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

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

The power systems are becoming more and more complex due to the inclusion of new components and increasing load demand. Consequently, it is imperative to incorporate additional generation units and transmission links into the system. Transmission Network Expansion Planning (TNEP) seeks to include generation units and transmission lines into the system at optimal locations and minimal costs. Mathematical techniques are extensively employed to address the problem. Nonetheless, mathematical methods necessitate extensive computation durations. Consequently, novel solution strategies are under investigation. The TNEP problem is characterized by an innovative and effective metaheuristic optimization techniques. This study presents a novel Opposition Based Learning and Fitness Distance Balance based Coati Optimization Algorithm (FDBCOA-OBL) designed to address Static and Dynamic TNEP problems. An extensive experimental investigation was undertaken to evaluate the efficacy of the suggested method in addressing the benchmark test suites and the TNEP problem. The FDBCOA-OBL algorithm, utilizing the Elite OBL approach, surpassed all other comparative versions in addressing the benchmark test problems. The Wilcoxon analysis indicates that it lost 6 problems, tied in 110, and won 166 problems. The proposed approach resolved the TNEP problem in 6, 25, and 93-bus test systems. The Static TNEP solution was applied to the 6 and 25 bus test systems, while the Dynamic Multistage TNEP method was utilized in the 93-bus test system. The acquired investment expenses were compared to the research already documented in the literature. The findings indicate that the suggested method demonstrates robust performance.

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.

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.

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.

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.

The paper is focusing on transmission network expansion planning (TNEP) problem solved using artificial intelligence techniques. It is divided into two parts. The 1st part is dedicated to the particle swarm optimization (PSO) and genetic algorithm (GA) concepts and mechanisms. The mathematical models and the associated software tool are also presented. Practical considerations are discussed. The 2nd part is focusing on case studies. 13 buses test power system, developed by the authors and IEEE 24 RTS have been used. The research work is going to be used in case of the Romanian power system (over 1000 buses).

Transmission network expansion planning (TNEP) is a key component of power system planning. Two important aspects to be considered in TNEP are economy and security factors, which are always contradictory and affect the benefits of both investment and power demand side, so the balance between these two factors needs to be focused. In this paper, a TNEP model based on coordinated economy and security is proposed so as to reconcile these two contradictory factors. The objective function of proposed TNEP model is to minimize the annual synthetic cost of transmission system, which contains investment cost, operation cost, maintenance cost and load shedding cost. The annual synthetic cost of transmission system is calculated with multistep load model established by load clustering using improved K-means algorithm. Then, an improved niche genetic algorithm is utilized to solve the TNEP model and an optimal scheme is obtained. Furthermore, the applicability and effectiveness of proposed TNEP model and algorithm are verified by case study on IEEE 24-bus system.

In electricity transmission networks, energy storage systems (ESS) provide a means of upgrade deferral by smoothing supply and matching demand. We develop a mixed integer programming (MIP) extension to the transmission network expansion planning (TEP) problem that considers the installation and operation of ESS as well as additional circuits. The model is demonstrated on the well known Carver's 6-bus and IEEE 25-bus test circuits for two 24 hour operating scenarios; a short peak, and a long peak. We show optimal location and capacity of storage is sensitive not only to cost, but also variability of demand in the network.

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.

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

Transmission network expansion planning (TNEP) problem is a major aspect of power system planning which must account for the load uncertainties in a practical system. However, this increases the computational burden very significantly. This paper presents a heuristic method to address the problem in an efficient way. A two stage method is developed, where the first stage comprises of solving DC, crisp TNEP, and the second stage utilizes the information obtained from first stage to constitute suitable heuristics to be used with any metaheuristics aimed at reducing the overall computational burden. DC boundary power flow approach is used to account for the load uncertainties. Two well-known test systems of 6 bus Garver and IEEE 24 bus system are used to demonstrate the effectiveness of the proposed approach in both base case and contingency cases. An improved harmony search (IHS) algorithm is used to test the effectiveness of developed heuristics.

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.

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.