Review of wind power scenario generation methods for optimal operation of renewable energy systems

Li, Jinghua; Zhou, Jiasheng; Chen, Bo

doi:10.1016/j.apenergy.2020.115992

Cited by 136 publications

(40 citation statements)

References 102 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Scenario generation methods applied for power system application can be classified into three general categories: sampling-based, forecasting-based and optimization-based approaches [30]; and different works apply various methods to generate appropriate number of scenarios [31][32]. Since the number of generated scenarios is normally huge in these methods, scenario reduction methods are applied to reduce the number of generated scenarios [33][34][35].…”

Section: Results For Stochastic Condition (Model 3)mentioning

confidence: 99%

Optimal Bidding Strategy for Physical Market Participants With Virtual Bidding Capability in Day-Ahead Electricity Markets

Mehdipourpicha

2021

IEEE Access

View full text Add to dashboard Cite

Virtual bidding provides a mechanism for financial players to participate in wholesale dayahead (DA) electricity markets. The price difference between DA and real-time (RT) markets creates financial arbitrage opportunities for financial players. Physical market participants (MP), referred to as participants with physical assets, can also take advantage of virtual bidding but in a different way, which is to further amplify the value of their physical assets. Therefore, this work proposes a model for such physical MPs to maximize the profits. This model employs a bi-level optimization approach, where the upper-level subproblem maximizes the total profit from both physical generations and virtual transactions while the lower-level model mimics the multi-period network-constrained DA market clearing process. In this model, uncertainties associated with other MPs as well as RT market prices are considered. Moreover, conditional value-at-risk (CVaR) metric is utilized to measure the risk of diverse strategies. The optimal strategy of the strategic physical MP is derived by solving this bi-level optimization model. The proposed bi-level model is transformed to a single level mixed integer linear programming (MILP) model using Karush-Kuhn-Tucker (KKT) optimality conditions and the duality theory. Case studies show the effectiveness of the proposed method and reveal physical MP tends to deploy virtual transactions in a very different way than pure financial MPs.INDEX TERMS bidding strategy, bi-level optimization, financial products, physical market participants, profit maximization, virtual bidding. NOMENCULTURE A. SETS AND INDICES Time periodsGenerating units of strategic producers Virtual participant Other generating units of the nonstrategic producer Demands Generation/demand offer/bid blocks Buses Transmission lines Scenarios related to other MPs' behaviors Scenarios related to RT market prices Sets of players located at bus B. PARAMETERSReal Time price at bus at time Marginal cost of strategic unit at time Marginal cost of nonstrategic unit at time Marginal utility of demand at time Upper limit of generation/consumption of the virtual participant at time in DA Market Upper limit generation of strategic unit at time Upper limit generation of nonstrategic unit at time Upper limit of demand at time Ramp up limit of generating unit Ramp down limit of generating unit Power Transfer Distribution Factor (sensitivity of power flows through line l to the injection power to bus n) Proxy of virtual transaction at time Proxy Budget Power flow Capacity of line Weighting parameter Confidence level Scenario s probability in DA market Scenario w probability Note that some of these constants contain index s or w when they refer to scenario s or w.

show abstract

Section: Results For Stochastic Condition (Model 3)mentioning

confidence: 99%

Optimal Bidding Strategy for Physical Market Participants With Virtual Bidding Capability in Day-Ahead Electricity Markets

Mehdipourpicha

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The stochastic nature of wind power generation and load demands are challenging issues for the operational decision-making process in electrical power systems. In order to effectively address system uncertainties in stochastic programming, scenario generation (SG) is suggested in different studies [42], [43]. In this paper, two sources of uncertainties come from load demands and wind power.…”

Section: Deld With Wind and Load Uncertainties A Scenario Generationmentioning

confidence: 99%

A Modified Teaching—Learning-Based Optimization for Dynamic Economic Load Dispatch Considering Both Wind Power and Load Demand Uncertainties With Operational Constraints

