A Novel Salp Swarm Algorithm for Expansion Planning with Security Constraints

Transmission expansion planning using multivariate interpolation

This paper studies transmission expansion planning (TEP) problem considering effects of wind farms on transmission network congestion, power system reliability, line repair, and operational costs of wind and generating units in a deregulated environment. Transmission congestion is modeled using difference between local marginal prices (LMPs) of start and end buses of each line that is multiplied by its power flow. In this way, wind units can efficiently affect network congestion through decrease in lines loading and LMPs of buses. Transmission system reliability is modeled using load shedding (LS) index as well as outage rate and outage duration of a single line or simultaneous outage of a line and a wind unit. Also, generation reliability is formulated using loss of load, value of lost load, and LS criteria due to outage rates and outage durations of generating and wind units considering line limits. In addition, the effect of wind units on repair cost of transmission lines is modeled by the relationship among wind generation, line failure rates and lines loading. Operational cost includes cost of network losses, pollution penalty, fuel expenses, and generation cost of wind units. In order to more efficiently investigate the effect of wind power on TEP problem, the proposed model was tested on the IEEE Reliability Test System (IEEE RTS) for different scenarios, and the results were discussed.

The rapidly increasing power demand is a challenging problem in power sector. Generation of power with limited fossil fuel resources is also a complex task. The operation and control of power system includes several interconnected activities. Transmission line expansion is a solution to cater aggressive load demand by adding transmission circuits. The expansion plan should be cost effective and viable. In recent years, expansion problem has been solved with the help of optimization algorithms. By taking motivation from this fact, in this paper first various aspects and solution techniques of the transmission expansion planning are described and then application of Teaching learning based optimization (TLBO) algorithm is reported to solve Transmission Expansion Planning (TEP) problem. The efficacy of TLBO is tested on two standard bus systems namely graver’s 6 bus system and Brazilian bus system. The performance of TLBO found competitive with contemporary optimizers. It has been observed that TLBO outperforms all.

Network Expansion Planning (NEP) plays a pivotal role in the development of power systems. It involves investing in new generating units and transmission lines to meet growing load demands and ensure a reliable electricity supply. Historically, the incorporation of demand response (DR) factors in power system planning has been limited due to their complexity and evaluation challenges. However, with advancements in smart grid technologies, increased integration of renewable energy, and the emergence of flexible loads, the inclusion of DR models has become crucial for enhancing power system reliability. While numerous studies have delved into generation and transmission expansion planning (GTEP) problems, only a few have explored the integration of network payment schemes and DR within the GTEP framework. This study proposes a multi-annual generation and transmission expansion planning model that incorporates three network payment schemes and two DR techniques. The objective is to secure financing for new generating units and transmission lines while minimizing the overall system cost. The proposed models employ the mixed-integer linear programming (MILP) optimization method and are validated using a modified IEEE 24-bus system. Two key system performance metrics, namely the network congestion index and network saturation index, are employed to assess system reliability and effectiveness. These results demonstrate that the integration of network payment schemes and DR techniques into the generation and transmission expansion planning model can lead to a cost reduction of 32.1% as compared to base model, reduced power system congestion and saturation (22.1%, 2.73%) to allow more renewable energy integration and enhanced power system reliability and operational flexibility.

The expected deployment of massive renewable energy sources (RES, mainly of wind and solar) at a continental or even intercontinental level creates very complex transmission expansion planning (TEP) problems. Both the size of the system and the level of uncertainty are huge. Solving a TEP problem for such big networks under high levels of uncertainty demands an exceptionally huge computational effort when using reasonably precise models. Conventional modeling approaches and solution strategies cannot be directly reproduced in this case due to their computational limitations. In this paper, we show a systematic way of approaching the problem, which deals both with modeling and with solution strategy aspects. We formulate the TEP problem as a two period stochastic linear programming problem characterized by common investment decisions to all scenarios in the first period and scenario-dependent decisions in the second period. In order to make it tractable, we devise a solution strategy based on decomposing the problem into successive optimization phases. Each phase uses the results of the previous one to reduce the search space. This reduction in complexity allows each phase to use more complex models with a similar computational load. Each optimization phase could be defined and solved as an independent problem, thus, allowing the use of specific decomposition techniques, or parallel computation when possible. A modified Garver's system is used to illustrate the methodologies. Test results from IEEE-300 bus system show that the proposed solution strategy is very effective. And, integrating the proposed solution strategy in the solution process contributes to a significant reduction in computational effort while fairly maintaining optimality of the solution.

Purpose The purpose of this study is to address the efficiency of power losses representation while still reducing the computational burden of an optimal power flow (OPF) model in transmission expansion planning (TEP) studies. Design/methodology/approach A modified TEP model is formulated with inclusions of linearized approximation of power losses for a large-scale power system with renewable energy sources. The multi-objectives function determines the effect of transmission line losses on the optimal power generation dispatch in the power system with and without inclusion of renewable energy sources with emphasis on minimizing the investment and operation costs, emission and the power losses. Findings This study investigates the impact of renewable energy sources on system operating characteristics such as transmission power losses and voltage profile. Sensitivity analysis of the performance for the developed deterministic quadratic programming models was analyzed based on optimal generated power and losses on the system. Research limitations/implications In the future, a comparison of the alternating current OPF and direct current (DC) OPF models based on the proposed mathematical formulations can be carried out to determine the efficiency and reduction of computation process of the two models. Practical implications This paper proposed an accurate way of computing transmission losses in DC OPF for a TEP context with a view of achieving a minimal computation time. Originality/value This paper addresses the following objectives: develop a modified DC OPF with a linearized approximation of power losses in TEP problem with large integration of RES. Investigate the impact of RES on system operating characteristics such as transmission power losses and voltage profile.

