Transmission network expansion planning with stochastic multivariate load and wind modeling

The increasing penetration of intermittent energy sources along with the introduction of shiftable load elements renders transmission network expansion planning (TNEP) a challenging task. In particular, the ever-expanding spectrum of possible operating points necessitates the consideration of a very large number of scenarios within a cost-benefit framework, leading to computational issues. On the other hand, failure to adequately capture the behavior of stochastic parameters can lead to inefficient expansion plans. This paper proposes a novel TNEP framework that accommodates multiple sources of operational stochasticity. Inter-spatial dependencies between loads in various locations and intermittent generation units' output are captured by using a multivariate Gaussian copula. This statistical model forms the basis of a Monte Carlo analysis framework for exploring the uncertainty state-space. Benders decomposition is applied to efficiently split the investment and operation problems. The advantages of the proposed model are demonstrated through a case study on the IEEE 118-bus system. By evaluating the confidence interval of the optimality gap, the advantages of the proposed approach over conventional techniques are clearly demonstrated

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

Multiobjective Optimization Model for Wind Power Allocation

There is an increasing need for the injection to the grid of renewable energy; therefore, to evaluate the optimal location of new renewable generation is an important task. The primary purpose of this work is to develop a multiobjective optimization model that permits finding multiple trade-off solutions for the location of new wind power resources. It is based on the augmented -constrained methodology. Two competitive objectives are considered: maximization of preexisting energy injection and maximization of new wind energy injection, both embedded, in the maximization of load supply. The results show that the location of new renewable generation units affects considerably the transmission network flows, the load supply, and the preexisting energy injection. Moreover, there are diverse opportunities to benefit the preexisting generation, contrarily to the expected effect where renewable generation displaces conventional power. The proposed methodology produces a diverse range of equivalent solutions, expanding and enriching the horizon of options and giving flexibility to the decision-making process

Low-carbon reliable transmission expansion planning with large-scale renewable energy integration.

Modern electricity systems are changed by the following factors: the development of emerging technologies including renewable energy, carbon capture, power electronics devices, the participation of the demand side in the electricity market; the retirement of aging coal-fired power plants (CFPP); and the implementation of carbon policies. Under the pressure of these changes, transmission systems require augmentations and upgrades to achieve operation safety and reliability requirements. New electricity network planning methods need to be developed to address the above changes. In this research study, the traditional transmission expansion planning (TEP) methods have been improved to adapt to the above changes from three aspects, namely economics, risks, and carbon emissions. To reduce the cost of planning, non-network solutions are coordinated in the TEP model. In terms of the low-carbon transformation: the TEP model is used for considering the CFPP retrofit with post-combustion carbon capture (PCC); while CFPP retirement and replacement models are proposed for aging CFPP. The Pareto optimality of aging CFPP retirement and replacement among three conflicting objectives including carbon emissions, total expenditure, and the operation risks are solved. Moreover, the effect of carbon policies including the carbon tax and carbon trading on TEP are tested. To address the reliability issues, a probability reliability assessment method, a renewable ramping cost model, and a novel risk index are developed to assess the risk in the power systems considering the large integration of renewable energy. The effectiveness of the proposed planning methods has been demonstrated in a few benchmark test systems. Simulations have been used to assess the efficiencies and advantages of each approach. This research study can be used to guide the low- carbon transformation of the electricity systems and it can give suggestions to system planners, power generation companies, and policy makers

Optimal composite generation and transmission expansion planning considering renewable energy sources, harmonics and system reliability

Abstract: Electricity generation via conventional generating systems is faced with challenges such as emission of greenhouse gases, uncertainty associated with fossil fuel prices and incidences of failure or outages of generation. This dissertation proposes the incorporation of largescale Renewable Energy Sources (RES) into power systems in order to reduce the challenges associated with conventional generating systems. The first part of this dissertation investigates the contribution of RES and economic incentives to the composite generation and transmission expansion planning procedure. A Mixed Integer Quadratic Programming (MIQP) model based on composite generation and transmission expansion planning is proposed for solving a multi-objective mathematical optimization problem which includes minimization of the investment costs, operational costs, emissions as well as the maximization of economic incentives. Obtained results, when compared with previous works indicate that encouraging economic incentives improves the effective utilization of offshore wind farms and consequently reduces the emissions from conventional generating units....D.Phil. (Electrical and Electronic Engineering

Contingency Management in Power Systems and Demand Response Market for Ancillary Services in Smart Grids with High Renewable Energy Penetration.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017