Merchant transmission planning in smart paradigm with introducing demand response aggregator in wholesale market

Abstract

SUMMARYWith introducing demand response aggregator (DRA) in smart paradigm, small customers can actively participate in price and incentive-based demand response programs. This new matter can significantly affect many factors of power system such as transmission network security. Accordingly, it is notably useful to define an adjusted framework for transmission expansion planning in smart environment. A long-term market simulation is performed using a tri-level iterative framework to find the best expansion decisions for transmission company (TransCo) in a pool-based market. The TransCo's investment decisions are made by a merchant approach consistent with transmission network security and smart environment. The effects of smart environment on future network configuration, strategic bidding of generators in the market operation, and contingency analysis are considered during planning process. The effectiveness of proposed method is examined on the IEEE 24-bus system with one DRA, one TransCo, and two generation companies under the independent system operator's supervision.

Sub-Transmission Network Expansion Planning Considering Regional Energy Systems: A Bi-Level Approach

“…In this paper, a number of fuzzy sets were defined for all of the RGES's profit functions and REC's costs we calculated according to Equations (22) and (23), respectively. It was assumed that maximum and minimum values of profit functions of RGES (i.e., PF max i and PF min i ) and cost functions (i.e., C max j and C min j ) of REC could be predicted.…”

“…It was assumed that maximum and minimum values of profit functions of RGES (i.e., PF max i and PF min i ) and cost functions (i.e., C max j and C min j ) of REC could be predicted. When the individual's profit and cost range are taken into account, the membership function µ(PF) and µ(C) for each objective function can be determined by Equations (22) and (23) [36]:…”

“…In this subsection, apart from the constituent elements of the OF RGES and OF REC , the equilibrium point of investment in view point of RGES and REC's investor were obtained using the FSM method described in Section 3.2. Using Equations (22) and (23), the fuzzy values of RGES profits and REC costs are provided in Tables 5 and 6. Using Equation (24), Pareto (10) is the equilibrium point for RGES and REC.…”

“…However, in practice, the investors in generation companies, transmission companies, and RGESs make expansion planning decisions based on profits [18][19][20]. Optimal GEP and TEP problems are also addressed in [21,22], respectively, considering the role of demand response (DR) aggregators. A model for congestion and investment reduction is proposed in [23], while a security-based co-planning of transmission line expansion and energy storage facilities is introduced in [24].…”

Incorporating energy storage and demand response into intentional controlled islanding using time decomposition

“…DR is regarded as a promising way to improve the system operation, 6 flexibility, 7 reliability, 8 and security. 9 DR is a type of generation-demand interaction in which the consumers alter their inherent electricity usage modes based on the price signal or incentive mechanism of a competitive electricity market. 10 The incentive-based DR is more related to small customers who cannot participate in a wholesale electricity market directly.…”