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Optimal behavior of electric vehicle parking lots as demand response aggregation agents
Abstract
With increasing environmental concerns, the electrification of transportation plays an outstanding role in the sustainable development. In this context, Plug-in Electric Vehicle (PEV) and demand response have indispensable impacts on the future smart grid. Since integration of PEVs into the grid is a key element to achieve sustainable energy systems, this paper presents the optimal behavior of PEV parking lots in the energy and reserve markets. To this end, a model is developed to derive optimal strategies of parking lots, as responsive demands, in both price-based and incentive-based Demand Response Programs (DRPs). The proposed model reflects the impacts of different DRPs on the operational behavior of parking lots and optimizes the participation level of parking lots in each DRP. Uncertainties of PEVs and electricity market are also considered by using a stochastic programming approach. Numerical studies indicate that the PEV parking lots can benefit from the selective participation in DRPs.
... Numerous studies [25][26][27] focus on V2G, mainly EV charging management schemes at parking lots. The autonomous electric vehicle parking coordination system has been proposed to use electric vehicle batteries for supporting several V2G services. ...
... 25 The reduction of electricity cost and increasing profits for aggregators at parking lots have been considered. [26][27][28][29][30][31] The EV parking allocation and the EV charging impact on grid are assessed based on voltage violation, total system loss, and maximum system load. However, renewable energy sources (RESs) does not penetrate the grid; thus the effectual use of RES supported with EV management is not disputed. ...
In this manuscript, a hybrid technique is proposed for energy management system (EMS) between electric vehicle (EV) and distribution system. The proposed hybrid system is joined execution of Fertile Field algorithm (FFA) and Gradient Boost decision tree (GBDT) hence it is named as FFA‐GBDT technique. The main purpose of the proposed system is minimizing the cost of system, the power loss of the system with optimal energy management of the system. Besides, during the proposed FFA‐GBDT based energy management development, this article investigates the cooperative assessment of EMS operation with various consideration such as bidirectional energy trading capabilities of EV fleet arrival time in EVs' driving plan, the PV uncertainty impact on EMS operation depends, the setting dissimilar prioritization factors effect on selling energy back to grid as resources on entire cost of system. The implementation of the proposed model is done at MATLAB/Simulink and execution is assessed using existing methods. Consequently, the outcomes illustrate that the proposed technique is effective for finding the near global optimum solution with less computation and reduces the complexity of the proposed algorithm. Thus, the simulation outcome indicates that effectiveness of the proposed technique and performance of the proposed strategy is compared to existing techniques. The energy consumption of proposed and existing techniques is also analyzed. The energy consumption of the proposed technique is 720.34 KJ. In this manuscript, a hybrid FFA‐GBDT system is proposed for the energy management system (EMS) between electric vehicle (EV) and distribution system. The proposed hybrid system is joint implementation of fertile field algorithm (FFA) and Gradient Boost decision tree (GBDT) hence it is named as FFA‐GBDT system. The major objective of proposed system is to minimize the cost of the system, the power loss of system with optimal energy management of the system.
... Yazdani-Damavandi et al. (2016) aggregated the batteries of PEVs in the parking lots based on multi-energy system. The PEVs' parking lots have been modelled by Shafie-khah et al. (2016) to evaluate their optimal strategies based on price and demand response programs. Tabatabaee et al. (2017) presented optimal scheduling of MG including PEVs via bat algorithm to minimize the network total cost. ...
Recently, the penetration of the electric vehicles (EVs) in distribution networks gained a great attention. This paper proposes a new solution methodology incorporated recent metaheuristic approach of competition over resource (COR) for evaluating the optimal allocations and sizes of parking lots installed in radial distribution network. The main target is enhancing the network reliability with achieving minimum cost. COR is easily implemented with achieving feasible convergence for confident solution. A new formula of objective function comprising reliability enhancement cost, power loss improvement cost, and investment cost is proposed. The COR approach is applied on 9-bus, 33-bus, and 69-bus radial distribution networks. Additionally, the algorithms of grey wolf optimizer (GWO), whale optimization algorithm (WOA), and water cycle algorithm (WCA) are programmed and their results are compared with those obtained via the proposed COR. For 9-bus network, the proposed COR achieved saving of 6628.835 $ with percentage of 1.47% than GWO and time benefits of 84.11%. Moreover, it achieved economic benefits of 5315.4578 $ with percentage of 0.57% with time benefits of 7.6% compared to WCA for 33-bus network. Furthermore, COR provided 0.037% saving in total benefits compared to GWO for 69-bus system. The obtained results confirmed the superiority of COR in evaluating the optimal allocations and sizes of the parking lots in radial distribution networks.
