Transmission Network Expansion Planning Problem Research Articles

The Efficacy of Multi-Period Long-Term Power Transmission Network Expansion Model with Penetration of Renewable Sources

The electrical energy demand increase does evolve rapidly due to several socioeconomic factors such as industrialisation, population growth, urbanisation and, of course, the evolution of modern technologies in this 4th industrial revolution era. Such a rapid increase in energy demand introduces a huge challenge into the power system, which has paved way for network operators to seek alternative energy resources other than the conventional fossil fuel system. Hence, the penetration of renewable energy into the electricity supply mix has evolved rapidly in the past three decades. However, the grid system has to be well planned ahead to accommodate such an increase in energy demand in the long run. Transmission Network Expansion Planning (TNEP) is a well ordered and profitable expansion of power facilities that meets the expected electric energy demand with an allowable degree of reliability. This paper proposes a DC TNEP model that minimises the capital costs of additional transmission lines, network reinforcements, generator operation costs and the costs of renewable energy penetration, while satisfying the increase in demand. The problem is formulated as a mixed integer linear programming (MILP) problem. The developed model was tested in several IEEE test systems in multi-period scenarios. We also carried out a detailed derivation of the new non-negative variables in terms of the power flow magnitudes, the bus voltage phase angles and the lines’ phase angles for proper mixed integer variable decomposition techniques. Moreover, we intend to provide additional recommendations in terms of in which particular year (within a 20 year planning period) can the network operators install new line(s), new corridor(s) and/or additional generation capacity to the respective existing power networks. This is achieved by running incremental period simulations from the base year through the planning horizon. The results show the efficacy of the developed model in solving the TNEP problem with a reduced and acceptable computation time, even for large power grid system.

Improved binary particle swarm optimization for the deterministic security-constrained transmission network expansion planning problem

- This paper proposes an improved version of Binary Particle Swarm Optimization (BPSO) to solve the security-constrained transmission network expansion planning (TNEP) problem using the DC network model. Some modifications, such as superior population configurations, are made to improve the efficiency of the BPSO by adapting it to the needs of the TNEP to obtain better results. A parallel implementation of the improved BPSO reduces execution times. Transmission losses are included using a quadratic function and piecewise linear approximation approach. Considering transmission losses in the long-term planning horizon can avoid underinvestment in network capacity and result in a different network expansion plan. The proposed approach is validated with numerical simulations of the classic Garver system, and its performance is tested on the IEEE 24-bus and 118-bus systems for a ten-year planning horizon. The simulation results are compared to those obtained from classic BPSO to verify the efficiency of the improved version of the BPSO.

A Branch and Bound Algorithm for Transmission Network Expansion Planning Using Nonconvex Mixed-Integer Nonlinear Programming Models

The branch and bound (BB) algorithm is widely used to obtain the global solution of mixed-integer linear programming (MILP) problems. On the other hand, when the traditional BB structure is directly used to solve nonconvex mixed-integer nonlinear programming (MINLP) problems, it becomes ineffective, mainly due to the nonlinearity and nonconvexity of the feasible region of the problem. This article presents the difficulties and ineffectiveness of the direct use of the traditional BB algorithm for solving nonconvex MINLP problems and proposes the formulation of an efficient BB algorithm for solving this category of problems. The algorithm is formulated taking into account particular aspects of nonconvex MINLP problems, including (i) how to deal with the nonlinear programming (NLP) subproblems, (ii) how to detect the infeasibility of an NLP subproblem, (iii) how to treat the nonconvexity of the problem, and (iv) how to define the fathoming rules. The proposed BB algorithm is used to solve the transmission network expansion planning (TNEP) problem, a classical problem in power systems optimization, and its performance is compared with the performances of off-the-shelf optimization solvers for MINLP problems. The results obtained for four test systems, with different degrees of complexity, indicate that the proposed BB algorithm is effective for solving the TNEP problem with and without considering losses, showing equal or better performance than off-the-shelf optimization solvers.