This paper shows a methodology for solving the Transmission Expansion Planning (TEP) problem when Multiple Generation Scenarios (MGS) are considered. MGS are a result of the market based environment introduced by electricity deregulation. The solution to this problem is carried out by using multiobjective evolutionary strategies for the optimization process, implementing a new hybrid modified NSGA-II/Chu-Beasley algorithm. The proposed methodology is validated using the 6-bus Garver system and the IEEE-24 bus system. The TEP is based on the DC model of the network and non-linear interior point method is used to initialize the population. A set of Pareto optimal expansion plans with different levels of cost and load shedding is found for each system, showing the robustness of the proposed approach.

The two previous chapters describe and analyze the generation expansion planning (GEP) problem as well as the joint generation and transmission expansion planning (G&TEP) problem, both from the perspective of a central planner that determines the expansion plan that is most beneficial for the system as a whole. Unlike the problems in those two chapters, referred to as centralized expansion problems, this chapter addresses a market-oriented generation investment problem, in which a single power producer competing in an electricity pool seeks to maximize its own profit through making the best generation investment decisions. It is important to note that a market-oriented decision-making problem is generally more complex than a centralized one, since the former is subject to additional uncertainty related to the behavior of other producers (rivals). In this chapter, we provide and analyze different investment models that progressively incorporate additional details with emphasis on solution techniques.

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.

This study presents a new binary differential evolution algorithm for probabilistic AC/DC transmission expansion planning (TEP). The probabilistic model would consider the uncertainty of distant wind/solar energy resources as well as load demand. In many power systems, the necessity of considering the impacts of HVDC links on the TEP studies is inevitable. Therefore, in this study, these links are considered a substitute candidate for the conventional HVAC transmission lines. The stochastic modelling and integration of HVDC links for TEP problem have introduced new challenges to transmission system planners. To overcome these problems, an efficient fuzzy set‐based optimisation technique is used to solve the multi‐objective TEP problem. Finally, the proposed method is applied to the test system consisting of 24 buses.

With increasing penetration of renewable energy sources and electric vehicles (EVs) in power grids, developing appropriate models for power system planning is of great importance. This article proposes a novel gray wolf optimization (GWO) algorithm-based transmission expansion planning (TEP) method considering renewable energy sources and EVs and evaluates the performance of alternating current power flow (ACPF) and direct current power flow (DCPF) models in the TEP problem in terms of accuracy, run-time, and objective functions. The uncertainty associated with renewable energy sources, EVs, and loads are explicitly modeled in this study using probability density functions (pdfs) and the Copula approach. The proposed GWO-based TEP method is validated by case studies using the IEEE 24-bus reliability test system (RTS) and IEEE 118-bus test system. The ACPF model leads to fewer transmission lines and lower costs than the DCPF model in most scenarios.

Transmission expansion planning (TEP) is a complex decision making process that requires comprehensive analysis to determine the time, location, and number of electric power transmission facilities that are needed in the future power grid. This chapter discusses the TEP problem in modern power systems, including the TEP process, available software tools, and mathematical models for TEP problems.

This paper proposes an improved harmony search (HS) algorithm for the solution of the static transmission expansion planning (TEP) problem with security constraints. The modification of this method in comparison to the original harmony search algorithm includes the introduction of a variable bandwidth in the memory consideration and the pitch adjustment phase for the selection of the new harmony vector (NHV) in the improvisation step of the algorithm. In that way, if previous harmonies are considered for the NHV, the algorithm allows only the best harmonies of the harmony memory in respect of objective function value to participate in the improvisation of the NHV. The results from the application of the proposed method on IEEE 24-bus reliability test system demonstrate the potentials and the practicality of the proposed HS algorithm. (6 pages)

The transmission expansion planning (TEP) problem in modern power systems is a large-scale, mixed-integer, non-linear and non-convex problem. Although remarkable advances have been made in optimization techniques, finding an optimal solution to a problem of this nature can still be extremely challenging. Based on the linearized power flow model, this paper presents a mixed-integer linear programming (MILP) approach that considers losses, generator costs and the N - 1 security constraints for the multi-stage TEP problem. The losses and generator cost are modeled as piecewise linear functions of the line flows and the generator outputs, respectively. The IEEE 24-bus system is used to compare the lossy and the lossless model. The results show that the lossy model provides savings in total cost in the long run. The selection of the best number of piecewise linear sections L is also shown. Then a complete planning framework is presented and a multi-stage TEP is performed on the IEEE 118-bus test system. Simulation results show that the proposed approach is accurate and efficient, and has the potential to be applied to large-scale power system planning problems.

Metaheuristic techniques are enormously being used nowadays for their applications in field like Power system and are being applied to many optimization problems of power system due to difficult approach of classical methods. Transmission expansion planning (TEP) is a challenging task to deal in power system. A new approach, hybrid genetic algorithm particle swarm optimization (HGAPSO) for solving TEP problem, is introduced to eliminate the drawback of GA and PSO. Problems of immature convergence in particle swarm optimization (PSO) and low convergence speed in genetic algorithm (GA) mitigate to the hybridization of both techniques. Proposed HGAPSO Algorithm is tested for three standard electric test systems for TEP problem in MATLAB tool. Experimental results found by HGAPSO are compared with GA and PSO Algorithm to test its performance for TEP problem.