This article presents a demand response strategy for managing electric vehicle (EV) charging rates in a community, addressing grid overloading during peak demand. The strategy dynamically adjusts charging and discharging rates based on real-time grid conditions, optimizing energy utilization and battery life. Additionally, an Extended Kalman Filter (EKF) is implemented for accurate State of Charge (SoC) estimation, considering measurement noise and time delays in SoC measurements. Simulation results demonstrate the effectiveness of the proposed strategies, ensuring efficient EV integration and maintaining grid stability. The findings provide valuable insights for policymakers, utilities, and researchers seeking sustainable EV integration within smart grids for a greener energy future.
An Electric Vehicle (EV) is considered as the solution for environmental limitation by integrating alternative resources. Besides, traditional vehicles are running against the Sustainable development goals that making a negative impact which can be overcome. This paper focus on the impacts on Renewable Energy Sources (RESs) and EV integration for meeting sustainable development goals such as Sustainable Developments Goal (SDG7) and Climate Action Goal (CAG13) by integrating EV and PV. The aforementioned sustainable developments Goals can be obtained by strategizing methos using Rule-Based Energy Management Strategy (RB-EMS) when integrating RESs into the proposed system. Thus, this study assists to measure the use of alternative sources as discussed in the result section in order to reduce the cost and emission and meet sustainable development goals. .
The high penetration of variable renewable energies requires the flexibility from both the generation and demand sides. This raises the necessity of modeling stochastic and flexible energy resources in power system operation. However, some distributed energy resources have both stochasticity and flexibility, e.g., prosumers with distributed photovoltaics and energy storage, and plug-in electric vehicles with stochastic charging behavior and demand response capability. Such partly controllable participants pose challenges to modeling the aggregate behavior of large numbers of entities in power system operation. This paper proposes a new perspective on the aggregate modeling of such energy resources in power system operation. Specifically, a unified controllability-uncontrollability-decomposed model for various energy resources is established by modeling the controllable and uncontrollable parts of energy resources separately. Such decomposition enables the straightforward aggregate modeling of massive energy resources with different controllabilities by integrating their controllable components with linking constraints and uncontrollable components with dependent discrete convolution. Furthermore, a two-stage stochastic unit commitment model based on the proposed model for power system operation is established. The proposed model is tested using a three-bus system and real Qinghai provincial power grid of China. The result shows that this model is able to characterize at high accuracy the aggregate behavior of massive energy resources with different levels of controllability so that their flexibility can be fully explored.
Demand response (DR) of power grids services an increasing key component of smart grids. This article is devoted to the DR active power control for smart buildings based on a cloud platform for security interconnection with power grids. The objective of the DR control is to cope with the intermittent renewable power injection and power fluctuation of the tie-line between the building and the distribution grid. The renewable energy power includes photovoltaic panel and on-site wind power generators on the building roof, and the flexible loads includes electric vehicles (EVs) connected to the building, and thermostatically controlled loads (TCLs) in the building. We propose two fair and flexible indexes, comfortable levels for TCLs and the charging priority for EVs, and design two kinds of DR control algorithm, i.e., centralized load reduction control algorithm and distributed load following control algorithm, to coordinate the power balance of the building while maintain the grid exchange power is distributed in a probabilistic security controllable interval such that the building turns out to be a grid-friendly building. The case study is tested on a smart building with random renewable power injection and flowing EVs, civil electric water heaters (EWHs) thermostatically controlled loads and the simulations results shows the effectiveness of the proposed DR control algorithms.
