Robust Self-Scheduling of Operational Processes for Industrial Demand Response Aggregators

Golmohamadi, Hessam; Keypour, Reza; Bak‐Jensen, Birgitte; Pillai, Jayakrishnan Radhakrishna; Khooban, Mohammad Hassan

doi:10.1109/tie.2019.2899562

Cited by 58 publications

(35 citation statements)

References 25 publications

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“…The Iran Energy Market (IEM) has been experiencing two major challenges recently. First of all, the Iran Power Grid (IPG) faces high peak demands during hot days of summer when the electricity demand of the air conditioning systems increases dramatically [1]. Increasing the generation and transmission capacities of the electricity network is a controversial decision.…”

Section: Motivation and Problem Descriptionmentioning

confidence: 99%

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: Motivation and Problem Descriptionmentioning

confidence: 99%

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

Golmohamadi

Asadi

2020

Energies

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“…In order to consider the caused risks by the pool market price uncertainty, in [17] robust optimization approach has been considered to evaluate the effects of DRP on the retailer cost. Also, in [18], the robust schedule of aggregated DRP is provided to improve the power system robustness. Besides, the information gap decision theory (IGDT) is another way to consider risks that creates uncertain parameters.…”

Section: A Literature Reviewmentioning

confidence: 99%

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

et al. 2020

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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

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“…In the electricity market, the participants needs to face various uncertainties, such as the electricity prices [21], renewable power productions [22]- [23], and the electric demands of electricity consumers, such as [24]- [27]. To develop the bidding strategy considering these uncertainties in the market, stochastic optimization, robust optimization and information gap decision theory (IGDT) techniques were utilized by the participants [21]- [26].…”

Section: Introductionmentioning

confidence: 99%

“…To develop the bidding strategy considering these uncertainties in the market, stochastic optimization, robust optimization and information gap decision theory (IGDT) techniques were utilized by the participants [21]- [26]. In the IGDT and robust optimization-based models, the uncertain parameters are represented by using their upper and lower bounds [21]- [24]; in contrast, the uncertain parameters in a stochastic optimization model are represented using a large number of scenarios, which should be generated based on the full probability distributions [25] [26]. In this circumstance, the computational cost of solving a stochastic optimization model tends to be larger than that of solving a robust optimizationbased or an IGDT-based model.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Up to Congestion Bidding Strategy in the Nodal Electricity Markets Considering Risk Management

Xiao¹,

Chen²

2020

IEEE Access

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Up to congestion (UTC) is a type of financial product available in the nodal electricity markets of the United States, based on which a financial participant can earn profits by utilizing the different congestion and loss components of the electricity prices in the day-ahead (DA) and real-time (RT) markets. This paper proposes the UTC bidding strategy by using stochastic optimization technique, where the uncertain electricity prices on the UTC transaction paths are represented via scenario sets. In the established stochastic model, the total expected profit and the Conditional Value at Risk (CVaR) of the UTC bidding strategy are maximized simultaneously considering risk management, where the risk preference of financial participant is characterized by using a risk aversion parameter. By solving the proposed stochastic model, non-increasing DA UTC bidding curves can be generated for all the time periods of the next operating day, where the credit requirements for UTC transactions are taken into account in detail. Finally, to verify the effectiveness of the proposed strategy, case studies are carried out based on the historical data and trading policies of the Pennsylvania-New Jersey-Maryland (PJM) electricity market, and the UTC bidding strategies generated by different models are analyzed. The numeral results indicate that, compared to the deterministic UTC bidding strategy, the proposed stochastic strategy can bring much higher expected profit and lower potential risks for the financial participant. Moreover, by adjusting the risk aversion parameter in the proposed model, the risks can be managed efficiently according to the financial participant's preference. INDEX TERMS Electricity market, financial participant, risk management, stochastic optimization, up to congestion I. NOTATIONS A. INDEXES AND SETS B. VARIABLES , , UTC bidding capacity on path d in time period t for scenario w. Auxiliary variable used to compute the CVaR. Auxiliary variable used to compute the Conditional Value at Risk (CVaR) for scenario w. , , Profit of the UTC bid used on path d in time period t for scenario w. , , Risk exposure of the UTC bid used on path d in time period t for scenario w.

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Robust Self-Scheduling of Operational Processes for Industrial Demand Response Aggregators

Cited by 58 publications

References 25 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

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

Stochastic Up to Congestion Bidding Strategy in the Nodal Electricity Markets Considering Risk Management

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