A hybrid robust-stochastic approach to evaluate the profit of a multi-energy retailer in tri-layer energy markets

Abstract

Amjad (2021) A hybrid robust-stochastic approach to evaluate the profit of a multi-energy retailer in trilayer energy markets. Energy, 214. p. 118948.

Electricity Cost-Sharing in Energy Communities Under Dynamic Pricing and Uncertainty

“…As an upgrade of [28], this paper precisely models demand flexibility, considers the stochastic nature of PV production and consumers' load, and investigates the value of flexibility incentives to adjust the RT operation schedule of household appliances to a predefined DA schedule. A multi-energy retailer (MER) aims to maximize its profit from selling electricity, gas, and heat demands to the multi-energy consumer [29]. MER participates in the electricity, gas, and heat market and operates tri-state compressed air energy storage (tri-CAES) and combined heat and power (CHP) technologies in order to satisfy the demand of final consumers.…”

A tactical scheduling framework for wind farm‐integrated multi‐energy systems to take part in natural gas and wholesale electricity markets as a price setter

“…Similarly, a stochastic decision‐making framework was investigated in [16] for MES to participate in reserve and day‐ahead markets. The authors in [17] scrutinized a hybridized stochastic programming (SP) and robust optimization (RO) model for an electricity retailer to seize the arbitrage opportunities. An analogous hybrid framework was also proposed in [18] to model MES behavior in NGM, day‐ahead, real‐time and local markets.…”

“…The chief downsides (DS) of the publications mentioned above are listed as follows: DS1 : In studies [9–21, 23, 24, 26, 28, 29, 31], the MES partakes in various markets as a price taker. Nevertheless, a price taker does not have any authority over market clearing price (MCP), and it is obliged to conform to the market price announced by the market operator. DS2 : The studies [9–20, 23, 24, 26, 28, 29, 31] have investigated the behavior and partition means of MES in different market levels. That said, the operational mechanism and behavior models of the markets have been neglected.…”

“…Disregarding their models renders these studies incompatible with practical circumstances. DS3 : Some of these studies (i.e., [9–13, 15, 16, 19, 21–27]) have not evaluated the CAES's presence in the tactical scheduling of MES as a price setter in various energy markets. A CAES can be operated together with a TES and NGS to enhance the role of MES in markets and manage the uncertainties derived from volatile wind power. DS4 : The studies [9–18, 21–29 31] have proposed RO and SP methods. However, the SP method requires complicated risk measures, and RO can only provide risk averse solutions, while in real‐world applications, the MES operator should have a choice between risk‐averse or risk seeker frameworks.…”

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

“…To this end, the motivation of this paper is to present a comprehensive review for electricity markets considering price and load forecasting mechanisms through wind energy, which is one of the fastest growing renewable energy resources due to a growing wind power integration into the electrical grids [13]. For the determined hypothesis, it is observed that forecasting approaches vary between economic terms (i.e., demand [14], supply [15], profit [16], producer, and consumer surplus [17]) and engineering techniques (i.e., power systems [18,19], optimization [20], control [21], and meta-heuristics algorithms [22,23]). As a methodology, this review follows the historical and structural development of electricity markets (i.e., day-ahead markets, intra-day markets, balancing power markets), price and load forecasting methods, and recent trends in wind energy generation, transmission, and consumption, being a novel contribution to the literature.…”