Flexible Multi-Objective Transmission Expansion Planning with Adjustable Risk Aversion

Abstract

This paper presents a multi-objective transmission expansion planning (TEP) framework. Rather than using the conventional deterministic reliability criterion, a risk component based on the probabilistic reliability criterion is incorporated into the TEP objectives. This risk component can capture the stochastic nature of power systems, such as load and wind power output variations, component availability, and incentive-based demand response (IBDR) costs. Specifically, the formulation of risk value after risk aversion is explicitly given, and it aims to provide network planners with the flexibility to conduct risk analysis. Thus, a final expansion plan can be selected according to individual risk preferences. Moreover, the economic value of IBDR is modeled and integrated into the cost objective. In addition, a relatively new multi-objective evolutionary algorithm called the MOEA/D is introduced and employed to find Pareto optimal solutions, and tradeoffs between overall cost and risk are provided. The proposed approach is numerically verified on the Garver's six-bus, IEEE 24-bus RTS and Polish 2383-bus systems. Case study results demonstrate that the proposed approach can effectively reduce cost and hedge risk in relation to increasing wind power integration.

Scenario Selection for Iterative Stochastic Transmission Expansion Planning

“…The bilevel optimization model of the GEP problem is formulated as the following optimization model in Equations (1)- (4), where the upper level decision variable x represents generation investment and retirement decisions x g,b,y , ∀g, b, y, and the lower level decision variable p represents both dispatch decisions p g,b,t,y , ∀g, b, t, y and locational marginal prices p LMP b,t,y , ∀b, t, y.…”

“…For a given transmission plan and two elements (i.e., demand and fuel cost) of a given scenario, we propose the following Algorithm 1 to calculate the generation capacity element by solving the GEP problem in Equations (1)- (4). In this algorithm, we use L OPF (x) to denote the parametric lower level optimal dispatch problem with a given generation investment decisionx:…”

“…There are also service companies, called regional transmission organizations in the U.S., that own no infrastructure, but are responsible for coordinating the planning function. Both of these organizations must solve the transmission expansion planning (TEP) problem under uncertainty regarding technology, quantity, location, and timing of generation investments [1][2][3][4]. Robust and stochastic optimization approaches are two major modeling techniques for transmission planning under uncertainty, which have their own strengths and limitations.…”

Interactive Energy Management for Networked Microgrids with Risk Aversion

“…Incentives will be paid to customers who are willing to increase or reduce their energy consumption when requested. The controllable load cost is represented by a linear function as [18]:…”

A Determination of PV and Wind Power Plant Penetration into a Power System

“…Load following analysis, ramping requirement analysis, day ahead error forecast and supporting technology option were described on that study. A new planning criterion for including the intermittency risk has been proposed in [7]. RES generation Levelized Cost and Levelized Avoided Cost of New Generation Resources were presented in [8].…”