Risk‐based medium‐term trading strategy for a virtual power plant with first‐order stochastic dominance constraints

Shabanzadeh, Morteza; Eslami, Mohammad H.; Haghifam, Mahmoud‐Reza

doi:10.1049/iet-gtd.2016.1072

Cited by 47 publications

(28 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…-First-order stochastic dominance (FOSD) with three different numbers of benchmark scenarios in Table V [32]. These FOSD -Second-order stochastic dominance (SOSD) with three different numbers of benchmark scenarios in Table VI [33].…”

Section: B Comparative Results Of Solution Methodsmentioning

confidence: 99%

“…These CVaR-based SP methods, denoted by CVaR1, CVaR2, and CVaR3, have 0.9, 0.95, and 0.99 confidence intervals. These uncertainty modeling approaches have been considered as comparative methods in the Tables IV, V, VI, and VII since these methods have been used for VPP self-scheduling in the previous works, such as in [3], [4], [5], [32] and [33]. As these comparative methods and the proposed approach have different uncertainty modeling strategies, for the sake of a fair comparison, an out-of-sample analysis with the same set of outof-sample scenarios [21] has been used to evaluate the performance of all methods in Tables IV, V, VI, and VII.…”

Section: B Comparative Results Of Solution Methodsmentioning

confidence: 99%

See 1 more Smart Citation

VPP Self-Scheduling Strategy Using Multi-Horizon IGDT, Enhanced Normalized Normal Constraint, and Bi-Directional Decision-Making Approach

Yazdaninejad

Amjady

Dehghan

2020

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

including the URL of the record and the reason for the withdrawal request. Abstract1-This paper presents a new robust self-scheduling strategy for virtual power plants (VPPs) considering the uncertainty sources of electricity prices, wind generations, and loads. Multi-horizon information-gap decision theory (MH-IGDT) as a non-deterministic and non-probabilistic uncertainty modeling framework is proposed here to specifically model the uncertainty sources considering their various uncertainty horizons. Since each uncertain parameter tends to optimize its uncertainty horizon competitively for a particular value of the uncertainty budget, the proposed MH-IGDT model is formulated as a multi-objective optimization problem. To solve this multi-objective problem, enhanced normalized normal constraint (ENNC) method is presented, which can obtain efficient uniformly-distributed Pareto optimal solutions. The proposed ENNC includes augmented normalized normal constraint method and lexicographic optimization technique to enhance the search performance in the objective space. To address the unsolved issue of being risk-averse or riskseeker for a VPP in the market, a bi-directional decision-making approach is presented. This decision maker comprises an ex-ante performance evaluation method and a forward-backward dynamic programming approach to hourly find the best Pareto solution within the generated risk-averse and risk-seeker Pareto frontiers. Simulation results of the proposed self-scheduling strategy are presented for a VPP including dispatchable/non-dispatchable units, storages, and loads.

show abstract

Section: B Comparative Results Of Solution Methodsmentioning

confidence: 99%

Section: B Comparative Results Of Solution Methodsmentioning

confidence: 99%

VPP Self-Scheduling Strategy Using Multi-Horizon IGDT, Enhanced Normalized Normal Constraint, and Bi-Directional Decision-Making Approach

Yazdaninejad

Amjady

Dehghan

2020

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

show abstract

“…ESS constraints: the following constraints are included in the problem to ensure the proper operation of the ESS unit: (14) bounds SoC of each ESS within an upper and lower limit. Also, constraints (15) and (16) are limitations for the maximum amount of charging and discharging of each ESS, respectively. The binary variable u i, ,t is applied to avoid the charge and discharge of ESSs at the same time.…”

Section: Developed Scheduling Modelmentioning

confidence: 99%

“…Moreover, some research studies implemented other methods to measure risk in VPP's dispatch problem. First‐order stochastic dominance constraint is applied in Shabanzadeh et al to enable the model to find the optimal decisions under different levels of uncertainty. Scheduling of a VPP which consists of several hubs is done with benders decomposition in Gerami Moghaddam .…”

Section: Introductionmentioning

confidence: 99%

A two‐stage stochastic programming framework for risk‐based day‐ahead operation of a virtual power plant

Emarati

Keynia

Rashidinejad

2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary With the increasing use of distributed energy resources (DERs), new technical and economic issues have been raised in power systems. Integration of DERs and energy storage systems (ESSs) in the form of virtual power plant (VPP) resolves an important part of these issues. This paper proposes a risk‐based two‐stage stochastic optimization framework to address the energy management problem for a VPP. The objective of the proposed framework is to optimize the operation of a VPP in day‐ahead (DA) and real‐time (RT) markets. In order to include the risk parameter in the proposed decision‐making problem, conditional value at risk (CVaR) index is applied in the objective function. The considered uncertain parameters in the model are price in DA market, as well as wind and solar generation for the next day. Markov chain Monte Carlo (MCMC) method is applied to model these uncertain parameters through generation of different scenarios. Also, the effects of using ESS on daily operation of considered VPP is investigated. The performance of the proposed method is illustrated through a case study using real data. The obtained results guarantee the appropriate operation of a VPP considering different values for level of the risk.

show abstract

“…As far as the term C market th g nth is now changed into C stack b z thb g nth , this causes nonlinearity in the objective function. In order to handle this nonlinearity, multiplication of a binary and a continuous variable is changed into an independent variable (zg nthb ) considering a technique as follows [29].…”

Section: B Modelling Of Energy Pricementioning

confidence: 99%

An A-Posteriori Multi-Objective Optimization Method for MILP-Based Distribution Expansion Planning

2020

View full text Add to dashboard Cite

Distribution expansion planning (DEP) is a mixed integer nonlinear programming (MINLP) problem and contains various options to be considered where the nonlinearity makes it difficult to solve. Over the years, different heuristic and classical optimization methods have been introduced to solve this problem. In a few classical optimization methods, this problem has been linearized. In this contribution, an-constraint based multi-objective method was proposed for mixed integer linear programming (MILP) based DEP model considering network loss. Operational network loss was linearized and incorporated in the model, then the operational loss cost was obtained by modeling the production cost. In order to model the production cost, the well-known supply price curve was considered, where the price of energy for each loading condition was obtained based on a linear model. Using the-constraint method, the Pareto optimal front was formed, which provides the decision maker with a range of solutions showing relationship among conflicting objectives. Then, a Fuzzy satisfying method was utilized to obtain the best compromised solution. Simulations were performed on an 18-node primary distribution network. Results showed the effectiveness of the proposed method for the MILP-based multi-objective DEP models. INDEX TERMS Mixed-integer linear programming, distribution expansion planning, power market, operational network loss, multi-objective optimization,-constraint optimization, a-posteriori. Feeder sections of i-th type B ij Alternative sizes of j-th branch of i-th type LC

show abstract

Risk‐based medium‐term trading strategy for a virtual power plant with first‐order stochastic dominance constraints

Cited by 47 publications

References 21 publications

VPP Self-Scheduling Strategy Using Multi-Horizon IGDT, Enhanced Normalized Normal Constraint, and Bi-Directional Decision-Making Approach

VPP Self-Scheduling Strategy Using Multi-Horizon IGDT, Enhanced Normalized Normal Constraint, and Bi-Directional Decision-Making Approach

A two‐stage stochastic programming framework for risk‐based day‐ahead operation of a virtual power plant

An A-Posteriori Multi-Objective Optimization Method for MILP-Based Distribution Expansion Planning

Contact Info

Product

Resources

About