Chu and Beasley Genetic Algorithm to Solve the Transmission Network Expansion Planning Problem Considering Active Power Losses

Due to the accelerated growth of electricity demand, the scarcity of primary resources to produce electricity, and technological advances in recent years, electricity companies must face and solve these challenges in the best possible way, and for that, the Transmission Network Expansion Planning (TNEP) plays a crucial role, since the decisions taken in longterm planning determine the optimal form of expansion of the networks, to respond to these needs of electricity demands. On the other hand, there is also the tendency to leave the TNEP problem more efficient, robust, and closer to what happens in real electrical networks. For these reasons, this article proposes a methodology to solve the TNEP problem considering active power losses. The problem is formulated as a mixed-integer nonlinear programming (MINLP) problem. The Chu-Beasley Genetic Algorithm (CBGA) is used to transform the MINLP problem into a linear programming (LP) problem. Furthermore, the Villasana Garver constructive heuristic (VGCH) algorithm is used to make the investment proposals made by the AGCB feasible. To measure the efficiency and effectiveness of the proposed methodology several tests are performed on the 6- bus Garver system, the IEEE 24-bus test system, and the South Brazilian 46-bus test system

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.

Flexible Transmission Network Expansion Planning Based on DQN Algorithm

Compared with static transmission network expansion planning (TNEP), multi-stage TNEP is more in line with the actual situation, but the modeling is also more complicated. This paper proposes a new multi-stage TNEP method based on the deep Q-network (DQN) algorithm, which can solve the multi-stage TNEP problem based on a static TNEP model. The main purpose of this research is to provide grid planners with a simple and effective multi-stage TNEP method, which is able to flexibly adjust the network expansion scheme without replanning. The proposed method takes into account the construction sequence of lines in the planning and completes the adaptive planning of lines by utilizing the interactive learning characteristics of the DQN algorithm. In order to speed up the learning efficiency of the algorithm and enable the agent to have a better judgment on the reward of the line-building action, the prioritized experience replay (PER) strategy is added to the DQN algorithm. In addition, the economy, reliability, and flexibility of the expansion scheme are considered in order to evaluate the scheme more comprehensively. The fault severity of equipment is considered on the basis of the Monte Carlo method to obtain a more comprehensive system state simulation. Finally, extensive studies are conducted with IEEE 24-bus reliability test system, and the computational results demonstrate the effectiveness and adaptability of the proposed flexible TNEP method.

Multi-Year Stochastic Transmission Network Expansion Planning Considering Line Uprating

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.

Transmission Network Dynamic Planning Based on a Double Deep-Q Network With Deep ResNet

Based on a Double Deep-Q Network with deep ResNet (DDQN-ResNet), this paper proposes a novel method for transmission network expansion planning (TNEP). Since TNEP is a large scale and mixed-integer linear programming (MILP) problem, as the transmission network scale and the optimal constraints increase, the numerical calculation and heuristic learning-based methods suffer from heavy computational complexities in calculation and training. Besides, due to the black box characteristic, the solution processes of the heuristic learning-based methods are inexplicable and usually require repeated training. By using DDQN-ResNet, this paper constructs a high-performance and flexible method to solve large-scale and complex-constrained TNEP problem. Firstly, we form a two-objective TNEP model, in which one objective is to minimize the comprehensive cost, and another objective is to maximize the transmission network reliability. The comprehensive cost takes into account the expansion cost, the network loss cost, and the maintenance cost. The transmission network reliability is evaluated by the expected energy not served (EENS) and the electrical betweenness. Secondly, from the TNEP model, the TNEP task is constructed based on the Markov decision process. By abstracting the task, the TNEP environment is obtained for DDQN-ResNet. In addition, to identify the construction value of lines, an agent is establish based on DDQN-ResNet. Finally, we perform the static planning and visualize the reinforcement learning process. The dynamic planning is realized by reusing the training experience. The validity and flexibility of DDQN-ResNet are verified on RTS 24-bus test system.

An efficient hybrid metaheuristics optimization technique applied to the AC electric transmission network expansion planning

The transmission network expansion planning (TNEP) problem consists of determining the necessary infrastructure additions, within a planning horizon, to minimize an investment objective function while meeting some operational and physical constraints. Even using simplified models to represent the electric network, the TNEP becomes a very complex, combinatorial and non-convex optimization problem. In recent years, the full alternating current (AC) network model has been proposed to formulate the TNEP problem. Due to its complexity, more robust and efficient optimization techniques to solve the AC formulation are required. This paper proposes a new effcient hybrid metaheuristic technique to solve the TNEP problem. Additionally, it presents a comprehensive comparative study including different powerful conventional, emerging and hybrid optimization metaheuristics techniques applied to solve the static, long-term TNEP problem, using the AC model, considering both operating and reactive power compensation costs. Simulation results are shown for three test systems: Garver 6-bus system, IEEE 24-bus system and the IEEE 118-bus system.

