MSDTNEP) model considering the N-1 reliability constraint. The integrated planning problem of N-1 security and transmission expansion planning is essential because a single line outage could be a triggering event to rolling blackouts. Two suggested scenarios were developed to obtain the optimal configuration of the Egyptian West Delta Network’s realistic transmission (WDN) to meet the demand of the potential load growth and ensure the system reliability up to the year 2040. The size of a blackout, based on the amount of expected energy not supplied, was calculated to evaluate both scenarios. The load forecasting (up to 2040) was obtained based on an adaptive neuro-fuzzy inference system because it gives excellent results compared to conventional methods. The linear population size reduction—Success-History-based Differential Evolution with semi-parameter adaptation (LSHADE-SPA) hybrid—covariance matrix adaptation evolution strategy (CMA-ES) algorithm (LSHADE-SPACMA)—is applied to solve the problem. The semi-adaptive nature of LSHADE-SPACMA and the hybridization between LSHADE and CMA-ES are able to solve complex optimization problems. The performance of LSHADE-SPACMA in solving the problem is compared to other well-established methods using three testing systems to validate its superiority. Then, the MSDTNEP of the Egyptian West Delta Network is presented, and the numerical results of the two scenarios are compared to obtain an economic plan and avoid a partial or total blackout.

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.

Robust dynamic transmission and renewable generation expansion planning: Walking towards sustainable systems

Recent breakthroughs in Dynamic Transmission Network Expansion Planning (DTNEP) have demonstrated that the use of robust optimization, while maintaining the full temporal dynamic complexity of the problem, renders the capacity expansion planning problem considering uncertainties computationally tractable for real systems. In this paper an adaptive robust formulation is proposed that considers, simultaneously: (i) a year-by-year integrated representation of uncertainties and investment decisions, (ii) the capacity expansion lines have and (iii) the construction and/or dismantling of renewable and conventional generation facilities as well. The Dynamic Transmission Network and Renewable Generation Expansion Planning (DTNRGEP) problem is formulated as a three-level adaptive robust optimization problem. The first level minimizes the investment costs for the transmission network and generation expansion planning, the second level maximizes the costs of operating the system with respect to uncertain parameters, while the third level minimizes those operational costs with respect to operational decisions. The method is tested on two cases: (i) an illustrative example based on the Garver IEEE system and (ii) a case study using the IEEE 118-bus system. Numerical results from these examples demonstrate that the proposed model enables optimal decisions to be made in order to reach a sustainable power system, while overcoming problem size limitations and computational intractability for realistic cases.

Rolling Window Based Dynamic Transmission Network Expansion Planning

Dynamic transmission network expansion planning (DTNEP) decides when (which year) and where (which R.O.W.), the new lines are to be built so that the overall cost of the plan is minimum and the system is secure. Generally, this is done for a fixed time horizon window (3/5 years) at a time. DTNEP has been proved to be better than the incremental approach in which optimal investment plans are found out for each year separately. In DTNEP, 1st year and last/terminal years are given different treatment, and there is a complete disconnect between two DTNEP windows. Also DTNEP assumes that the conditions during optimization like the earlier year generation, transmission expansion plans get\ executed exactly as per plan &/or the load forecast for the entire period remains unchanged. However, it is well known that this is generally not true. In view of above, this paper presents a rolling window based approach which treats every year of investment in exactly the same fashion. It also has an inherent capability to take care of such changes. It involves successive, overlapping DTNEP for 3/5 years planning horizon. A harmony search algorithm is used for the solution of the problem. Results for two test systems have been obtained to demonstrate the potential of the algorithm.

Transmission network expansion planning under an improved genetic algorithm [discussion and closure

The authors congratulate the authors of the original paper (see ibid., Vol.18, no.3, p.1168-75, 2000) for their excellent contribution in the field of transmission expansion planning. The successful application of an improved GA (IGA) for the solution of the static transmission network expansion-planning problem is presented. In their opinion, the main contributions of this paper are the proposed and proved ideas for the IGA, related to: representation; initial population construction; crossover; and mutation mechanism. They explored these topics in the context of the dynamic transmission network expansion planning. They confirmed the importance of adequate representation of individuals that avoid invalid solutions, of an initial population based on sensitivity analysis of an initial expansion plan, and a crossover based on a decimal representation. The response of the original authors to this discussion is also included.