Optimal scheduling of electric vehicles considering uncertain RES generation using interval optimization (original) (raw)
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Resource Scheduling Under Uncertainty in a Smart Grid With Renewables and Plug-in Vehicles
IEEE Systems Journal, 2012
The power system and transportation sector are our planet's main sources of greenhouse gas emissions. Renewable energy sources (RESs), mainly wind and solar, can reduce emissions from the electric energy sector; however, they are very intermittent. Likewise, next generation plug-in vehicles, which include plug-in hybrid electric vehicles and electric vehicles with vehicle-to-grid capability, referred to as gridable vehicles (GVs) by the authors, can reduce emissions from the transportation sector. GVs can be used as loads, energy sources (small portable power plants) and energy storage units in a smart grid integrated with renewable energy sources. However, uncertainty surrounds the controllability of GVs. Forecasted load is used in unit commitment (UC); however, the actual load usually differs from the forecasted one. Thus, UC with plug-in vehicles under uncertainty in a smart grid is very complex considering smart charging and discharging to and from various energy sources and loads to reduce both cost and emissions. A set of valid scenarios is considered for the uncertainties of wind and solar energy sources, load and GVs. In this paper, an optimization algorithm is used to minimize the expected cost and emissions of the UC schedule for the set of scenarios. Results are presented indicating that the smart grid has the potential to maximally utilize RESs and GVs to reduce cost and emissions from the power system and transportation sector.
Modified interval‐based generator scheduling for PFR adequacy under uncertain PV generation
IET Generation, Transmission & Distribution, 2019
Large quantum of photovoltaic (PV) generation into the grid would reduce system's primary frequency response (PFR) capability. Adequate PFR is critical to provide rapid frequency stability after contingencies such as large generation outage. PFR estimation and its scheduling within acceptable operational time frames is a challenging task for system operator. Uncertain PV generation makes this problem critical. Existing models for PV uncertainty characterisation with stochastic scheduling are computationally demanding. This study proposes a computationally fast modified interval scheduling approach for operation and PFR cost minimisation. Uncertainty is modelled through interval forecasting, while hourly ramp needs are based on net load scenario. The proposed model is compared with stochastic scheduling approach for PFR performance, overall cost performance, computational time and PV curtailment. Numerical results show that the proposed model drastically reduces simulation time for similar cost performance.
Optimal-Cost Scheduling of Electrical Vehicle Charging Under Uncertainty
IEEE Transactions on Smart Grid, 2017
Electric vehicle (EV) charging stations are increasingly set up to meet the recharge demand of EVs, and the stations equipped with local renewable energy generation need to optimize their charging. A basic challenge for the optimization stems from inherent uncertainties such as intermittent renewable generation that is hard to predict accurately. In this paper, we consider a charging station for EVs that have deadline constraints for their requests, and aim to minimize its supply cost. We use Lyapunov optimization to minimize the time-average cost under unknown renewable supply, EV mobility, and grid electricity prices. We model the unfulfilled energy requests as a novel system of queues, based on whose evolution we define the Lyapunov drift and minimize it asymptotically. We prove that our algorithm achieves at most O(1 V) more than the optimal cost, where the parameter V trades off cost against unfulfilled requests by their deadlines, and its time complexity is linear in the number of EVs. Simulation results driven by real-world traces of wind power, EV mobility, and electricity prices show that, compared with a state-of-theart scheduling algorithm, our algorithm reduces the respective charging costs by 12.48% and 51.98% for two scenarios.
Electric Power Components and Systems, 2019
In this paper a new framework for scheduling of available resources in the distribution networks is developed. In this respect attempts are focused on interactions between charging/ discharging profiles of electric vehicles (EVs) and output power of distributed generation units. To reach this goal, the proposed framework is designed as a two-stage optimization procedure. In the first stage, the charging/discharging schedules of EVs are extracted running a linear programing optimization problem taking into account the EV users' constraints and requirements. The usage profiles of the DG units, strategy of buying electricity from the market and also the final charging/discharging patterns of the EVs are set running the second stage of this scheduling framework. The attained mixed-integer non-linear programing optimization problem is linearized and a procedure is organized to check the technical aspect of network. The results show that the proposed energy resource scheduling method satisfies financial and technical goals of network.