Car exhaust is one of the most common causes of ozone hole aggravation, electrical vehicles (EVs) represent a promising solution to avoid this problem. Despite the benefits of EVs, their random charging behavior causes some difficulties regarding the electric network performance, such as increased energy losses and voltage deviations. This paper aims to achieve the proper scheduling of the EVs charging process, avoid its negative impacts on the network, and satisfy the EVs users’ requirements. The EVs charging process is formulated as an optimization problem and solved using particle swarm optimization. The optimization problem formulation considers the EV arrival and departure times and the state of charge required by the user. Different strategies such as separated, accumulated, and ranked strategies with continuous or interrupted fixed charging have been applied to solve the uncoordinated EVs charging problem. These strategies are extensively tested on the modified IEEE 31 bus system (499-node network), using the combination of both Open DSS and MATLAB m-files. The simulation results confirm the effectiveness of the proposed accumulated ranked strategy with interrupted fixed charging in improving the overall power system performance. The achieved improvements include minimizing: the peak power consumed, the peak power losses, and the voltage drop. Moreover, the cost of the EVs charging in most of the feeders has been decreased to a satisfying value. A comparison between the proposed strategy and some previously reported strategies has been performed to ensure the technical and economic enhancement of the proposed strategy.
Internet of Things (IoT) is one of the emerging technologies, which is widely used across the globe. As the idea of a smart city was founded, IoT has been acknowledged as the key foundation in smart city paradigms. Technology makes a person smart, and to make the world smart, we have to make the country smart. To make the country smart, we have to make cities smart and to make smart cities, we have to be smart. In short, to create a smart environment, one must be equipped and familiar with the current trends. The integration of various smart devices and systems facilitates IoT for a smart city. The interdependent and interwoven nature of smart cities puts notable legislative, socioeconomic, and technical challenges for integrators, organizations, and designers committed to administrating these novel entities. The goal of this paper is to illustrate a contemporary survey of IoT-based smart cities with their potential, current trends and developments, amenity architecture, application area, real-world involvement, and open challenges. In addition, key elements with potential implementation constraints and integration of various IoT-based application areas that play a key role in building a smarter city have also been discussed. This extensive study contributes a useful panorama on various key points and gives a critical direction for forthcoming investigations. This study will also provide a reference point for practitioners and academics in the near future.
Transportation has become one of the major contributors to emissions. Global concerns on emissions reduction have led to the drastic adoption of renewable sources and Electric Vehicles (EV). Stochastic nature is the predominant aspect of renewables for which energy storage is indispensable. The coordination of renewables and EVs storage will bring new opportunities in handling the stochastic nature of heterogeneous entities of Micro Grids (MG). This article proposes a novel EV fleet control strategy for MG Energy Management System (EMS). Here, reducing grid dependency and operating energy cost of the MG are the key objectives. A smart FUZZY based EV prioritization is developed. A probabilistic approach is developed to estimate the chance of EV usage for the smart decision on choosing EVs while supporting EMS. Minimizing battery degradation and maximizing EV storage exploitation are the key objectives of EV prioritization. Optimal Energy Distribution (OED) is accomplished using Water Filling Algorithm (WFA). The proposed EMS is implemented in a real time on‐grid MG scenario hosting solar Photovoltaic (Solar PV), EV fleet, and Diesel Generators (DG). A comprehensive cost analysis has been presented and the efficacy of the proposed EMS is analyzed. Depiction of proposed MG scenario with EMS
The fast development of technologies in the smart grids provides new opportunities such as co-optimization of multi-energy systems. One of the new concepts that can utilize multiple energy sources is a hybrid fuel station (HFS). For instance, an HFS can benefit from energy hubs, renewable energies, and natural gas sources to supply electric vehicles along with natural gas vehicles. However, the optimal operation of an HFS deals with uncertainties from different sources that do not have similar natures. Some may lack in term of historical data, and some may have very random and unpredictable behavior. In this study, we present a stochastic mathematical framework to address both types of these uncertainties according to the innate nature of each uncertain variable, namely: epistemic uncertainty variables (EUVs) and aleatory uncertainty variables (AUVs). Also, the imprecise probability approach is introduced for EUVs utilizing the copula theory in the process, and a scenario-based approach combining Monte Carlo simulation with Latin Hypercube sampling is applied for AUVs. The proposed framework is employed to address the daily operation of a novel HFS, leading to a two-stage mixed-integer linear programming problem. The proposed approach and its applicability are verified using various numerical simulations.
