Multi-national transmission planning using joint and disjoint solutions

Abstract

Abstract-Transmission systems have a critical role in wellfunctioning of liberalised electricity markets. Diversity in generation portfolio and improving system reliability are the main incentives for significant cross-border trades between Transmission System Operators (TSOs) in Europe. Nowadays, two ways are discussed for investment in additional transmission capacity by multiple TSOs: (1) Cooperative approach and (2) non-cooperative approache. In cooperative planning, the governing body looks for the highest overall social welfare while in non-cooperative approach, each TSO decides on his own transmission system based on his social welfare. The problem is that the outcome of these two approaches might be different. Each TSO tries to maximise his own social welfare and they are reluctant to act in a joint solution where they are losing some of their social welfare. In the context, they prefer to act strategically in order to maximise their social welfare. This paper evaluates the joint and disjoint solutions of the multi-national transmission planning. We propose a simultaneous-move game between the involved TSOs. The game is formulated as a one-shot mixedinteger linear programming problem. Then the joint solution is developed by merging all TSOs in one TSO responsible for transmission planning in all TSOs' regions. The joint solution is formulated as a mixed-integer linear programming and used as the benchmark in our studies.

Transmission Investment Coordination using MILP Lagrange Dual Decomposition and Auxiliary Problem Principle

“…In our numerical study, we have analyzed an illustrative two-region network and a larger and more general three-region system. The two-region network results intuitively analyzed and validated our incentive mechanism approach, which includes a TPC, compared to the game-theoretic Nash equilibrium approach that does not have a TPC [13,11,15]. We showed that our proposed incentive mechanism indeed reached the Pareto optimal solution which is also the socially optimal solution.…”

“…The authors have studied the impact of this free-riding on congestion revenues. In the reference [13], the authors have tried to improve the social welfare as presented in [11] by adopting different solution approaches, whereas in [14], the multi-regional transmission planing has been explored in the presence of wind generation and its associated uncertainty. References [15], [16], and [17] have explored the transmission expansion planning in the presence of generation expansion planning considering proactive and reactive coordination methods.…”

Transmission Investment Coordination using MILP Lagrange Dual Decomposition and Auxiliary Problem Principle

“…In our numerical study, we have analyzed an illustrative two-region network and a larger and more general three-region system. The two-region network results intuitively analyzed and validated our incentive mechanism approach, which includes a TPC, compared to the game-theoretic Nash equilibrium approach that does not have a TPC [31,32,36]. We showed that our proposed incentive mechanism indeed reached the Pareto optimal solution which is also the socially optimal solution.…”

“…The authors have studied the impact of this free-riding on congestion revenues. In the reference [31], the authors have tried to improve the social welfare as presented in [32] by adopting different solution approaches, whereas in [33], the multi-regional transmission planing has been explored in the presence of wind generation and its associated uncertainty. References [36], [34], and [35] have explored the transmission expansion planning in the presence of generation expansion planning considering proactive and reactive coordination methods.…”