Enhanced Coati Optimization Algorithm for Static and Dynamic Transmission Network Expansion Planning Problems

A novel meta-heuristic model for the multi-year transmission network expansion planning

Expansion of the electricity sector and liberalized electricity market leads to more jobs challenging the designer of the transmission network. In order to match with the load growth and generation patterns schemes optimal transmission network expansion planning (TNEP) has to be done. It will find out where, when and how many new transmission lines should be added in the network. From the recent past, renewable energy resources are becoming major attraction part to the power sector. Large scale integration of distributed generation (DG) affects the electrical industry and as well as the strategy of the transmission network expansion. In this paper the impact of distributed generation (DG) in transmission network expansion planning (TNEP) is presented. The objective of TNEP is to minimize the transmission line investment cost required to meet the growing load and the added constraints. Static TNEP problem is modeled by using DC power flow model and to solve this problem a population search based algorithm named, Gbest Artificial Bee Colony (GABC) optimization algorithm is proposed. To show the effectiveness of the proposed method, it is evaluated on Garver's 6-bus network, and IEEE 24-bus test system. Results obtained are compared with the previous published literature.

A reliable approach for solving the transmission network expansion planning problem using genetic algorithms

The paper is focusing on dynamic transmission network expansion planning (DTNEP). The DTNEP problem has been approached from the retrospective and prospective point of view. To achieve this goal, the authors are developing two software tools in Matlab environment. Power flow computing is performed using conventional methods. Optimal power flow and network expansion are performed using artificial intelligence methods. Within this field, two techniques have been tackled: particle swarm optimization (PSO) and genetic algorithms (GA). The case study refers to a real power system modeled on the Center, Northern, Eastern and Southern parts of the Romanian Power System.

The main goal of Transmission Network Expansion Planning (TNEP) is determination of the number, time and location of new lines to be added to transmission network. Up to now, different methods have been used to solve the static TNEP (STNEP). In most of them, this problem is implemented regardless of power loss and the uncertainty in the load demand. With respect to the importance of these two parameters (loss and uncertainty) and their key role in an effective and precise planning, the evaluation and solution of STNEP using more efficient methods can be very useful. Hence, in this paper, a new method named Improved Discrete Particle Swarm Optimization (IDPSO) is employed for the solution of STNEP problem considering simultaneously the loss and uncertainty in load demand. Finally, the proposed approach is applied to the real transmission network of Azerbaijan Regional Electrical Company located in northwest of Iran. Comparison of the results obtained from the proposed method with those of Discrete Particle Swarm Optimization (DPSO) approach verifies the effectiveness and accuracy of the method in STNEP problem.

The main goal of Transmission Network Expansion Planning (TNEP) is determination of the number, time and location of new lines to be added to transmission network. Up to now, different methods have been used to solve the static TNEP (STNEP). In most of them, this problem is implemented regardless of power loss and the uncertainty in the load demand. With respect to the importance of these two parameters (loss and uncertainty) and their key role in an effective and precise planning, the evaluation and solution of STNEP using more efficient methods can be very useful. Hence, in this paper, a new method named Improved Discrete Artificial Bee Colony Algorithm (IDABC) is employed for the solution of STNEP problem considering simultaneously the loss and uncertainty in load demand. Finally, the proposed approach is applied to the real transmission network of Azerbaijan Regional Electrical Company located in northwest of Iran. Comparison of the results obtained from the proposed method with those of Discrete Artificial Bee Colony Algorithm (DABC) approach verifies the effectiveness and accuracy of the method in STNEP problem.

Long term planning in power transmission network expansion provides a well ordered and profitable extension of power equipment and facilities to meet the expected electric energy demand with an allowable degree of reliability. However, high quality and improved reliability in energy supply have to be balanced with the available funds. The need to expand transmission network can never be over emphasized. Transmission Network Expansion Planning (TNEP) is a periodical measure that must be carried out due to dynamic societies that attract extra energy demands. It is highly important to minimize the network reinforcement and operational costs while satisfying the increase in demand imposed by technical and economic conditions over the planning horizon. Several optimization algorithms for TNEP problems have been developed and applied over the past decades. This paper presents a comprehensive state-of-the-art survey on the TNEP optimization algorithms. The approach of this paper is in the area of highlights of the various available TNEP algorithms, their applications, viability, computational complexities and drawbacks, which can aid in the identifications of the proper methods that can yield an optimal solution to TNEP problem.

