Ancillary Services Management and Dispatch Using Dynamic Game Model for a Generation Aggregator Business Market in Smart Grids

Interruptible Power Estimation and Auxiliary Service Allocation Using Contract Theory and Dynamic Game for Demand Response in Aggregator Business Model

“…For example, the US Pacific Power and Gas Company (PG&E) must have a minimum of 75% of the agreed load capacity; otherwise, a fine will be applied if it is less than 50% [30], [31]. To achieve the balance between supply and demand, the AS must provide sufficient spare capacity, load control, frequency control, and voltage control to immediately meet the operating conditions [14]. With the premise of saving energy and reducing carbon emissions, each dispatch is preferentially provided with renewable energy (wind and solar) and energy storage, and then excess renewable energy is resold to the electricity industry or near generation aggregators.…”

“…The interruptible power price per kWh (TWD/kWh) can be set in the range of Equation (14). If the parameter b is formulated, the maximum interruptible power can be estimated by Equation (5).…”

“…, Nl, represents the number of participating users in the user group. The interruptible power can be estimated using Equations ( 11)- (14). The demand aggregator can use the DG capacity to determine the interruptible power by each user, S int,h (assigned according to proportion):…”

“…To reduce the transaction costs of performing DR between the electricity industry and the user groups, past literatures [2], [5]- [8], [13], [14] have pioneered the aggregator business model in liberalized power markets (United States, 2,000 years, as seen in Figure 1. Aggregators can be divided into two roles namely, demand and generation aggregators [15], [16].…”

“…The process considers the users' demands, electricity purchased cost, DG available capacity, and risk evaluation (uncertain renewable energy [15]) to determine the appropriate interruptible power along with the price or incentive payment [11], [23], leading to both electricity industry and user groups can get profits and rewards. Each demand aggregator can cooperate with multiple generation aggregators and use the dynamic game model (DGM) [14], [24]- [28] to evenly distribute the DG resources, such as renewable energy capacity, AC power generation capacity, and energy storage system capacity, all of which are based on user participation intention, interruptible power, user demand, and decentralized power supply risk factors. In this study, according to the one-hour-ahead strategy and DG information, we will consider the virtual customer across three user groups as a case study to verify the feasibility of the proposed methods for DR and AS tasks.…”