Two‐stage algorithm for efficient transmission expansion planning with renewable energy resources

Abstract

In this study, a ‘two‐stage’ deterministic algorithm an for efficient transmission expansion planning with renewable energy (RE) resources under the assumption that existing conventional generators provide the reserve to mitigate RE generation forecast error has been proposed. Zero‐RE penetration has been considered as a ‘reference scenario’, as well as cost‐minimisation objective has been considered as a planning criterion in Stage 1. In the proposed algorithm, Stage 2 is required to be solved only if the network performance degrades in relation to the reference scenario. In Stage 2, congestion cost is also incorporated as a sub‐objective. Here, the locational marginal prices of the unit scheduling problem obtained from the solution of Stage 1 have been used to calculate the congestion cost. In addition to being computationally tractable, the proposed algorithm guarantees performance improvement in the network. The planning horizon is divided into smaller blocks to facilitate delayed investment. When implemented on an IEEE 24‐bus reliability test system, the proposed algorithm generated minimum cost plan with better performance.

A Multi-objective Hybrid Optimization for renewable energy integrated Electrical Power Transmission Expansion Planning

“…Table 7 shows the final expansion plan obtained by the methodology proposed. Table 8 compares the generation capacity obtained before and after TEP with GWGA methodology at different nodes and Table 9 shows the nodes where the renewable energy sources [51][52][53][54][55] are connected for optimal operation of the proposed work.…”

A Hybrid Genetic Algorithm Applied to the Transmission Network Expansion Planning Considering Non-conventional Solution Candidates

“…In this regard, there are studies that have separately considered the inclusion of series compensation [10], shunt compensation [11], repowering and reconfiguration of existing circuits [12] - [14], change in voltage levels and technology of conductors in certain circuits [15]. Furthermore, another aspect that has gained relevance in the evolution of the models is the integration of the previously mentioned solution techniques with new methodologies, to consider aspects of electricity markets [16], financial and economic evaluation [17], regulatory mechanisms [18], smart grids [19], generation with renewable resources [20], and demand response [21] among others.…”

Security constrained AC transmission network expansion planning

“…It is computationally very challenging due to its large dimensionality and mixed-integer nature. Numerous methods and techniques have been utilized for solving this problem [1]- [20] and [22]- [27]. However, most of the planning studies carried out in recent past focus on simplistic DC solution of the problem [1]- [10].…”

“…As only some selective network contingencies are tested for the TNEP against all possible network contingencies, the resultant planning is much susceptible to produce infeasible network operations in at least a few of such omitted contingencies. A colony size of the modified artificial bee colony (MABC) algorithm ℎ number of neighbours in the MABC algorithm maximum iterations per trial trial limit for generation of scout bees dimension size of the TNEP problems factor to control the effect of global optimum on a bee movement time required per trial number of fitness function evaluations required to obtain a solution two-stage algorithm has been proposed in [27] for solution of ACTNEP with high penetration of renewable energy resources. Here, objective function considers both investment as well as operational costs.…”

“…Similarly, in [26], a multi-objective ACTNEP is solved with evolutionary algorithm for some selective network contingencies based on the corrective control risk index value. As only some selective network contingencies are tested for the TNEP against all possible network contingencies, the resultant planning is much susceptible to produce infeasible network operations in at least a few of such omitted contingencies number of fitness function evaluations required to obtain a solution two-stage algorithm has been proposed in [27] for solution of ACTNEP with high penetration of renewable energy resources. Here, objective function considers both investment as well as operational costs.…”