Optimal Scheduling Methods to Integrate Plug-In Electric Vehicles with the Power System: A Review
Proceedings of the 19th IFAC World Congress, 2014
The introduction of the Tesla in 2008 has demonstrated to the public of the potential of electric vehicles in terms of reducing fuel consumption and green-house gas from the transport sector. It has brought electric vehicles back into the spotlight worldwide at a moment when fossil fuel prices were reaching unexpected high due to increased demand and strong economic growth. The energy storage capabilities from of fleets of electric vehicles as well as the potentially random discharging and charging offers challenges to the grid in terms of operation and control. Optimal scheduling strategies are key to integrating large numbers of electric vehicles and the smart grid. In this paper, state-of-the-art optimization methods are reviewed on scheduling strategies for the grid integration with electric vehicles. The paper starts with a concise introduction to analytical charging strategies, followed by a review of a number of classical numerical optimization methods, including linear programming, non-linear programming, dynamic programming as well as some other means such as queuing theory. Metaheuristic techniques are then discussed to deal with the complex, high-dimensional and multiobjective scheduling problem associated with stochastic charging and discharging of electric vehicles. Finally, future research directions are suggested.
Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017), 2017
With the dramatic increase of plug-in electric vehicles (EVs) grid penetration, the random characteristics of EVs will influence the normal operation of the power system. Given this background, a multi-objective optimization model is proposed in this paper to mitigate the peak-to-valley deference of equivalent load and reduce the active power losses of the distributed grid for a regional electrical power system, taking the storage capacity of EVs, the charging/discharging power, the distributed power flow, and the driving characteristics of EVs into consideration. Defining each objective membership function, multi-objective optimization problem is reformulated into a nonlinear single-objective programming problem by means of fuzzy satisfaction-maximizing method, and this nonlinear single-objective programming problem is solved by using modified particle swarm optimization algorithm based on hybrid mechanism. Simulation results indicate that the proposed model and algorithm can flat the curve of equivalent load, reduce the reserved capacity in adjusting the peak, optimize the active power losses and provide the voltage support for the system.
Day-Ahead Resource Scheduling Including Demand Response for Electric Vehicles
IEEE Transactions on Smart Grid, 2013
Energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and V2G. The main focus of this paper is the comparison of different EV management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs in the V2G approach. Three different DR programs are designed and tested in this paper (trip reduce, shifting reduce and reduce+shifting). Other important contribution of the paper is the comparison between deterministic and computational intelligence techniques to reduce execution time. The proposed scheduling is solved with a modified particle swarm optimization. Mixed integer non-linear programming is also used for comparison purposes. Full ac power flow calculation is included to allow taking into account the network constraints. A case study with a 33 bus distribution network and 2000 V2G resources is used to illustrate the performance of the proposed method.
2016
The advantage of grid-to-vehicle power over vehicle to grid is that the existing power grid infrastructure and technology are able to support its operation. In this paper, a regulation operation framework for grid-to-vehicle-based plug-in electric vehicle (PEV) aggregator is proposed. Based on that, PEVs can provide regulation services during charging. The objective function consider the influence of regulation services on the energy of battery charging for PEVs and the constraint of systems for battery charging power of PEVs, achieving the maximum aggregator earnings. A regulation algorithm is proposed for the aggregator to schedule PEVs. The algorithm considers the constraint of PEVs battery capacity and reduces the communication traffic between the aggregator and the PEVs. Simulation results indicate that the optimal scheduling can not only increase the earnings of the aggregator but also reduce the charging cost of PEV owners on the basis of meeting the charging requirements of PEVs.
Energies, 2021
This paper presents a robust optimization model to find out the day-ahead energy and reserve to be scheduled by an electric vehicle (EV) aggregator. Energy can be purchased from, and injected to, the distribution network, while upward and downward reserves can be also provided by the EV aggregator. Although it is an economically driven model, the focus of this work relies on the actual availability of the scheduled reserves in a future real-time. To this end, two main features stand out: on one hand, the uncertainty regarding the EV driven pattern is modeled through a robust approach and, on the other hand, a set of non-anticipativity constraints are included to prevent from unavailable future states. The proposed model is posed as a mixed-integer robust linear problem in which binary variables are used to consider the charging, discharging or idle status of the EV aggregator. Results over a 24-h case study show the capability of the proposed model.
Renewable and Sustainable Energy Reviews, 2015
Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.