Multi-Objective Day-Ahead Scheduling of Power Market Integrated With Wind Power Producers Considering Heat and Electricity Trading and Demand Response Programs

Abstract

The large-scale penetration of renewable energy, such as wind power, brings a lot of economic and environmental benefits to the grid, and it also causes hidden dangers in the reliability and security of the power system due to its uncertainty. As an effective demand-side management method, demand response has unique advantages in smoothing wind power fluctuations and mitigating grid pressure. This paper proposes a new model for the demand response aggregator (DRA) that includes both combined heat and power systems (CHPS) and energy storage devices. DRA can interact with the Independent system operator (ISO) through combined heat and power (CHP) units, energy storage devices, and the heat buffer tank to benefit from the electricity market and the thermal market simultaneously. At the same time, wind power producers (WPP) are modeled to turn wind power that was initially passively consumed into active market participants. The problem is modeled using an improved weighted method, which aims to take the diverse objectives of multiple market participants into account. The proposed model is tested on the modified IEEE RTS-24 test system to analyze the optimal scheduling strategies of each participant in the power market. INDEX TERMS Combined heat and power, demand response, electricity market, electricity storage, wind power producer. NOMENCLATURE Most of the symbols and notations used throughout this paper are defined below for quick reference. Others are defined following their first appearances, as needed.

Distributed Demand Response Management for a Virtually Connected Community With Solar Power

“…Most of the studies in the literature are remote from the practical situation due to over-simplification. The simulation of simple building thermal models such as lumped and thermal equivalent circuits are abundant [13], [16], [17],…”

“…A detailed building simulation brings additional challenges such as heat/ cooling load variations with outside temperature, building orientation, sun elevation to the horizontal plane, and heat transfer from other loads. Longer time steps such as one-hour simulation studies [17], [18] [19], lack faster variations of TCLs. For instance, a study done for a large number of HVAC systems in residential and light-commercial spaces [20] had a median cycle time of 8 minutes.…”

Operating Hydrogen-Based Energy Storage Systems in Wind Farms for Smooth Power Injection: A Penalty Fees Aware Model Predictive Control

“…Moving forward to timescales of the order of minutes, for example Lyu et al [4] develops an automatic generation control for power smoothing which is set in response to the actual grid needs. At this point, the literature jumps to timescales of the order of hours, targeting, e.g., market participation/demand response programs [5][6][7] but without power smoothing, which instead is investigated in Abdelghany et al [8], where an MPC-based strategy is developed featuring a two-step sequential optimization: firstly, a function of the previous output power variations, such that the new decided value does not lie too far from previous ones, is minimized; secondly, other costs are minimized, such as, e.g., a reference tracking cost, and, among the others, a similar function used in the first step is constrained so as not to exceed the previously optimal computed value. In this paper, we investigate a scenario similar to [8], which shares some of the authors of this paper; however, later, the differences will be highlighted in order to identify the major advancements and novelties beyond the state of the art.…”

Impact of Local Emergency Demand Response Programs on the Operation of Electricity and Gas Systems

“…However, respecting the privacy and security of end-users in the distribution network when executing DR is one of the requirements of the operators that can be calculated through indirect processing of transformer data and the demand profile of end-users [4]. Another method of DR implementation has been employed to maximize the participants' profits from active market participation using the improved weighting method [5]. Additionally, to solve the problem of real-time response, a predictive control model based on the roller optimization stage strategy can be used [6].…”