Ahmad Hably

Rule-Based Charging of Plug-in Electric Vehicles (PEVs): Impacts on the Aging Rate of Low-Voltage Transformers

Massive deployment of plug-in electric vehicles (PEVs) in the coming years will create more challenges for the power system including the aging rate of transformers. It will be an essential requirement to propose solutions to minimize the impacts related to the integration of PEVs. Special attention must be given to the residential electric grid where charging will mostly take place. In this paper, first we propose a rule-based (RB) algorithm which determines the minimum charging power levels of home-charged PEVs with/without a charging ban during peak hours. Second, we evaluate the consequences of supplying an RB algorithm on life duration of a low-voltage transformer supplying a residential area.

A comparative study of low sampling non intrusive load dis-aggregation

Non-intrusive load monitoring (NILM) deals with the identification and subsequent energy estimation of the individual appliances from the smart meter data. The state of the art applications typically runs once per day and reports the detected appliances. In this work, data driven models are implemented for two different sampling rates (10 seconds and 15 minutes). The models are trained for 20 houses in the Netherlands and tested for a period of 4-weeks. The results indicate that the disaggregation methods is applicable for both sampling cases but with different use-case.

Escort Evolutionary Game Dynamics Approach for Integral Load Management of Electric Vehicle Fleets

This paper proposes an application of an evolutionary game dynamics called the escort dynamics (ED) for the decentralized load management of plug-in electric vehicles (PEV). Different from earlier contributions, in the present approach, PEVs work together in a fair scheme in order to provide several ancillary services to the grid: Load shifting, active power balancing, and partial supply of reactive power demand on each phase of the distribution transformer. Meanwhile, batteries are guaranteed to be fully charged according to the constraints imposed by the owners. In the proposed formulation, chargers can be either three phase or single phase; however, in this paper, only three-phase chargers are considered. The key concepts behind ED, especially for escort functions, are provided at the beginning of this paper. Based on these concepts, the assumptions and analogies followed for the construction of the proposed approach are explained in detail, especially for the proposed definition of escort functions. A multipopulation scenario is proposed for the interaction of several PEVs using local ED routines. This interaction among populations follows another well-known evolutionary game dynamics called the best reply dynamics. Performance is evaluated using real data measured from a distribution transformer from the SOREA utility grid company in the region of Savoie, France.

An Electric Vehicle Load Management Application of the Mixed Strategist Dynamics and the Maximum Entropy Principle

An application of an evolutionary game dynamics called mixed strategist dynamics (MSD), for the decentralized load scheduling of plug-in electric vehicles (PEVs), is proposed in this paper. Following an analogy with the maximum entropy principle (MEP) for tuning parameters of discrete probability distributions, entropy of the total load distribution and the local load distributions are considered as objectives of the scheduling approach, and a tradeoff among them is defined by the electric vehicle owners convenience. While entropy maximization for the local load distributions contributes to preserve the batteries states of health, entropy maximization for the total load distribution reduces the undesirable peak effects over the transformer loading. The problem is formulated such that final states of charge are assured depending on time constraints defined by the owners. Furthermore, mixed strategies in the MSD are defined such that they represent the vertices of the convex set of feasible load profiles which results from the constraints imposed by owners and chargers. The synergy of several PEVs is modeled as an application of the MSD in a multipopulation scenario, where the interaction among populations follows another evolutionary game dynamics called best reply (BR) dynamics. The performance of the proposed approach is tested on real data measured on a distribution transformer from the SOREA utility grid company in the region of Savoie, France.

Plug-in electric vehicle collaborative charging for current unbalance minimization: Ant system optimization application

Plug-in electric vehicles (PEVs) are one of the solutions to reduce transportation dependency on oil. Nevertheless, uncoordinated charging in distribution low voltage (LV) networks can lead to local grid problems such as current unbalance and consequently voltage unbalance. In this paper, a combinatorial method based on Ant System (AS) optimization is proposed in order to minimize the current unbalance factor (CUF) by controlling the connection and disconnection of PEVs. The CUF is generated by PHEVs and neighboring residential loads. In addition a simulation environment is implemented to validate the performance of the AS method by benchmarking it against an Exhaustive Search approach. Results show that AS method gives satisfactory optimal solutions reducing the computation cost for scenarios having several hundred of PEVs.

A distributed cooperative control scheme with optimal priority assignment and stability assessment

In this paper, a distributed partially cooperative control framework is proposed for a network of linear interconnected subsystems. It is assumed that each subsystem in the network possesses its own objective and a corresponding nominal interaction-free state feedback law. The proposed framework enables each subsystem to compute an additional control term in order to help maintaining the integrity of the overall network. As this cooperation-like behavior involves relative priority assignment, a communication aware heuristic is proposed with an associated stability assessment that is based on the closed-loop network matrixs spectrum monitoring. Illustrative examples are used to assess the effectiveness of the proposed scheme including a distributed load frequency problem.

Control of a Magnus Effect-Based Airborne Wind Energy System

This chapter studies the control of an airborne wind energy system that is operated in pumping cycles and uses a rotating cylinder to provide aerodynamic lift with the Magnus effect. The proposed control strategy aims at stabilizing the output power production which can be used for off-grid applications, for example. In a first case study, the wind tunnel setup of a small-scale system is investigated experimentally and by means of numerical simulation. The proposed controller works well to effectively manage the tether length. However, a comparison of the results demonstrates the penalizing effects of wind turbulence with a factor of three difference in power production. In a second case study, the control strategy is used for the numerical simulation of a medium scale prototype with a potential power rating of 50 kW. The results show that the control strategy is very effective to track the desired power production even in the presence of wind velocity fluctuations. In a third case study, the scalability of the system is evaluated by applying the control scheme to the numerical simulation of a MW scale platform. The results show that the system with a span equal to the diameter of a conventional wind turbine can generate an equivalent amount of power.

Dynamic Programming and Potential Game Approach

This chapter discusses the details of a decentralized approach to optimally manage PEV charging schedules based on Dynamic Programming (DP) and Game Theory [OHB15].

Evolutionary Game Theory Approach Part II: Escort Dynamics

In this chapter, the second part of an evolutionary game theory approach for the decentralized PEV load scheduling problem is presented. This approach is based on the application of a family of evolutionary game dynamics called Escort Dynamics (ED). In this application, a multi-population scenario is considered for representing PEV energy and reactive power quantities to be distributed over the three phases of the system and over multiple time slots in a given time horizon.

Evolutionary Game Theory Approach Part I: Mixed Strategist Dynamics

In this chapter, the first part of an evolutionary game theory approach for decentralized PEV load scheduling is presented.