A specialized genetic algorithm to solve the short term transmission network expansion planning

This paper presents a mathematical model and a methodology to solve a transmission network expansion planning problem considering open access. The methodology finds the optimal transmission network expansion plan that allows the power system to operate adequately in an environment with multiples generation scenarios. The model presented is solved using a specialized genetic algorithm. The methodology is tested in a system from the literature.

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.

In this paper, a method for solving the short term transmission network expansion planning problem is presented. This is a very complex mixed integer nonlinear programming problem that presents a combinatorial explosion in the search space. In order to find a solution of excellent quality for this problem, a constructive heuristic algorithm is presented in this paper. In each step of the algorithm, a sensitivity index is used to add a circuit (transmission line or transformer) or a capacitor bank (fixed or variable) to the system. This sensitivity index is obtained solving the problem considering the numbers of circuits and capacitors banks to be added (relaxed problem), as continuous variables. The relaxed problem is a large and complex nonlinear programming and was solved through a higher order interior point method. The paper shows results of several tests that were performed using three well-known electric energy systems in order to show the possibility and the advantages of using the AC model.

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) 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.

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.

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.

In deregulated power system, the transmission networks are experiencing significant changes in both the operational and expansion planning. Traditionally, transmission network expansion planning was based on minimization the total cost of energy delivering with a certainty level of reliability, and it is optimal under strict constraints. But some constraints and objectives are flexibility or elasticity due to uncertain in electricity market, the solution is insignificante in practice under strict constraints. This paper presents a new approach and model for transmission network expansion planning in deregulated power system. This method adopts elasticity constraints and elasticity coefficients for transmission network expansion planning, the main elastic factors node real power injection, circuit real power limitation and circuit investment cost are considered. The optimal solution is based on a certain belief level, it can overcome the shortcoming of strict constraints, and accords with the engineering practice. This Elaticity modelling method for transmission network expansion planning provides a new planning conceptual in deregulated power system.

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.

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.

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

As installed capacity of wind power has been increasing in the last decade, power system planners are exposed to the several challenges, with regard to the drawbacks for wind power usage because of the existing infrastructure. The transmission expansion planning (TEP) and reactive power planning (RPP) should be specified by considering the maximization of wind power usage as well as meeting load growth. In this study, a new methodology for TEP and RPP is proposed to minimize wind energy spilled due to the power system constraints such as voltage and transmission line limitations. For this reason, the power flow equations of AC model should be used. Because of the intractable nature of large scale optimization problems, the backward search approach is utilized. AC optimal power flow based economic dispatch is solved to determine optimal dispatches. The proposed methodology is illustrated on the IEEE-24 bus reliability test system to demonstrate the results.

In this paper, a Line-Wise (LW) formulation of the AC Transmission Network Expansion Planning (AC-TNEP) problem for meshed transmission networks is proposed. The proposed formulation is then relaxed using a novel Quadratic Convex (QC) relaxation that can handle the mixed-integer nature of the problem and be solved efficiently using commercial solvers. The exact and relaxed formulations have been integrated into an algorithm for solving the AC-TNEP problem that has been introduced as an enhancement of an existing algorithm in the literature for obtaining feasible solutions of the AC-TNEP problem upon the use of convex relaxations to solve it. The AC-TNEP problem is solved for different scenarios that involve test cases ranging from 6 to 118 buses. The obtained solutions for the 6-bus to 46-bus test cases are shown to be globally optimal or reinforced to identical or better than the available solutions in the literature. Moreover, as distinguished from solutions in the literature, the proposed algorithm does not utilize approximations and heuristic constraints that are used to ease solving the AC-TNEP problem and were found to potentially lead to locally optimal solutions. Furthermore, the proposed algorithm is used to solve the AC-TNEP problem for the 87-bus and 118-bus test cases to establish its ability to solve for larger test cases. Extensions of the proposed algorithm to account for Reactive Power Planning (RPP) and dynamic planning are studied to further demonstrate its merits.

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