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Optimization of Power Cost for a Community with Distributed Sources and Electric Vehicles
Abstract
The renewable energy sources, cogeneration and trigeneration units, loads that are connected together by means of a controller form a microgrid. In this paper is presented the optimization of the power cost for a small community (a microgrid) that comprises loads and four distributed sources (two photovoltaic, one wind turbine and one micro-hydro). In the microgrid there are also present electric vehicles. The power demand of the microgrid loads is added with the power required by the electric vehicles, which will be charged in a controlled manner. The optimization of the power cost is conducted by means of the fmincon function from the MATLAB software. Several charging scenarios of the electric vehicles will be considered, as well as the power cost from the power markets (day-ahead market and bilateral contracts market) in 2021 and 2022. The results give the optimal power cost for the microgrid and the best option to supply the microgrid.KeywordsOptimizationPower costMicrogridDistributed sourcesElectric vehiclesPower markets
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With high electric vehicle (EV) adoption, optimization of the charging process of EVs is becoming increasingly important. Although the CO2 emission impact of EVs is heavily dependent on the generation mix at the moment of charging, emission minimization of EV charging receives limited attention. Generally, studies neglect the fact that cost and emission savings potential for EV charging can be constrained by the capacity limits of the low-voltage (LV) grid. Grid reinforcements provide EVs more freedom in minimizing charging costs and/or emissions, but also result in additional costs and emissions due to reinforcement of the grid. The first aim of this study is to present the trade-off between cost and emission minimization of EV charging. Second, to compare the costs and emissions of grid reinforcements with the potential cost and emission benefits of EV charging with grid reinforcements. This study proposes a method for multi-objective optimization of EV charging costs and/or emissions at low computational costs by aggregating individual EV batteries characteristics in a single EV charging model, considering vehicle-to-grid (V2G), EV battery degradation and the transformer capacity. The proposed method is applied to a case study grid in Utrecht, the Netherlands, using highly-detailed EV charging transaction data as input. The results of the analysis indicate that even when considering the current transformer capacity, cost savings up to 32.4% compared to uncontrolled EV charging are possible when using V2G. Emission minimization can reduce emissions by 23.6% while simultaneously reducing EV charging costs by 13.2%. This study also shows that in most cases, the extra cost or emission benefits of EV charging under a higher transformer capacity limit do not outweigh the cost and emissions for upgrading that transformer.
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With the implementation of vehicle-to-grid technologies, electric vehicles in distribution systems are becoming controllable resources and enabled to provide a lot of ancillary services (e.g. peak power shaving, voltage regulation, spinning reserve and so on). This phenomenon brings positive effects to the operation of distribution systems but simultaneously challenges the deployments of related power devices, especially the distributed generation resources. Based on this background, this paper proposes an optimization model to jointly deploy electric vehicle charging stations and distributed generation resources, during which the vehicle-to-grid function of electric vehicles is comprehensively considered. To make the optimization model ideally convex, linearized Distflow equations, as well as an exact second order conic relaxation are adopted and utilized in this paper. Consequently, the proposed model can be efficiently solved by off-the-shelf commercial solvers and the allocation schemes with minimal annualized social costs are obtained in polynomial time. Finally, a practical urban area fed by a 31-bus distribution system in China is selected as the test system to verify the effectiveness of the proposed approach, and numerical results are analyzed.
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This paper proposes a new optimal operation of Microgrids (MGs) in a distribution system with wind energy generators (WEGs), solar photovoltaic (PV) energy systems, battery energy storage (BES) systems, electric vehicles (EVs) and demand response (DR). To reduce the fluctuations of wind, solar PV powers and load demands, the BES systems and DR are utilized in the proposed hybrid system. The detailed modeling of WEGs, solar PV units, load demands, BES systems and EVs has been presented in this paper. The objective considered here is the minimization of total operating cost of microgrid, and it is formulated by considering the cost of power exchange between the main power grid and microgrid, cost of wind and solar PV energy systems, cost of BES systems, EVs and the cost due to the DR in the system. Simulations are performed on a test microgrid, and they are implemented using GAMS software. Various case studies are performed with and without considering the proposed hybrid system.
A power balancing method of distributed generation (DG) and electric vehicle charging is presented for minimizing operation costs of distribution systems with uncertainties, which includes the uncertainties in output power of DG and the randomness of charging power of electric vehicles (EV). A probability model is established for the uncertain characteristics of DG and electric vehicle charging. A multi-state optimization coordination method for DG of renewable energy systems and electric vehicle charging in distribution systems based on quadratic rotation cone programming is presented to minimize the expected generation cost of generators in the main power grid, the expected operation cost of DG systems in distribution systems, and the expected social outage loss. An objective function maximizing the operation efficiency of distribution systems with DGs and EVs is proposed. Using quadratic rotated conic programming, the nonlinear objective function and constraint functions are transformed into a linear form. An IEEE 14-node distribution system is used as a study example to illustrate adaptability of the proposed model and the feasibility of the proposed method. The simulation results show that the proposed method simplifies the original problem of the optimization problem and makes its solution faster, more stable, and better. © 2017 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.
The need for developing new methodologies in order to improve power system stability has increased due to the recent growth of distributed energy resources. In this paper, the inclusion of a voltage stability index in distributed energy resources scheduling is proposed. Two techniques were used to evaluate the resulting multiobjective optimization problem: the sum-weighted Pareto front and an adapted goal programming methodology. With this new methodology, the system operators can consider both the costs and voltage stability. Priority can be assigned to one objective function according to the operating scenario. Additionally, it is possible to evaluate the impact of the distributed generation and the electric vehicles in the management of voltage stability in the future electric networks. One detailed case study considering a distribution network with high penetration of distributed energy resources is presented to analyse the proposed methodology. Additionally, the methodology is tested in a real distribution network.
