Integration of renewable energy resources into the power system has increased the financial and technical concerns for the market-based transmission expansion planning. This paper proposes a stochastic framework for transmission grid reinforcement studies in a power system with wind generation. A multi-stage multi-objective transmission network expansion planning (TNEP) methodology is developed which considers the investment cost, absorption of private investment and reliability of the system as the objective functions. A non-dominated sorting genetic algorithm (NSGA II) optimization approach is used in combination with a probabilistic optimal power flow (POPF) to determine the Pareto optimal solutions considering the power system uncertainties. Using a compromise-solution method, the best final plan is then realized based on the decision-maker preferences. The proposed methodology is applied to the IEEE 24-bus Reliability Tests System (RTS) to evaluate the feasibility and practicality of the developed planning strategy.

A Multi-Objective Transmission Expansion Planning Framework in Deregulated Power Systems With Wind Generation

Efficient and well-timed investment in distributed generation (DG) in distribution networks in the electricity market environment presents a big challenge for distribution companies. In this study, a real option valuation framework is proposed to determine the optimal investment strategies for DG including the investment location, size, and timing. Within the proposed framework, the profit from investing in DG is modelled, where the benefits include the operation cost savings and capacity update deferral benefit compared with a no-DG-investment scenario over the study period. Future power demands and electricity prices are modelled as stochastic variables. The candidate DG investment plans are considered as multiple mutually exclusive options, and the corresponding managerial flexibility for seizing opportunities and mitigating risk of loss upon an unfavourable unfolding of future uncertainties is assessed with real option analysis using the extended least square Monte Carlo method. The distribution of future investment strategies and optimal initial investment threshold levels under various scenarios are analysed in a case study to demonstrate the characteristics of the proposed framework and methodology.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2016.0541

Optimal investment strategies for distributed generation in distribution networks with real option analysis

Summary

In market-based planning studies, the financial aspects of transmission grids have been posed as a major challenge since these are important keys to provide a non-discriminative and competitive environment for energy market participants. In this respect, this paper proposes a probabilistic multi-objective transmission expansion planning algorithm considering private investors' preferences, technical concerns, and random nature of power system uncertainties. The combination of Non-Dominated Sorting Genetic Algorithm II, as a widely-used robust multi-objective optimization technique, and point estimation method is employed to handle non-commensurable probabilistic objective functions. The IEEE Reliability Test System is taken into consideration as the test system to verify and address the different aspects of the proposed method. Copyright © 2014 John Wiley & Sons, Ltd.

Probabilistic multi‐objective transmission investment and expansion planning

Given the ambitious, politically-driven wind energy agenda in some U.S. States (e.g., California and Texas) and in Europe (e.g., Germany and Spain), adequate regulatory instruments are needed that provide incentives for additional generation capacity and transmission expansion. This paper analyzes the impact of wind energy extension scenarios in 2020 on the European high voltage grid, using a nodal pricing mechanism and assuming expanded wind generation capacity. Our analysis is based on a DC Load Flow network model that is implemented in GAMS. The results show that the necessary network extensions mostly arise from existing congestion, particularly between countries, and that additional wind capacity can be integrated with relatively little effort. We conclude that the regulatory implications of additional feeding-in of wind energy are less critical than often asserted.

https://mpra.ub.uni-muenchen.de/65655/1/wp_em_31_Leuthold_etal_EU_wind.pdf

When the Wind Blows Over Europe: A Simulation Analysis and the Impact of Grid Extensions

Cooperation under uncertainty: Assessing the value of risk sharing and determining the optimal risk-sharing rule for agents with pre-existing business and diverging risk attitudes

This paper introduces the concept of perpetual options theory to analyze merchant transmission investment through an approved rate. Since the electric usages and the associated revenue is stochastic in nature, applying the perpetual options theory allows an investor to determine the most opportunistic time to start a transmission project and obtain the maximum revenue returns or let the option expire when the economic incentive is not sufficient. In today's environment, this decision approach is more appropriate since it provides transmission investors a better projection of its return on investment and an exit strategy for an investment decision.

H. Salazar

Papers

Decision Analysis of Merchant Transmission Investment by Perpetual Options Theory