Uncertainty-based electricity procurement by retailer using robust optimization approach in the presence of demand response exchange

Nojavan, Sayyad; Nourollahi, Ramin; Pashaei-Didani, Hamed; Zare, Kazem

doi:10.1016/j.ijepes.2018.08.041

Cited by 76 publications

(30 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, the retailer offers incentive payments for customers' SL resources instead of the IL resources investigated in [30][31][32]34], and the case study results suggest that the SL is another important DR resource the retailer can deploy to improve its profit and reduce the risk caused by the market price uncertainty. In [31][32][33], the retailer's incentive prices for DR resources are specified according to predetermined stepwise reward-based DR curves, however, in this paper the SL incentive prices are specified by solving a bi-level model which can better model the characteristics of customers, e.g., customers' load-shift capability and customers' willingness to participate in the DRP. In addition, in [33], a RO-based decision-making model is proposed to maximize retailer's benefit in the worst case, however, over-conservative strategies may be obtained if the uncertainty set is not constructed properly.…”

Section: Discussionmentioning

confidence: 99%

“…It is shown that retailers can avoid unfavorable prices in the real-time market and amend imbalances in demand by participating in the reward-based DRP. Retailers' energy allocation in the wholesale market, contracts market and short-term DR bidding market is investigated in [32] based on the RO to minimize the electricity procurement cost. In [33], a CVaR-based bi-level optimization model for retailers' trading strategy with multi-segment IL contracts offered to customers is developed.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Conditional-Robust-Profit-Based Optimization Model for Electricity Retailers with Shiftable Demand

Zhang

Wang

et al. 2020

Energies

View full text Add to dashboard Cite

This paper investigates the problem of how to deploy customers’ shiftable load (SL) for electricity retailers’ risk management under uncertainty of the day-ahead (DA) wholesale market price. The robust profit (RP) and the conditional robust profit (CRP) are introduced for a risk-averse retailer’s risk-reward trade-off analysis in its decision-making of electricity procurement from various options. A CRP-based bi-level optimization model is proposed for the risk-averse retailer to determine its electricity procurement strategy taking into consideration customers’ shiftable load. In the upper problem, the retailer decides its electricity procurement from various options and the SL incentive prices to maximize its CRP under a given confidence level, and in the lower problem, the customers shift their load according to the SL incentive prices to minimize their comprehensive costs including the discomfort cost caused by rescheduling electricity consumption. Finally, a case study is used to verify the effectiveness of this model. It is shown that the retailer can achieve larger profit and less risk by utilizing customers’ SL and the retailer’s risk-aversion level has an important impact on its electricity procurement and SL incentive strategies.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Conditional-Robust-Profit-Based Optimization Model for Electricity Retailers with Shiftable Demand

Zhang

Wang

et al. 2020

Energies

View full text Add to dashboard Cite

show abstract

“…The proposed scheme is modeled to real data from Queensland jurisdiction within the NEM 2012. Input data for the modeled problem are derived from [35]. In this study, the pool market price and total demand of consumers are two uncertain parameters that modeled using stochastic programming.…”

Section: A Datamentioning

confidence: 99%

Risk-Based Traded Demand Response Between Consumers’ Aggregator and Retailer Using Downside Risk Constraints Technique

