Dynamic electricity pricing for electric vehicles using stochastic programming

Soares, João; Ghazvini, Mohammad Ali Fotouhi; Borges, Nuno; Vale, Zita

doi:10.1016/j.energy.2016.12.108

Cited by 53 publications

(22 citation statements)

References 40 publications

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“…The energy management aggregator (EMA) in an SG (Fig. 1) is defined as any organization or individual that uses several resources with the objective of obtaining reduced prices, better services, or more benefits when providing energy or any related services to the group of retail energy customers [45]. Furthermore, the EMA can use its own assets like ESUs to supply the required load.…”

Section: Problem Formulation a Descriptionmentioning

confidence: 99%

Solving the Problem of Large-Scale Optimal Scheduling of Distributed Energy Resources in Smart Grids Using an Improved Variable Neighborhood Search

Ramli

Bouchekara

2020

IEEE Access

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Since the last decade, power systems have been evolving dynamically due to smart grid technologies. In this context, energy management and optimal scheduling of different resources are very important. The main objective of this paper is to study the optimal scheduling of distributed energy resources (OSDER) problem. This problem is a challenging, complex and very large-scale mixed-integer non-linear programming (MINLP) problem. Its complexity escalates with incorporation of uncertain and intermittent renewable sources, electric vehicles, variable loads and markets which makes it hard to be solved using traditional optimization algorithms and solvers. However, it can be handled efficiently and without approximation or modification of the original formulation using modern optimization algorithms such as metaheuristics. In this paper, an improved version of the variable neighborhood search (IVNS) algorithm is proposed to solve the OSDER problem. The proposed algorithm was tested on two large-scale centralized day-ahead energy resource scenarios. In the first scenario, the 12.66 kV, 33-bus test system with a total of 49,920 design variables is used whilst in the second scenario, the 30 kV, 180-bus test system is used with a total of 154,800 design variables. The optimization results using the proposed algorithm were compared with five existing optimization algorithms, i.e., chaotic biogeography-based optimization (CBBO), cross-entropy method and evolutionary PSO (CEEPSO), chaotic differential evolution with PSO (Chaotic-DEEPSO), Levy differential evolution with PSO (Levy-DEEPSO), and the variable neighborhood search (VNS). For the first test system, the IVNS has achieved a score of-5598.89 while for the second test system it has achieved a score of-3180.15. A comparative study of the results has shown that the proposed IVNS algorithm performs better than the remaining algorithms for both cases.

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Section: Problem Formulation a Descriptionmentioning

confidence: 99%

Solving the Problem of Large-Scale Optimal Scheduling of Distributed Energy Resources in Smart Grids Using an Improved Variable Neighborhood Search

Ramli

Bouchekara

2020

IEEE Access

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“…A stochastic model was also proposed here to consider the uncertainty of the electricity market data. In [18], the authors formulated a stochastic model for day-ahead energy resource scheduling integrated with the dynamic electricity pricing for EVs to address the challenges brought by the demand and renewable sources uncertainty. Meanwhile, in [19], the problem of the optimal scheduling problem of plug-in electric vehicle aggregators in the electricity market considering the uncertainties of market prices, availability of vehicles, and status of being called by the Independent system operator (ISO) in the reserve market was reviewed.…”

Section: Introductionmentioning

confidence: 99%

EV Aggregators and Energy Storage Units Scheduling into Ancillary Services Markets: The Concept and Recommended Practice

Aldik

Khatib

2019

WEVJ

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In this paper, optimization of Electric Vehicle (EV) batteries and dedicated energy storage unit charging profiles were conducted for the sake of bidding into day-ahead ancillary service markets. The aim of the optimization is to provide the maximum operational profits for both the EV aggregator and dedicated energy storage unit administrator. Ancillary service algorithms were then introduced to simulate the response of the EV batteries and dedicated energy storage units. Results showed that the usage of dedicated energy storage units for bidding into the ancillary services markets is more profitable than the case of operating an EV aggregator.

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“…This analysis also suggests that research on EVs in the smart grid context is yet in the early stage. Although many theoretical concepts and ideas have been proposed (e.g., dynamic pricing [38,39] and vehicle-to-grid [6,40]), both EVs and smart grid are yet in the early stage of introduction and lessons have to be learned to better understand how EVs can realize the full smart grid potential. Energy resource management-related research has increased steadily along with the research in DR topics since its importance to the new paradigm of power systems (see Section 3.2).…”

Section: Complexitymentioning

confidence: 99%

Survey on Complex Optimization and Simulation for the New Power Systems Paradigm

et al. 2018

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This survey provides a comprehensive analysis on recent research related to optimization and simulation in the new paradigm of power systems, which embraces the so-called smart grid. We start by providing an overview of the recent research related to smart grid optimization. From the variety of challenges that arise in a smart grid context, we analyze with a significance importance the energy resource management problem since it is seen as one of the most complex and challenging in recent research. The survey also provides a discussion on the application of computational intelligence, with a strong emphasis on evolutionary computation techniques, to solve complex problems where traditional approaches usually fail. The last part of this survey is devoted to research on large-scale simulation towards applications in electricity markets and smart grids. The survey concludes that the study of the integration of distributed renewable generation, demand response, electric vehicles, or even aggregators in the electricity market is still very poor. Besides, adequate models and tools to address uncertainty in energy scheduling solutions are crucial to deal with new resources such as electric vehicles or renewable generation. Computational intelligence can provide a significant advantage over traditional tools to address these complex problems. In addition, supercomputers or parallelism opens a window to refine the application of these new techniques. However, such technologies and approaches still need to mature to be the preferred choice in the power systems field. In summary, this survey provides a full perspective on the evolution and complexity of power systems as well as advanced computational tools, such as computational intelligence and simulation, while motivating new research avenues to cover gaps that need to be addressed in the coming years.

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Dynamic electricity pricing for electric vehicles using stochastic programming

Cited by 53 publications

References 40 publications

Solving the Problem of Large-Scale Optimal Scheduling of Distributed Energy Resources in Smart Grids Using an Improved Variable Neighborhood Search

Solving the Problem of Large-Scale Optimal Scheduling of Distributed Energy Resources in Smart Grids Using an Improved Variable Neighborhood Search

EV Aggregators and Energy Storage Units Scheduling into Ancillary Services Markets: The Concept and Recommended Practice

Survey on Complex Optimization and Simulation for the New Power Systems Paradigm

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