In order to reduce greenhouse gas emissions, governments seek to replace conventional fuels by renewable ones. Nowadays, most attention is paid to electric vehicles in the transport systems and the use of renewable energy in the power systems. The aim of this work is to achieve a 100 % renewable and sustainable system and to examine the impact of electrification in the transport sector on the power curve and the integration of renewable energy into the power systems of the Dubrovnik region up to 2050.
The analyses of different charging regulation models for the electric vehicles were derived in the EnergyPLAN, which is a computer model for Energy Systems Analysis of the major energy systems and runs on an hourly basis. Calculations were done for selected years—2020, 2030 and 2050. Charging models provided in the EnergyPLAN were dumb charge, flexible demand, smart charge and smart charge with vehicle-to-grid. For each year, two different charging models were selected. Charging regulations according to three tariff models, based on a lower and higher electricity price, with different distributions, were also done for 2050, i.e. tariff model 1, 2 and 3.
The results for the year 2020 showed no difference between the models. In 2030, smart charge gained better results than a flexible demand. In 2050, the flexible demand allowed to achieve better results than the smart charge with vehicle-to-grid and the tariff model 1, while tariff model 3 provided the best results for 2050. It is also shown that the energy systems which include electric vehicles have a greater impact on the reduction of a critical excess electricity production than the systems excluding electric vehicles.
The power system of the Dubrovnik region was set up as an isolated system, and the electric vehicle batteries are the only storage provided. The results showed that each scenario yielded an excess in electricity produced in the system, which means that the available storage was insufficient and there was a need for more storage capacities in order to achieve a 100 % renewable and sustainable power system.
The 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 the intensive use of distributed generation and V2G. The main focus 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 (trip reduce, shifting reduce and reduce+shifting). Other important contribution of the paper is the comparison between deterministic and computational intelligence techniques to reduce the 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.
The large scale penetration of electric vehicles (EVs) will introduce technical challenges to the distribution grid, but also carries the potential for vehicle-to-grid services. Namely, if available in large enough numbers, EVs can be used as a distributed energy resource (DER) and their presence can influence optimal DER investment and scheduling decisions in microgrids. In this work, a novel EV fleet aggregator model is introduced in a stochastic formulation of DER-CAM [1], an optimization tool used to address DER investment and scheduling problems. This is used to assess the impact of EV interconnections on optimal DER solutions considering uncertainty in EV driving schedules. Optimization results indicate that EVs can have a significant impact on DER investments, particularly if considering short payback periods. Furthermore, results suggest that uncertainty in driving schedules carries little significance to total energy costs, which is corroborated by results obtained using the stochastic formulation of the problem.
Due to the intermittent nature, renewable energy sources (RES) has brought new challenges on load balancing and energy dispatching to the Smart Grid. Potentially served as distributed energy storage, Electric Vehicle’s (EV) battery can be used as a way to help mitigate the pressure of fluctuation brought by RES and reinforce the stability of power systems. This paper gives a comprehensive review of the current situation of EV technology and mainly emphasizing three EV discharging operations which are Vehicle to Grid (V2G), Vehicle to Home (V2H), and Vehicle to Building (V2B), respectively. When needed, EV’s battery can discharge and send its surplus energy back to power grid, residential homes, or buildings. Based on our data analysis, we argue that V2G with the largest transmission power losses is potentially less efficient compared with the other two modes. We show that the residential users have the incentive to schedule the charging, V2G, and V2H according to the real-time price (RTP) and the market sell-back price. In addition, we discuss some challenges and potential risks resulting from EVs’ fast growth. Finally we propose some suggestions on future power systems and also argue that some incentives or rewards need to be provided to motivate EV owners to behave in the best interests of the overall power systems.
Renewable energy and Electric Vehicles (EVs) are promising solutions for energy cost savings and emission reduction. However, integration of renewable energy sources into the electric grid could be a difficult task, because of the generation source intermittency and inconsistency with energy usage. In this paper, we present results of our study on the problem of allocating energy from renewable sources to EVs in a cost efficient manner. We have assumed that the renewable energy supply is time variant and in many ways unpredictable. EVs' charging requests should be satisfied within a specified time frame, which may incur a cost of drawing additional energy (possibly non-renewable energy) from the power grid if the renewable energy supply is not sufficient to meet the deadlines and may also reduce energy efficiency. We have formulated a stochastic optimization problem based on queuing model to minimize the time average cost of using non-renewable energy sources. The proposed approach fully considers the individual charging rate limit and deadline of each EV. The Lyapunov optimization technique is used to solve the problem. The developed dynamic control algorithm does not require knowledge of the statistical distribution of the time-varying renewable energy generation, EV charging demand, or extra energy pricing. Simulation results using different wind power generation profiles were performed and analyzed in the study. The results show that our EV charging scheduling method based on Lyapunov optimization can reduce both charging cost and mean delay time of fulfilling EV charging requests.
In this paper, we propose a new model of demand response management for the future smart grid that integrates plug-in electric vehicles and renewable distributed generators. A price scheme considering fluctuation cost is developed. We consider a market where users have the flexibility to sell back the energy generated from their distributed generators or the energy stored in their plug-in electric vehicles. A distributed optimization algorithm based on the alternating direction method of multipliers is developed to solve the optimization problem, in which consumers need to report their aggregated loads only to the utility company, thus ensuring their privacy. Consumers can update their loads scheduling simultaneously and locally to speed up the optimization computing. Using numerical examples, we show that the demand curve is flattened after the optimization, even though there are uncertainties in the model, thus reducing the cost paid by the utility company. The distributed algorithms are also shown to reduce the users' daily bills.
Optimizarea fiabilităţii sistemelor electrice
- D Sarchiz