Relaxations and approximations of HVdc grid TNEP problem

In recent years, the use of HVdc technology has increased and VSC technology has enabled the realization of HVdc grids. This calls for the development of new tools to solve the transmission network expansion planning (TNEP) model. A detailed representation of the dc grid TNEP problem is highly nonlinear and more complex than the traditional ac grid expansion problem due to extra constraints and additional decision variables from the converter station model. The present day industrial solvers have difficulties to tackle the resultant MINLP problem. Therefore, the linearized ‘DC’ approximation is often used in practice, which may not produce sufficiently accurate answers. In this paper, different relaxations and approximations of the dc grid TNEP problem are presented. The performance of each formulation is evaluated using eight test cases. Although there is no clear formulation that shows the best performance, LPAC approximation and SOC relaxations seem to provide better alternatives to ‘DC’ approximations. The developed formulations do not guarantee feasibility and a second corrective stage is required to obtain feasible solutions.

A two-stage constructive heuristic algorithm to handle integer investment variables in transmission network expansion planning

Due to the complexity and great relevance of the transmission network expansion planning (TNEP) for electrical systems, this topic remains on the focus of the academic and industry communities. Therefore, this paper proposes a new approach to deal efficiently with the basic formulation of this problem, combining low computational effort and good quality of the obtained solutions. In this approach four factors contribute to solve TNEP problem more efficiently: (i) the investment decisions are selected using a new Constructive Heuristic Algorithm (CHA); (ii) the proposed CHA includes two stages, using the relaxation of the decision integers variables through the hyperbolic tangent function and the setting of its function's slope; (iii) the performance index that was adopted was modified regarding what was reported in the literature; (iv) the use of the primal–dual interior point optimization technique allows the representation of the nonlinearities in the problem: transmission power losses and the hyperbolic tangent function (investment decision). The quality and effectiveness of the proposed algorithm is verified using two real power systems, where the proposed CHA is able to lead to better quality solutions than the ones reported in the literature.

Robust transmission expansion planning with uncertain generations and loads using full probabilistic information

Recent advances in Transmission Network Expansion Planning (TNEP) have demonstrated that two-stage adaptive robust optimization (ARO) renders the expansion planning problem tractable for real systems and, at the same time, constitutes a relevant approach to deal with uncertain demand and generation capacity in the TNEP problem. However, the use of cardinality and polyhedral uncertainty sets does not consider the correlation of uncertain parameters and makes the interpretation of final results difficult from a probabilistic view-point. The recent use of level II structural-reliability and decomposition techniques allows the incorporation of ellipsoidal uncertainty sets, which permits the use of expected values and the variance-covariance matrix of uncertain parameters (First-Order Second-Moment, FOSM). However, it also constitutes an approximation. This paper presents an iterative method which, using FOSM and appropriately modifying the reliability index associated with the desired objective function quantile, transforms the model into a level III structural-reliability method that allows the consideration of the complete probabilistic structure of the uncertain parameters. Numerical results from a realistic case study are presented and discussed.

Reliability-constrained dynamic transmission expansion planning considering wind power generation

This paper proposes a two-stage framework to solve the long-term transmission network expansion planning (TNEP) problem ensuring user-defined reliability levels and wind curtailment over the planning horizon. In the first stage, the static TNEP (S-TNEP) problem is solved to define where a set of new lines must be installed at the end of planning horizon. In the second stage, a multistage procedure is performed to solve the dynamic TNEP (D-TNEP) in order to determine the moment that each transmission line should be built. Both S-TNEP and D-TNEP are decomposed into investment decision and performance assessment through a bi-level scheme based on Benders decomposition; thus, the reliability is considered and evaluated over the planning process. The reliability and performance indexes are obtained through the non-chronological Monte Carlo simulation considering the random behavior of transmission lines failures, load fluctuations and uncertainties over wind availability. The loss of wind probability performance index is introduced to measure the probability of wind curtailment in each connection point of the system, and it is used to prevent wind farms from being underused. The proposed methodology is tested in modified versions of the 24-bus IEEE reliability test system and the IEEE 118-bus test system.

