Metaheuristics for Transmission Network Expansion Planning

This chapter presents the characteristics of the metaheuristic algorithms used to solve the transmission network expansion planning (TNEP) problem. The algorithms used to handle single or multiple objectives are discussed on the basis of selected literature contributions. Besides the main objective given by the costs of the transmission system infrastructure, various other objectives are taken into account, representing generation, demand, reliability and environmental aspects. In the single-objective case, many metaheuristics have been proposed, in general without making strong comparisons with other solution methods and without providing superior results with respect to classical mathematical programming. In the multi-objective case, there is a better convenience of using metaheuristics able to handle conflicting objectives, in particular with a Pareto front-based approach. In all cases, improvements are still expected in the definition of benchmark functions, benchmark networks and robust comparison criteria

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review

YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000

Transmission Expansion Planning for Large Power Systems

abstract: 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 dissertation investigates the topic of solving TEP problems for large power systems. The dissertation can be divided into two parts. The first part of this dissertation focuses on developing a more accurate network model for TEP study. First, a mixed-integer linear programming (MILP) based TEP model is proposed for solving multi-stage TEP problems. Compared with previous work, the proposed approach reduces the number of variables and constraints needed and improves the computational efficiency significantly. Second, the AC power flow model is applied to TEP models. Relaxations and reformulations are proposed to make the AC model based TEP problem solvable. Third, a convexified AC network model is proposed for TEP studies with reactive power and off-nominal bus voltage magnitudes included in the model. A MILP-based loss model and its relaxations are also investigated. The second part of this dissertation investigates the uncertainty modeling issues in the TEP problem. A two-stage stochastic TEP model is proposed and decomposition algorithms based on the L-shaped method and progressive hedging (PH) are developed to solve the stochastic model. Results indicate that the stochastic TEP model can give a more accurate estimation of the annual operating cost as compared to the deterministic TEP model which focuses only on the peak load.Dissertation/ThesisPh.D. Electrical Engineering 201

Hybrid Spatial-Artificial Intelligence Approach for Renewable Energy Sources Sites Identification and Integration in Sarawak State

As many new power infrastructures are planned under Sarawak State, the energy demand is expected to grow exponentially in these coming years. Besides, the minority of the rural villages are still not electrified yet. Fortunately, Sarawak State is blessed with indigenous Renewable Energy such as solar, hydro and wind power but they are scattered in the interior of the Sarawak State. Thus, the first phase is to develop a criteria scheme data for potential Renewable Energy Sources (RES) sites. It is followed by identifying RES sites using spatial data and Multi-Criteria Decision Making-Analytical Hierarchy Process (MCDM-AHP) algorithm. Accordingly, Spatial-Artificial Intelligence (AI) approach is utilised to integrate a high number of RES sites with minimum total distance. The research also proposed a hybrid Spatial-AI approach to integrate a high number of RES sites with minimum total distance and minimum total elevation difference. Initially, the Geographic Information System (GIS) tool is utilised to perform the assessments on current geographical conditions. From this, the spatial criteria scheme data is produced. The MCDM-AHP algorithm is applied to the criteria scheme data to identify the number of RES sites. Four cases were developed for RES sites integration, representing four different arrangements of RES sites. In each case, the Traveling Salesman Problem-Genetic Algorithm (TSP-GA) algorithm is applied to determine a minimum total distance of RES sites integration. Furthermore, a hybrid Spatial-Artificial Intelligence (AI) algorithm is proposed to integrate RES sites with minimum total distance and minimum total elevation difference. This research successfully identifies 55 solar energy sites and 15 wind energy sites. Meanwhile, 155 hydro energy sites were identified using the spatial map from Sarawak Energy Berhad (SEB). The second phase of the research work is to integrate the RES sites. TSP-GA algorithm is applied to generate the transmission line routing among the RES sites with minimum total distance. The minimum total distances in all four cases are acquired and validated as both the TSP-GA algorithm and the Traveling Salesman Problem-Mixed Integer Linear Programming (TSP-MILP) algorithm produced the same routing pattern. In the end, the proposed algorithm is successfully minimized the total distance and total elevation difference. The improved Spatial-AI algorithm showed approximately 15% better compared to ordinary TSP-GA in all four cases

