Risk-aware stochastic bidding strategy of renewable micro-grids in day-ahead and real-time markets

Fazlalipour, Pary; Ehsan, Mehdi; Mohammadi‐Ivatloo, Behnam

doi:10.1016/j.energy.2018.12.173

Cited by 53 publications

(12 citation statements)

References 40 publications

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“…Addressing uncertainty modeling method can be categorized into five classes: stochastic optimization (SO), robust optimization (RO), interval optimization, fuzzy method, and autoregressive moving average (ARIMA) models. In [3], a stochastic-based comprehensive bidding strategy for DER aggregators was developed considering PV and WT output uncertainties. The authors in [4] proposed a stochastic linear programming-based optimal bidding strategy for DER aggregators in a dayahead market that involves DER uncertainties.…”

Section: B Literatrue Reviewmentioning

confidence: 99%

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Data-Driven Bidding Strategy for DER Aggregator Based on Gated Recurrent Unit–Enhanced Learning Particle Swarm Optimization

2021

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Distributed energy resources (DERs) such as wind turbines (WTs), photovoltaics (PVs), energy storage systems (ESSs), local loads, and demand response (DR) are highly valued for environmental protection. However, their volatility poses several risks to the DER aggregator while formulating a profitable strategy for bidding in the day-ahead power market. This study proposes a data-driven bidding strategy framework for a DER aggregator confronted with various uncertainties. First, a data-driven forecasting model involving gated recurrent unit-enhanced learning particle swarm optimization (GRU-ELPSO) with improved mutual information (IMI) is employed to model renewables and local loads. It is critical for a DER aggregator to accurately estimate these components before bidding in the day-ahead power market. This aids in reducing the penalty costs of forecasting errors. Second, an optimal bidding strategy that is based on the information gap decision theory (IGDT) is formulated to address market price uncertainty. The DER aggregator is assumed to be risk-averse (RA) or risk-seeker (RS), and the corresponding bidding strategies are formulated according to the risk preferences thereof. Thenr, an hourly bidding profile is created for the DER aggregator to bid successfully in the day-ahead power market. The proposed datadriven bidding framework is evaluated using an illustrative system wherein a dataset is obtained from the PJM market. The results reveal the effectiveness of handling uncertainty by providing accurate forecasting results. In addition, the DER aggregator can bid effectively in the day-ahead power market according to its preference for robustness or high profit, with a suitable bidding profile.

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Section: B Literatrue Reviewmentioning

confidence: 99%

“…In (3), it is clear that Sz∈{0, 1, 2…, 7}, and Sz, counts the numbers from zero to seven. From the aforementioned discussion, the individual and joint probabilities can be computed as (4).…”

Section: A Data-preprocessing and Feature Selectionmentioning

confidence: 99%

Data-Driven Bidding Strategy for DER Aggregator Based on Gated Recurrent Unit–Enhanced Learning Particle Swarm Optimization

2021

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“…Then, a random sample is drawn from each section of the input distribution. The steps of the LHS method [37] to generate price scenarios are given below.…”

Section: ) Spot Contractmentioning

confidence: 99%

Optimal Day-Ahead Decision-Making Scheduling of Multiple Interruptible Load Schemes for Retailer With Price Uncertainties

2021

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The interruptible load contract is a flexible and reliable resource that can help retailers reduce market risk in the form of an option contract. However, the mechanism of IL contract price formation and the representation of value are rarely considered in the study of interruptible load contracts, which helps reduce the contract price. It is studied in the paper that the optimal decisions by retailer in the day-ahead spot market under multiple types of interruptible load contracts. Four IL contracts are proposed considering the possibility of consumers' participation in demand response at different periods. The uncertain spot price is simulated by the stochastic scenario method, and a model for retailer's decision under multiple contracts is proposed by Conditional Value-at-risk assessment to maximize the expected profit. The presented model is a mixed-integer nonlinear programming problem, which is solved by CPLEX solver of GAMS software. The feasibility of the given decision strategy is verified through the numerical results.

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“…In stochastic optimization, the optimization solution is based upon the scenario values and probabilities so the scenario generation method is vital to achieve realistic results. Many of the previous researches focused on the model based scenario generation methods that first attempt to fit probability distributions, then uses Monte Carlo Simulation (MCS) as in [16], [21]- [23] or Latin Hyper cube Sampling (LHS) as in [24]- [25] to sample from these probability distributions and generate scenarios. The probability distributions are not guaranteed to be accurate in representing the uncertain variables.…”

Section: Introductionmentioning

confidence: 99%

Data Driven Stochastic Energy Management for Isolated Microgrids Based on Generative Adversarial Networks Considering Reactive Power Capabilities of Distributed Energy Resources and Reactive Power Costs

et al. 2021

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This paper proposes a two-stage stochastic energy management (EM) in an isolated microgrid (MG) to decide for the day-ahead optimal dispatch. The dispatch aims to effectively manage the MG power sources, including intermittent renewable energy sources (RESs), battery energy storage systems (BESSs), and diesel generators such that the expected operation costs, reactive power costs, spinning reserve, and load shedding are minimized. The Generative Adversarial Networks (GANs) is utilized in this paper as a data driven scenario generation method to model the uncertainties in the output power of the RESs to be used in the stochastic programming formulation. Then, the fast forward scenario reduction algorithm is used to reduce the number of scenarios with the help of SCENRED/GAMS software. Usually, fuel consumption costs of diesel generators are considered to be dependent on active power generation only. However, neglecting the related reactive power costs might result in increased operation costs and deviations in the dispatches from the optimal solutions. Hence, this paper co-optimizes the costs related to both active and reactive powers of diesel generators. In addition, this study considers the reactive power capability of inverter-interfaced distributed energy resources (DERs). Moreover, the detailed models for the different resources are presented, especially for diesel generators where the actual capability curves are used instead of the widely used box constraints. The problem is formulated as a nonlinear programming problem in the General Algebraic Modeling System (GAMS) software and is solved by the CONOPT solver.

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Risk-aware stochastic bidding strategy of renewable micro-grids in day-ahead and real-time markets

Cited by 53 publications

References 40 publications

Data-Driven Bidding Strategy for DER Aggregator Based on Gated Recurrent Unit–Enhanced Learning Particle Swarm Optimization

Data-Driven Bidding Strategy for DER Aggregator Based on Gated Recurrent Unit–Enhanced Learning Particle Swarm Optimization

Optimal Day-Ahead Decision-Making Scheduling of Multiple Interruptible Load Schemes for Retailer With Price Uncertainties

Data Driven Stochastic Energy Management for Isolated Microgrids Based on Generative Adversarial Networks Considering Reactive Power Capabilities of Distributed Energy Resources and Reactive Power Costs

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