Study of the Effect of Time-Based Rate Demand Response Programs on Stochastic Day-Ahead Energy and Reserve Scheduling in Islanded Residential Microgrids

Abstract

Abstract:In recent deregulated power systems, demand response (DR) has become one of the most cost-effective and efficient solutions for smoothing the load profile when the system is under stress. By participating in DR programs, customers are able to change their energy consumption habits in response to energy price changes and get incentives in return. In this paper, we study the effect of various time-based rate (TBR) programs on the stochastic day-ahead energy and reserve scheduling in residential islanded microgrids (MGs). An effective approach is presented to schedule both energy and reserve in presence of renewable energy resources (RESs) and electric vehicles (EVs). An economic model of responsive load is also proposed on the basis of elasticity factor to model the behavior of customers participating in various DR programs. A two-stage stochastic programming model is developed accordingly to minimize the expected cost of MG under different TBR programs. To verify the effectiveness and applicability of the proposed approach, a number of simulations are performed under different scenarios using real data; and the impact of TBR-DR actions on energy and reserve scheduling are studied and compared subsequently.

A Virtual Power Plant Architecture for the Demand-Side Management of Smart Prosumers

“…More precisely, when the request d is satisfied in time slot t (i.e., when w dt = 1), then E t and V t have to be less or equal than the power limitation associated to the request, otherwise (when w dt = 0) the limitation is represented by the nominal maximum values. Constraints (15) and (16), instead, manage the requests of increasing, respectively, the power taken from and supplied to the grid, ensuring that E t and V t are greater than or equal to the minimum power associated with the request d if satisfied in time slot t. Finally, constraint (17) states that a request d is totally satisfied (z d = 1) only if d is satisfied in all of the time slots t ∈ T d involved in the DR request. Constraints (18) and (19) are binary conditions on variables.…”

“…Several research streams have dealt with the upcoming challenges related to the transition towards decarbonized and distributed energy systems, covering different fields: from the application and control of Distributed Electrical Storage Systems (DESSs) for the mitigation of the stochastic behavior of RESs [4][5][6][7], and the development of innovative control approaches for the operation of Smart Grids (SGs) and micro-Grids (µGs) [8][9][10][11], to the application of transactive Demand-Side Management (DSM) schemes [12][13][14][15] and the design of advanced measurement systems [16,17]. All these studies resulted in a significant reformulation of the fundamental concepts of current energy systems, remarking the need for a radical shift from the current centralized management of energy systems towards distributed intelligence and Holonic Multi Agent Systems (HMASs) [1,2] supported by a proper Information and Communication Technology (ICT) infrastructure [18].…”

Stochastic Unit Commitment of Wind-Integrated Power System Considering Air-Conditioning Loads for Demand Response

“…AC n x AC n x P P ≤ (12) The power of ACLs is normally determined by the outdoor temperature and the set value of the indoor temperature. This study aims to reduce the peak loads by controlling the usage of ACLs.…”

“…The DR is described as a resource that originates from the behavior and usage changes of electricity customers in response to rate variations [11] or incentive mechanisms [12]. Generally, there are two types of DR programs: incentive-based and price-based programs.…”

Coordination of EVs Participation for Load Frequency Control in Isolated Microgrids

“…With increasing penetration level of EVs and considering that they are available most of the times in a day, they can play the role of ESSs in a way to alter their energy consumption/production level under the vehicle-to-grid (V2G) concept and exchange the power with the grid [5]. Thus, with V2G capability, EVs can provide ancillary services for the grid, such as frequency regulation [5], load levelling [6,7] and spinning reserve [8]. With the application of a well-designed energy management system (EMS), EVs can act as an effective solution to compensate the uncertain behaviour of RESs.…”