Transmission Network Expansion Planning Considering Line Switching

Commonly, in the transmission network expansion planning (TNEP) problem, the objective is to define the minimal-cost investment in new transmission devices, e.g., the transmission lines and transformers, for the system to operate adequately in a planning horizon. In addition to the traditional approach of considering the installation of new components, the presented model considers the enhancement of the system operation performance through the modification of its topology in each operation scenario. This modification is made by switching off the lines that degrade the system's performance. By using an illustrative example, we discuss why congestion is eliminated by switching off specific lines from the system, thereby improving the performance of the existing network and constructed candidate lines and leading to lower investment costs. The proposed method also considers a strategy to reduce the number of lines switched off in order to improve the reliability of the system and avoid alternative optimal solutions with unnecessary lines switched. The tests are carried out using the 46-bus southern Brazilian system and the IEEE 24-bus system, which are usually used as benchmarks for the TNEP problem. The results are compared with the solutions of the traditional model for TNEP to show the efficiency of the proposed approach.

TNEP of meshed HVDC grids: ‘AC’, ‘DC’ and convex formulations

With the increased utilisation of high-voltage direct current (HVDC) links in power systems, it is necessary to upgrade the traditional ac grid focused transmission network expansion planning (TNEP) problem to accommodate multiple HVDC links and even HVDC grids. This study proposes a non-linear formulation for the TNEP problem of dc grids and derives two relaxations of the problem: the linear approximation, the second-order cone convex relaxation as a branch flow model. The presented formulations allow both monopolar and bipolar configurations, and this as point-to-point connections and HVDC grids as expansion candidates. The three formulations are examined on various test cases and compared for their accuracy and computational speed.

Security constrained transmission network expansion planning using grey wolf optimization algorithm

Transmission network expansion planning problem (TNEP) is an utmost necessary aspect of the power system. The TNEP is required to supply adequate power to consumer load demand in a reliable and economical manner. In this work, a recently developed meta-heuristic technique known as grey wolf optimizer (GWO) is applied to solve (N-1) contingency based security constrained TNEP (STNEP). The GWO is applied to this STNEP for solution of six bus Graver’s system and 46 bus Brazilian system. The accessed findings for STNEP by GWO are correlated with the other algorithms provided in the literature, which shows the potential of GWO algorithm in terms of optimum system cost for STNEP.

AC load flow based model for transmission expansion planning

This paper proposes an accurate model to solve the transmission network expansion planning (TNEP) problem. The TNEP problem is solved using a linear DC model, a mixed integer linear DC model, and the proposed accurate exact mixed integer non-linear AC model. A 19 bus system which is a part of the 220 kV Egyptian transmission network is used for the verification of the proposed load flow models. The proposed exact non-linear AC load flow based model that considered control of active and reactive power flow in the network and considered the power losses in the system yielded promising results when compared to DC load flow based linear and mixed integer linear models. It provided a planned network with least number of added lines and consecutively with the least cost.

Voltage stability constrained transmission network expansion planning using fast convergent grey wolf optimization algorithm

Transmission network expansion planning problem (TNEP) is of utmost importance in power system now a days. In this paper, a voltage stability indicator (L-index) is applied to limit the voltage of buses. Hence TNEP is formulated incorporating this voltage stability feature. Thus formulated TNEP is termed as voltage stability constrained TNEP (VSC-TNEP). TNEP has been solved using various conventional and non-conventional strategies by various researchers in these days. Here, TNEP and VSC-TNEP for IEEE-24 bus test system is solved using grey wolf optimization technique (GWO) and modified GWO. The modified GWO algorithm namely, fast convergent GWO (FCGWO) is validated over 25 standard benchmark test functions and compared with other techniques.

Transmission network expansion planning using state-of-art nature inspired algorithms: a survey

Transmission network expansion planning (TNEP) problem has been continuously solved for many years still the cost effective, reliable, and optimise solution is always desirable. The TNEP has been solved by various conventional and non conventional strategies. The strategy to find the solution of TNEP by classical mathematical optimisation techniques is tedious, slow and inefficient. In recent years, nature inspired algorithms (NIAs) have proven their importance to provide the solutions of the TNEP problem over classical mathematical optimisation techniques. This paper presents a review on the key contributions of the state-of-art NIAs to solve the TNEP problem. Further, the TNEP system specific significant works presented in the literature are summarised for easy understanding of the readers. The readers can get a brief description of the considered NIAs algorithms which has been applied to solve various systems of TNEP problem and they can also identify the significant NIA which is being applied for specific TNEP system.

A Comprehensive State-of-the-Art Survey on the Transmission Network Expansion Planning Optimization Algorithms

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.