Optimal dispatch of virtual power plant using interval and deterministic combined optimization

Liu, Yangyang; Min, Li; Lian, Hongbo; Tang, Xiaocheng; Liu, Chuanquan; Jiang, Chuanwen

doi:10.1016/j.ijepes.2018.04.011

Cited by 78 publications

(31 citation statements)

References 17 publications

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“…Various real-world applications can be formulated as interval optimization problems, such as optimal dispatch of a virtual power plant [26], aircraft wing and automobile design [27], [28], and household load scheduling [29]. As a result, seeking approaches suitable for addressing interval optimization problems is of great significance in theory and applications, and among which interval analysis is one of representative tools [30].…”

Section: Interval Multi-objective Evolutionary Optimizationmentioning

confidence: 99%

A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization Problems

Gong

Zhang

et al. 2020

IEEE Trans. Evol. Computat.

144

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Dynamic interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances.

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Section: Interval Multi-objective Evolutionary Optimizationmentioning

confidence: 99%

A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization Problems

Gong

Zhang

et al. 2020

IEEE Trans. Evol. Computat.

144

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show abstract

“…The Latin hypercube sampling (LHS) method has been used to simulate wind and solar generation scenarios. In Liu et al, VPP's dispatch problem is solved based on the combination of interval and deterministic optimization. The authors claimed that the provided approach has lower computational burden compared with robust and stochastic optimization methods.…”

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

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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.

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“…The upper level optimizes the exchange power curve and reduces the imbalance costs in intraday, while the lower level tracks the optimized power curve in real-time to complete the regulation target. Liu et al 6 combined interval and deterministic optimization together and adopted the combined approach to solve a VPPs' dispatch problem. The combined optimization not only maximized VPPs' deterministic profits under forecasted scenarios to estimate the VPPs' most likely profits, but also maximized VPPs' profit intervals to manage uncertainties.…”

Section: Introductionmentioning

confidence: 99%

A load-balancing power scheduling system for virtual power plant considering emission reduction and charging demand of moving electric vehicles

Huang

Liu

et al. 2019

Measurement and Control

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With the rapid development of the emerging technologies and significant cost reduction of the deployment for solar energy and wind power, the replacement of traditional power generation by renewable energy becomes feasible in the future. However, different from currently deployed centralized power sources, renewables are categorized as one kind of intermittent energy sources, and the scale of renewables is small and scattered. In the recent literature, the architecture of virtual power plant was proposed to replace the current smart grid in the future. However, the energy sharing concept and the uncertainties of intermittent energy sources will cause the short-term energy management for the virtual power plant much more complicated than the current centralized control energy management for traditional power generation system. We thus propose a hierarchical day-ahead power scheduling system for virtual power plant in this work to tackle the complex short-term energy management problems. We first collect electricity consumption data from smart appliances used in households and predict power-generating capacity of renewable energy sources at the prosumer level. Then, the proposed hierarchical power scheduling system is employed to schedule the usage of electricity for the customers by considering the efficiency of the use of distributed renewables. Notably, charging management of a moving electric vehicle is also considered in the proposed power scheduling mechanism. In addition, a real-time power tracking mechanism is presented to deal with the forecast errors of volatile renewable power generation, electricity load, and moving electric vehicle charging, and the maximal usage of renewables and reduction of the burden on community virtual power plants during time period of peak load can be achieved accordingly. The experimental results show that the proposed day-ahead power scheduling system can mitigate the dependency on traditional power generation effectively, and balance peak and off-peak period load of electricity market.

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Optimal dispatch of virtual power plant using interval and deterministic combined optimization

Cited by 78 publications

References 17 publications

A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization Problems

A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization Problems

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

A load-balancing power scheduling system for virtual power plant considering emission reduction and charging demand of moving electric vehicles

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