An Integration Mechanism between Demand and Supply Side Management of Electricity Markets

Abstract

One of the main challenges in the emerging smart grid is to jointly consider the demand and supply, which is also reflected in the wholesale market (supply side) and the retail market (demand side). When integrating the demand and supply side into one framework, the mechanism for determining the market clearing price has been changed. This is due to the demand variations in the demand side in response to the market clearing price and the change of generation costs in the supply side from the demand variation. In order to find the best balance between the supply and demand under the demand response management scheme, this paper proposes a new integrated supply and demand coordination mechanism for the electricity market and smart pricing methods for generator and retailers. Another important contribution of this paper is to develop an efficient algorithm to find the match equilibrium between the demand and supply sides in the new proposed mechanism. Experimental results demonstrate that the new mechanism can effectively handle unpredictable demand under dynamic retail pricing and support the ISO to dispatch the generation economically. It can also help in achieving the goals of dynamic pricing such as maximizing the profits for retailers.

Analysis on the potential of an energy aggregator for domestic users in the Italian electricity system

“…Specifically, the coordination actions have to be executed by a virtual structure, the so-called energy "aggregator". That instrument creation allows to gather the multiple customers flexibility making it interesting for the energy market proposals [32].…”

Effects of Producer and Transmission Reliability on the Sustainability Assessment of Power System Networks

“…The capacity of each of the transmission lines is given in Table 3. x [1] x [2] x [3] Producers x [4] x [5] x [6] x [7] x [8] x [9] x [10] x [11] x [12] x [13] x [14] x [15] x [16] x [17]…”

“…For Scenario 3, the second scenario is modified by introducing renewable energy technologies into the power network. At Node 1, the producer represented by the decision variable x [2] is replaced by a wind farm with a capacity of 15 MW, and the decision variable x [3] is replaced by a photovoltaic farm with a capacity of 15 MW. In this way, the total installed capacity of 30 MW at this node is not altered, and a fair comparison of this scenario with the former two scenarios is maintained.…”

“…Power network sustainability-reliability is an important aspect of an energy generationtransmission system because the electricity demand of a group of customers needs to be assured at every instant of time with the lowest possible price and the least damage to the environment and society [1][2][3][4]. The reliability of a power network is usually dependent on the uncertainties associated with generation [5][6][7][8][9], transmission (and distribution) [10][11][12], load demand [13][14][15], and the presence of unexpected catastrophic events [16][17][18].…”

A Machine Learning Pipeline for Demand Response Capacity Scheduling

“…This is one of the uncertainties faced by the DR program operators [4]. Deviations from the scheduled DR capacity would necessitate additional real-time balancing and reserve energy resources to guarantee the security of supply in the grid [5]. Often, such deviations result in penalties to the DR participants, who may be discouraged to continue delivering DR.…”