Transmission network expansion planning (TNEP) has become increasingly challenging due to the worldwide transformation of power systems, with the fast deployment of renewable energies as one of the key drivers. Given current socio-environmental concerns, building new lines to integrate new renewable energy sources may take several years and has a significant risk of delays and cost overruns. A sound strategy to achieve a more adapted expansion plan is to include the option of uprating existing lines among planning candidates. This paper presents a novel model for multi-year stochastic TNEP considering line uprating options, such as line reconductoring, voltage uprating, and adding series compensation, along with adding new lines, simultaneously. Compared to adding new lines, line uprating can be performed with low out-of-time service and has lower socio-environmental impact, thus being a cost-effective alternative, especially in cases where acquiring new right-of-ways is difficult and expensive. Illustrative results demonstrate that including line uprating options within the TNEP problem allows us to obtain more economic expansion plans with fewer new line projects in comparison to traditional approaches. This in turn results in less environmental impact of the optimal expansion plan, which makes construction permissions easier to obtain, thus reducing the risk of having delays and cost overruns. Additionally, considering line uprating options also helps the integration of renewable energies by deferring investment decisions until more information regarding future development of renewable energies becomes available.

This paper presents, a static transmission network expansion planning (STNEP) with considering the pumped-storage (PS) unit, and the transmission switching (TS) concept. The aim of this work, is to minimize the total cost (TC) of the system. The TC is the summation of the transmission line cost (CTL) and the generation cost (GC) of thermal generating units. TS is a relatively new area of research which provides a path to the transmission system operator (TSO) to avoid generation expansion and transmission expansion. The addition of new lines increases the investment cost hence, now-a-days more renewable energy resources have been adopted by the TSO. The proposed STNEP problem is modelled using a DC power flow model. The performance of the proposed scheme is tested on the modified Garver 6-bus system. The results indicate that the combinatorial effect of PS unit and TS has reduced the total cost of the system notably.

Impact of wind uncertainty, plug-in-electric vehicles and demand response program on transmission network expansion planning

This paper presents a metaheuristic approach to solve the transmission network expansion planning (TNEP) problem considering non-conventional solution candidates. The TNEP consists on finding the set of new elements required in a power system to meet a given future demand at a minimum cost. The TNEP traditionally considers as candidate solutions the addition of new lines and transformers. The main contribution of this work is the inclusion of non-conventional solution candidates. Such non-conventional solution candidates are namely: repowering of existing circuits and reactive shunt compensation. Also, an AC modeling of the network that allows obtaining more realistic results than the traditional DC model has been considered. The TNEP is represented by means of a non linear mixed integer programming problem which is solved through a hybrid genetic algorithm (HGA). Several tests were performed on two benchmark power systems to show the applicability and effectiveness of the proposed approach.

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.

Up to now, various methods have been presented for solution of the static transmission network expansion planning (STNEP) problem. However, in all of these methods, STNEP problem has been solved regardless to voltage level of transmission lines and role of voltage level in reducing annual losses of the network. In this paper, STNEP has been studied considering voltage level, network losses and number of bundle lines (which cause different network annual losses) using decimal codification based genetic algorithm (DCGA). Genetic Algorithms (GAs) have demonstrated the ability to deal with non-convex, nonlinear, integer-mixed optimization problems, like the TNEP problem, better than a number of mathematical methodologies. The proposed method is tested on an actual transmission network of the Azerbaijan regional electric company, Iran to illustrate its robust performance. The results show that considering the network losses in a power system with different voltage levels, decreases the operational costs considerably and the network satisfies the requirement of delivering electric power more safely and reliably to load centers.

The transmission network expansion planning is necessary for supplying the future needs, considering load growth. Furthermore, in restructured environments, transmission lines provide the required infrastructure for creating a competitive environment. In recent years, there has been a significant advancement in storage technologies. This advancement leads to using energy storage systems to postpone the construction or replacement of transmission lines. Therefore, in this paper, the problems of transmission expansion planning and energy storage systems deployment are investigated simultaneously. Considering the presence of storage devices and their effect on network operation cost, in this paper, the operation cost is modeled as an independent objective function along with investment cost. Moreover, the problems of transmission and storage expansion planning are modeled as a tri-objective optimization problem with the objectives of reducing costs and increasing the social welfare index in the power market. The multi-objective shuffled frog leaping evolutionary algorithm is used to solve these problems. The presented model for expansion planning is implemented and analyzed on IEEE 24-bus test system in the presence and absence of energy storage systems, and the effect of change in the price of energy storage systems is studied. The results of this research show that as the technology advances and the storage costs decrease, energy storage systems can play a pivotal role in reducing expansion planning costs of the power network and improving market-based indices in the restructured environment.

Guest Editorial: Situational awareness of integrated energy systems