Integration of distributed generations that fluctuate widely (such as Photovoltaic panels, Wind power, Electric Vehicles and Energy Storage Systems), poses a chance to the stability of power technology and distribution structures. However, the primary reason is that the electricity ratio between supply and demand may not be balanced. An extra or scarcity inside the production or intake of electricity can disrupt the system and cause critical problems which include a drop/rise in voltage and, under difficult conditions, power outages. The use of Energy Management Systems can effectively increase the balance between supply and demand and decrease peak load throughout unplanned durations. The energy management system is capable of not only sharing or exchanging energy between the different energy resources available, but also of economically supplying loads in a reliable, safe and effective manner under all conditions necessary for the operation of the power grid. This work outlines the structure, goals, benefits and defies of the energy control system via an in-intensity analysis of the distinctive stakeholders and participants engaged on this system. A detailed essential analysis of the functioning of distinct programs which includes Demand Response, Demand Management and Energy Quality Management implemented inside the electricity management gadget is presented in this review. It also summarizes quantifications of the various strategies of uncertainty. It includes as well a comparative and an important assessment of the primary optimization techniques which are used to obtain the extraordinary goals of energy management structures while at the same time meeting a wide range of requirements.
The replacement of hydrogen with conventional fossil fuel has been viewed as a feasible route to the ZERO emission transportation. Nowadays, the water electrolysis method has become mature and efficient enough to produce large-scale of hydrogen, and the condensation and reliquefaction technologies also greatly reduce the hydrogen loss via pipeline transmission, which both motivate the applications of hydrogen as a fuel. In this study, a combined power-hydrogen network is planned to supply the power demand of residential blocks and the charging/fueling demand for electrical vehicles and hydrogen vehicles, respectively. However, the joint scheduling of power-hydrogen network is challenging due to the non-linearity and non-convexity inherited from the hydraulic dynamics of hydrogen transmission. To resolve this problem, a convex-concave based sequential convex approximation method is proposed. A revised 37-bus system is used to demonstrate the validity of proposed method.
Home Energy Management Systems (HEMSs) may not be able to solve network issues, especially in the presence of high penetration level of Electric Vehicles (EVs) and decentral renewable energy. To solve the problem, Grid Energy Management Systems (GEMSs) were introduced. However, because of the contradictory nature of the main objectives of HEMS which are economical oriented on end-users, e.g., cost minimization, and GEMS which are technical oriented on system operators, e.g., maximization of system stability and power quality cannot be satisfied simultaneously. Hence, a multi-level energy management system seems to be necessary to improve the techno-economic performance of the distribution system while satisfying end-users, electricity retailers, and the system operator. Because of the significance of the subject, this paper presents the state-of-the-art regarding different energy management systems at home, aggregator, and network levels. The advantages and disadvantages of each system are discussed and compared, considering their main elements such as objective functions, constraints, optimization algorithms, communication protocols, and impact of EVs. The challenges and limitations in hierarchical energy management are explained. Finally, some future research directions are suggested to improve the multi-level energy management system.
Influx of plug‐in electric vehicles (PEVs) creates a pressing need for careful charging infrastructure planning. In this paper, the primary goal is to devise a closed‐form expression for the PEV charging station capacity problem. Two types of commercial charging stations are considered. The first problem is related to the calculation of the optimal service capacity for charging lots located at workplaces where PEV parking statistics are given as a priori. The second problem, on the other hand, is related to the optimisation of arrival rates for a given station capacity. In the second part, the mathematical models are expanded for the case where multiple charger technologies serve customer demand. This time the goal is to calculate the optimal customer load for each charger type according to its rate. Calculations are carried out for both social and individual optimality cases. Markovian queues are used to model the charging station system to capture the complex interactions between customer load, service waiting times, and electricity cost. The related optimisation problems are solved using convex optimisation methods. Closed‐form expressions of station capacity and optimal arrival rates are explicitly derived. Both analytical calculations and discrete‐event simulations are carried out and the results show that 60% of the waiting times and 42% of the queue length can be reduced by optimal capacity planning.