et al. 2020

Self Cite

View full text Add to dashboard Cite

Electricity retailer is the most critical player in the restructured electricity market. Retailer's electricity procurement problem is a big challenge for them. Electricity retailer can purchase their energy from various options such as pool market, forward contracts, and etc. A relatively new way with a lesser investigation is the demand response exchange market. In this paper, the risk-constrained stochastic power procurement problem of electricity retailers is formulated by modeling the uncertainty of pool-market price and consumers' electricity demand. The Downside risk constraints (DRC) risk-evaluation method is used to obtain risk-based power procurement scheduling of electricity retailers. By using the proposed method, conservative electricity retailer can experience a scenario-independent strategy over the stochastic optimization. In other words, the proposed risk strategy is such that impose more cost for electricity retailer while having an equal cost in all scenarios. Based on the obtained results, the operation cost of electricity retailers in all scenarios is closed to about $4827570, which is a relatively zero-risk strategy due to equality overall scenarios. Besides, results are compared in two cases to demonstrate the advantages of the proposed risk-evaluation method. Also, the Pareto front between risk-in-cost and expected cost can introduce an optimal risk-strategy for electricity retailers in the presence of uncertainties. Finally, the riskaverse strategy of electricity retailer is proposed to obtain the retailer's optimal conservative schedule during power procurement in the presence of uncertainties. INDEX TERMS Electricity retailer, demand response exchange (DRX) market, downside risk-constraint (DRC), risk evaluation. NOMENCLATURE A. INDEXES SAYYAD NOJAVAN received the B.Sc., M.Sc., and Ph.D. degrees in electrical power engineering from the

show abstract

“…In this regard, scheduling strategies for the LSEs have been the subject area of many research works. A robust optimization approach is proposed in Nojavan et al to handle market price uncertainty, in which the retailer seeks to minimize the energy procurement costs with only considering DR programs. In Fang et al, a strategic bidding framework for LSE agent has been proposed in which the objective is to maximize LSE's profit by implementing DR programs.…”

Section: Introductionmentioning

confidence: 99%

“…Table 1 addresses a systematic comparison between the contributions of this paper and some of the recent works in the same subject area. As can be observed, most of the recent works (ie, see previous studies [6][7][8][9][10][11][13][14][15][16] ) did not consider the PEVs' charging and discharging management in the optimization problems of LSEs, and they mainly investigated only impacts of DR programs on the other types of responsive loads. To the best of the authors' knowledge, there are some limited works addressing the decision-making problem of LSE by considering both PEVs' and customers' participation in DR programs, simultaneously, however, they did not consider competitive trading floor (ie, Momber et al 12 ).…”

Section: Introductionmentioning

confidence: 99%

A stochastic bi‐level decision‐making framework for a load‐serving entity in day‐ahead and balancing markets

Rashidizadeh‐Kermani

Vahedipour‐Dahraie

Anvari‐Moghaddam

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary This paper investigates a stochastic bi‐level scheduling model for decision‐making of a load‐serving entity (LSE) in competitive day‐ahead (DA) and regulating markets with uncertainties. In this model, LSE as the main interacting player of the market sells electricity to end‐use customers and plug‐in electric vehicles (PEVs) to maximize its expected profit. Therefore, a two‐level decision‐making process with different objectives is considered to solve the problem. In one level, the objective is to maximize the LSE's profit by optimally scheduling of responsive loads and PEVs charging/discharging process, while in the other level, the payments of the customers and PEV owners should be minimized in a competitive market. In the proposed decision‐making process, to model the uncertainties, market prices, required energy of customers and PEVs, and the rival LSEs' prices are considered as random variables. The bi‐level stochastic problem is then converted into a linear single‐level stochastic model with equilibrium constraints by using Karush‐Kuhn‐Tucker (KKT) optimality conditions as well as duality theory. A case study is implemented to indicate the applicability of the intended model. The applicability of the proposed model is tested on Nordic market and the results show that in a competitive market, the LSE can increase its revenue and attract more demand loads and PEV owners by offering more moderate prices.

show abstract

Uncertainty-based electricity procurement by retailer using robust optimization approach in the presence of demand response exchange

Cited by 76 publications

References 55 publications

Conditional-Robust-Profit-Based Optimization Model for Electricity Retailers with Shiftable Demand

Conditional-Robust-Profit-Based Optimization Model for Electricity Retailers with Shiftable Demand

Risk-Based Traded Demand Response Between Consumers’ Aggregator and Retailer Using Downside Risk Constraints Technique

A stochastic bi‐level decision‐making framework for a load‐serving entity in day‐ahead and balancing markets

Contact Info

Product

Resources

About