Alanazi

2021

IEEE Access

View full text Add to dashboard Cite

Dynamic economic load dispatch (DELD), which determines the best generation scheduling for different power plants during the next 24 hours in order to meet the electricity demand as well as minimize the total energy cost, is a highly complex non-linear and non-convex problem. This study proposes an efficient modified teaching-learning-based optimization algorithm, called MTLBO, to effectively solve the DELD optimization problem in a power system containing thermal generations and wind power. The planning problem comprises the total fuel cost function with valve-point loading effect and the transmission power losses. Also, the uncertainties of wind power and load demand along with the various equality and inequality operational constraints such as power generation limits, ramp rate limits, prohibited operating zones and power balance are considered in the problem. Additionally, in the proposed MTLBO algorithm, the learning phase is properly integrated into the teaching phase to improve the convergence characteristic of the original TLBO. Moreover, in order to enhance the feature of local optima avoidance, interaction of up to five students are incorporated into the learning phase to improve the knowledge of each student. To exhibit the effectiveness of the proposed approach, the algorithm is applied into the 14 real test functions. In addition, several cases with 10 and 30 unit test systems are investigated over the planning period. Compared to the main TLBO algorithm which is mostly used optimization algorithms proposed in prior studies, the simulation results demonstrate the efficiency and superiority of the proposed optimization approach in terms of consistency, robustness, convergence rate and finding better plausible optimal solutions. INDEX TERMS Dynamic economic load dispatch, modified teaching learning based optimization, load demand, operational constraints, optimal solutions, real-world optimization problems, total fuel cost function, transmission power loss, uncertainty, valve-point loading effect, wind power.

show abstract

“…Turk et al (Turk et al, 2020) introduced that high level of uncertainty and fluctuation of renewable energy sources exist and proposed the scenario generation algorithm with corresponding probabilities to improve the utilization of wind energy. Li et al (Li et al, 2020) discussed and classified the scenario generation method to address the uncertainties of energy systems with integrated wind power. In the above studies, the ways to describe the uncertainty of renewable energy can be classified as probabilistic forecasting, scenario generation, and uncertainty description by conditional value at risk.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Dispatching Method for Wind-Hydropower-Pumped Storage Integrated Power Systems

et al. 2021

View full text Add to dashboard Cite

With the fossil energy crisis and environmental pollution, wind energy and other renewable energy have been booming. However, the strong intermittence and volatility of wind power make difficult of its integration into grid. To solve this problem, this study proposes a complementary power generation model of wind-hydropower-pumped storage systems, which uses hydropower and pumped storage to adjust the fluctuation of wind power. How to consider the uncertainty and unpredictability of wind power output and make more reliable hydropower generation plan and pumped storage generation plan is the key problem to be solved in the grid with the high proportion of renewable energy. The martingale model of forecast evolution is used to describe the uncertainty evolution of wind power in different regions. According to the flexible load in the region, the flexibility index is used to quantify flexibility, and the transaction price is set to be proportional to flexibility. The two-stage framework of day-ahead and real-time dispatching model is then developed. In the day-ahead stage, different regions trade with each other. If the power after trading is imbalanced, it will be supplemented by hydropower and the grid to meet the power demand. In the real-time stage, the pumped storage is added to quickly balance the deviation of wind power and load between the real-time and day-ahead stages. Finally, considering the positive effect of hydropower on wind power consumption in the grid, a benefit allocation method based on improved Shapley value method is proposed. Test cases are simulated to verify the rationality of the proposed dispatching model and the benefit allocation method. After the cooperation of hydropower and pumped storage, the average revenue growth is 3.02%. The improved benefit allocation scheme makes more benefit of hydropower and pumped storage and promotes the cooperation of multi-participants.

show abstract

Review of wind power scenario generation methods for optimal operation of renewable energy systems

Cited by 136 publications

References 102 publications

Optimal Bidding Strategy for Physical Market Participants With Virtual Bidding Capability in Day-Ahead Electricity Markets

Optimal Bidding Strategy for Physical Market Participants With Virtual Bidding Capability in Day-Ahead Electricity Markets

A Modified Teaching—Learning-Based Optimization for Dynamic Economic Load Dispatch Considering Both Wind Power and Load Demand Uncertainties With Operational Constraints

A Two-Stage Dispatching Method for Wind-Hydropower-Pumped Storage Integrated Power Systems

Contact Info

Product

Resources

About