Probabilistic Power Distribution Planning Using Multi-Objective Harmony Search Algorithm

In this paper, power distribution planning (PDP) considering distributed generators (DGs) is investigated as a dynamic multi-objective optimization problem. Moreover, Monte Carlo simulation (MCS) is applied to handle the uncertainty in electricity price and load demand. In the proposed model, investment and operation costs, losses and purchased power from the main grid are incorporated in the first objective function, while pollution emission due to DGs and the grid is considered in the second objective function. One of the important advantages of the proposed objective function is a feeder and substation expansion in addition to an optimal placement of DGs. The resulted model is a mixed-integer non-linear one, which is solved using a non-dominated sorting improved harmony search algorithm (NSIHSA). As multi-objective optimization problems do not have a unique solution, to obtain the final optimum solution, fuzzy decision making analysis tagged with planner criteria is applied. To show the effectiveness of the proposed model and its solution, it is applied to a 9-node distribution system

Toward a Smart Distribution System Expansion Planning by Considering Demand Response Resources

This paper presents a novel concept of "smart distribution system expansion planning (SDEP)" which expands the concept of demand response programs to be dealt with the long term horizon time. The proposed framework, integrates demand response resources (DRRs) as virtual distributed generation (VDG) resources into the distribution expansion planning. The main aim of this paper is to develop and initial test of the proposed model of SDEP to include DRRs which are one of the most important components to construct smart grid. SDEP is modeled mathematically as an optimization problem and solved using particle swarm optimization algorithm. The objective function of the optimization problem is to minimize the total cost of lines’ installation, maintenance, demand response persuasion, energy losses as well as reliability. Furthermore, the problem is subject to the constraints including radiality and connectivity of the distribution system, permissible voltage levels, the capacity of lines, and the maximum penetration level of demand response. Based on two sample test systems, the simulation results confirmed that the consideration of DRRs simultaneously with distribution system expansion can have economical profit for distribution planners

Approximate Dynamic Programming for Military Resource Allocation

This research considers the optimal allocation of weapons to a collection of targets with the objective of maximizing the value of destroyed targets. The weapon-target assignment (WTA) problem is a classic non-linear combinatorial optimization problem with an extensive history in operations research literature. The dynamic weapon target assignment (DWTA) problem aims to assign weapons optimally over time using the information gained to improve the outcome of their engagements. This research investigates various formulations of the DWTA problem and develops algorithms for their solution. Finally, an embedded optimization problem is introduced in which optimization of the multi-stage DWTA is used to determine optimal weaponeering of aircraft. Approximate dynamic programming is applied to the various formulations of the WTA problem. Like many in the field of combinatorial optimization, the DWTA problem suffers from the curses of dimensionality and exact solutions are often computationally intractability. As such, approximations are developed which exploit the special structure of the problem and allow for efficient convergence to high-quality local optima. Finally, a genetic algorithm solution framework is developed to test the embedded optimization problem for aircraft weaponeering

Incentive-Based Expansion Planning and Reliability Enhancement Models for Smart Distribution Systems

