Retail Energy Management in Electricity Markets: Structure, Challenges and Economic Aspects- a Review

Golmohamadi, Hessam; Keypour, Reza

doi:10.1007/s40866-017-0036-3

Cited by 26 publications

(17 citation statements)

References 79 publications

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“…In this study, the ORI can purchase/sell the deficit/excess of electrical energy from/to the power network, i.e., the wholesale electricity market. Generally, the electricity price of the wholesale market is a variable with imperfect data [25]. Therefore, the problem is under electricity price uncertainty.…”

Section: Solution Methodologymentioning

confidence: 99%

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Integration of Joint Power-Heat Flexibility of Oil Refinery Industries to Uncertain Energy Markets

Golmohamadi

Asadi

2020

Energies

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This paper proposes a novel approach to optimize the main energy consumptions of heavy oil refining industries (ORI) in response to electricity price uncertainties. The whole industrial sub-processes of the ORI are modeled mathematically to investigate the joint power-heat flexibility potentials of the industry. To model the refinery processes, an input/output flow-based model is proposed for five main refining units. Moreover, the role of storage tanks capacity in the power system flexibility is investigated. To hedge against the electricity price uncertainty, an uncertain bound for the wholesale electricity price is addressed. To optimize the industrial processes, a dual robust mixed-integer quadratic program (R-MIQP) is adopted; therefore, the ORI’s operational strategies are determined under the worst-case realization of the electricity price uncertainty. Finally, the suggested approach is implemented in the south-west sector of the Iran Energy Market that suffers from a lack of electricity in hot days of summer. The simulation results confirm that the proposed framework ensures industrial demand flexibility to the external grids when a power shortage occurs. The approach not only provides demand flexibility to the power system, but also minimizes the operation cost of the industries.

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Section: Solution Methodologymentioning

confidence: 99%

“…The parametric FOR is depicted in Figure 3. Equations (25) and (26) explain the ramp-up/-down of the CHP.…”

Section: Co-generation Unitsmentioning

confidence: 99%

Integration of Joint Power-Heat Flexibility of Oil Refinery Industries to Uncertain Energy Markets

Golmohamadi

Asadi

2020

Energies

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“…1 Aside from environmental concerns, rapid increases in energy consumption in recent years and fossil fuel depletion, energy management amongst customers is important in determining portfolio outcomes for electricity retailers. 1 Among these outcomes, one of the most important is the financial risk experienced by retailers. 2 One source of financial risk is the quantity risk 2 that arises from the variation in electricity use amongst retail customers.…”

Section: Introductionmentioning

confidence: 99%

The effect of sociodemographic diversity of residential customers on the financial risk experienced in the retail electricity market

et al. 2020

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The sociodemographic diversity of residential customers can affect the level of financial risk that an electricity provider experiences in the retail market. To demonstrate the relationship between sociodemographic diversity and financial risk, electricity consumption data drawn from the United Kingdom Power Networks 'Low Carbon London' project was analyzed to explore the relationship between sociodemographic diversity and financial risk experienced by electricity retailers. The results show that when increasing the sociodemographic diversity amongst a group of residential customers the effect on financial risk depends on the electricity consumption patterns of individual customers and the relationship of consumption patterns between residential customers. Increasing sociodemographic diversity amongst residential customers with very distinct energy consumption patterns can decrease the overall financial risk associated with the aggregated revenue received from these customers. However, the results showed that adding customers to a customer base without consideration for their sociodemographic background can cause the overall financial risk associated with the aggregated revenue received to change erratically. Whilst previous studies have considered customer diversity and its influence on peak electricity demand, this research advances the stateof-the-art by showing the importance of customer diversity to the financial quantity risk experienced by electricity retailers. This finding has serious implications for electricity providers seeking to mitigate financial risk in the retail electricity market.

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“…To hedge the price risk, retailers can undertake several strategies such as power purchase agreements, optimizing self-generation portfolio, and financial contracts on the secondary market. Among the hedging methods, implementing an electricity sales plan in the retail market is generally the adopted method in restructured electricity markets [4]. This can be accomplished through the optimal operation of a demand response program, which is divided into two categories: Incentive-based programs (IBPs) and price-based programs (PBPs) [5,6].…”

Section: Introductionmentioning

confidence: 99%

Optimal Operation of Critical Peak Pricing for an Energy Retailer Considering Balancing Costs

2019

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Recently, there have been frequent fluctuations in the wholesale prices of electricity following the increased penetration of renewable energy sources. Therefore, retailers face price risks caused by differences between wholesale prices and retail rates. As a hedging against price risk, retailers can utilize critical peak pricing (CPP) in a price-based program. This study proposes a novel multi-stage stochastic programming (MSSP) model for a retailer with self-generation photovoltaic facility to optimize both its bidding strategy and the CPP operation, in the face of several uncertainties. Using MSSP, decisions can be determined sequentially with realization of the uncertainties over time. Furthermore, to ensure a global optimum, a mixed integer non-linear programming is transformed into mixed integer linear programming through three linearization steps. In a numerical simulation, the effectiveness of the proposed MSSP model is compared with that of a mean-value deterministic model based on a rolling horizon method. We also investigate the optimal strategy of a retailer by changing various input parameters and perform a sensitivity analysis to assess the impacts of different uncertain parameters on the retailer’s profit. Finally, the effect of the energy storage system on the proposed optimization problem is investigated.

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Retail Energy Management in Electricity Markets: Structure, Challenges and Economic Aspects- a Review

Cited by 26 publications

References 79 publications

Integration of Joint Power-Heat Flexibility of Oil Refinery Industries to Uncertain Energy Markets

Integration of Joint Power-Heat Flexibility of Oil Refinery Industries to Uncertain Energy Markets

The effect of sociodemographic diversity of residential customers on the financial risk experienced in the retail electricity market

Optimal Operation of Critical Peak Pricing for an Energy Retailer Considering Balancing Costs

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