Due to the rapid progress toward the implementation of smart grid technologies, electric power distribution systems are undergoing profound structural and operational changes. Climate concerns, a reduction in dependency on fossil fuel as a primary generation source, and the enhancement of existing networks constitute the key factors in the shift toward smart grid application, a shift that has, in fact, already led power industry stakeholders to promote more efficient network technologies and regulation. The results of these advances are encouraging with regard to the deployment and integration of small-scale power generation units, known as distributed generation units (DGs), within distribution networks. DGs are capable of contributing to the powering of the grid from distribution or even sub-distribution systems, providing both a positive effect on network performance and the least adverse impact on the environment. Smart grid deployment has also facilitated the integration of a variety of investor assets into power distribution systems, with a consequent necessity for positive and active interaction between those investors and local distribution companies (LDCs). This thesis proposes a novel incentive-based distribution system planning (IDSP) model that enables an LDC and DG investors to work collaboratively for their mutual benefit. Using the proposed model, the LDC would establish a bus-wise incentive program (BWIP) based on long-term contracts, which would encourage DG investors to integrate their projects at the specific system buses that would benefit both parties. The model guarantees that the LDC will incur minimum expansion and operation costs while concurrently ensuring the feasibility of DG investors’ projects. The proposed model also provides the LDC with the opportunity to identify the least-cost solution among a combination of the proposed BWIP and traditional expansion options (i.e., upgrading or constructing new substations, upgrading or constructing new lines, and/or reconfiguring the system). In this way, the model facilitates the effective coordination of future LDC expansion projects with DG investors. To derive appropriate incentives for each project, the model enforces a number of economic metrics, including the internal rate of return, the profit-investment ratio, and the discounted payback period. All investment plans committed to by the LDC and the DG investors for the full extent of the planning period are then coordinated accordingly. The intermittent nature of both system demand and wind- and PV-based DG output power is handled probabilistically, and a number of DG technologies are taken into account. Several linearization approaches are applied in order to convert the proposed model into a mixed integer linear programming (MILP) model, which is solved using a CPLEX solver. Reliability of service in a deregulated power environment is considered a major factor in the evaluation of the performance of service providers by consumers and system regulators. Adhering to imposed obligations related to the enhancement of overall system reliability places a substantial burden on the planning engineer with respect to investigating multiple alternatives and evaluating each option from both a technical and an economical perspective. This thesis also proposes a value-based reinforcement planning model for improving system reliability while maintaining reliability metrics within allowable limits. The optimal allocation of tie lines and normally open switches is determined by this planning model, along with required capacity upgrades for substations and lines. Two hierarchical levels for system operation under contingencies, namely, the restoration process and islanding-based modes, are applied in the model. A probabilistic analytical model is proposed for computing distribution system reliability indices based on consideration of these two hierarchical operating levels and taking into account variations in system demand, DG output power, and the uncertainty associated with system components. Due to the nature and complexity of these kinds of problems, a metaheuristic technique based on a genetic algorithm (GA) is implemented for solving this model. This thesis also proposes a new iterative planning model for smart distribution systems in which system reliability is considered a primary component in the setting of incentive prices for DG owners. A new concept, called generation sufficiency for dynamic virtual zones, is introduced in the model as a means of enhancing reliability in areas that are subject to reliability issues. To avoid any contravention of operational security boundaries, DG capacity is represented by two components: normal DG operating capacity and reserve DG capacity. The MILP planning model is constructed in a GAMS environment and solved with the use of a CPLEX solver

Optimización binivel aplicada al problema de la planeación de redes eléctricas de media y baja tensión

Este documento presenta un modelo binivel para la planeación de redes de distribución de media y baja tensión, con penetración de generación distribuida (GD) en la red de baja tensión. El modelo binivel propuesto tiene en cuenta en los niveles superior e inferior, el planeamiento de las redes de media y baja tensión, respectivamente. Esta metodología considera como conflicto entre estos dos agentes (niveles superior e inferior) el tamaño y la localización de los transformadores de distribución (TD), es decir, la incidencia que tiene el flujo de potencia que circula de la red primaria a la secundaria. El principal objetivo de este enfoque es encontrar una solución global conjunta que permita obtener un equilibrio que beneficie el planeamiento de ambas redes. Los dos niveles involucran los costos de instalación y repotenciación de elementos nuevos y existentes (tramos de red, TD, subestaciones y GD) y el costo de las pérdidas de energía. Este problema binivel es formulado como un modelo no lineal entero mixto y es solucionado usando un algoritmo de búsqueda tabú (ABT). Para verificar la eficiencia de la metodología propuesta se emplean tres casos de estudio: i ) planeamiento integrado tradicional, ii ) planeamiento integrado binivel y iii ) planeamiento integrado binivel con GD en la red de baja tensión. Los resultados obtenidos muestran la importancia de considerar en los estudios de planeación de redes de distribución, la red primaria y secundaria de forma simultánea, lo cual permite encontrar respuestas con costos globales más bajos