A smart city is an enhanced urban area that shines in the field of governance, economy, social capital, people and life through strong human capital. It is a novel advance to raise the effectiveness, decrease the expenses, control the difficulty of city life and enhance the standard of living of the people. These applications of smart cities hold the upcoming vision of cities, which intends at developing the information and communication technology (ICT) infrastructure, i.e., Internet of things (IoT) for worth added service delivery. Because of these raising improvements in sophisticated digital technologies, smart cities have been set with several electronic devices with the usage of IoT. In this paper, a detailed investigation has been conducted on the significance, usage levels as well as advancements of IoT-based smart cities. Also, few challenging issues along with the limitations of IoT-based smart cities are realized. Also, this paper aims to inspire and motivate researchers to innovate novel solutions for smart cities with the help of IoT technology.
Thanks to the demand response (DR), the system operators have a substantial opportunity to manipulate the demand side of the power system. Moreover, the system operators can easily incorporate trending technologies such as electric vehicles (EV) due to the idea of introducing improved level of controllable loads. In this study, a real-time optimization-based energy management model for an EV parking lot (EVPL) is propounded by using linear programming. The proposed algorithm offers a peak load limitation oriented DR program providing operational flexibility from the load-serving entity point of view together with the objective of maximizing the load factor of the EVPL in daily operation. Because of the mobility of the EVs, uncertain arrival/departure times along with the state-of-energy levels upon their arrival are generated by considering historical data to ensure a more realistic approach. In order to prove the validness of the suggested optimization model, a bunch of case studies is performed. Besides, credible results and useful findings are found out by activating the propounded model.
The rapid rise in the grid integration of low-carbon technologies, e.g., renewable energy sources (RESs) and plug-in electric vehicles (PEVs), have led to several challenges in distribution networks (DNs). This is due to the intermittent generation of RESs and uncertain loads of PEVs, both of which necessitate enhancing the flexibility requirements at the distribution level so as to accommodate the high penetration of these clean technologies in the future. To address such issues, this paper proposes a mixed-integer linear programming (MILP) expansion planning model for DNs considering the impact of high RES and PEV penetration, the associated uncertainties, and providing flexibility requirements at the distribution level. In this respect, the Spherical Simplex Unscented Transformation (SSUT), an analytical uncertainty modelling method, is implemented in the planning model to take into account the forecasting errors of the uncertain green technologies. Also, in order to estimate the electric vehicle parking lot demand at each load node of the network, a new approach for PEV charging model is suggested. To investigate the effectiveness and efficiency of the proposed probabilistic planning model, it is implemented on two test DNs, and the obtained results are thoroughly discussed.
We study the scheduling of large-scale electric vehicle (EV) charging in a power distribution network under random renewable generation and electricity prices. The problem is formulated as a stochastic dynamic program with unknown state transition probability. To mitigate the curse of dimensionality, we establish the nodal multi-target (NMT) characterization of the optimal scheduling policy: all EVs with the same deadline at the same bus should be charged to approach a single target of remaining energy demand. We prove that the NMT characterization is optimal under arbitrarily random system dynamics. To adaptively learn the dynamics of system uncertainty, we propose a model-free soft-actor-critic (SAC) based method to determine the target levels for the characterized NMT policy. The proposed SAC+NMT approach significantly outperforms existing deep reinforcement learning methods (in our numerical experiments on the IEEE 37-node test feeder), as the established NMT characterization sharply reduces the dimensionality of neural network outputs without loss of optimality.
Increasing grid integration of intermittent renewable energy sources (RESs) and plug-in electric vehicles (PEVs) with uncertain behaviours have necessitated enhancing the flexibility requirements of distribution networks. Thus, in the state-of-the-art distribution network expansion planning (DNEP) models, both flexibility requirements and high penetration of RESs and PEVs should be taken into consideration. In this respect, a novel collaborative planning model for power distribution network (PDN) and plug-in Electric Vehicle Parking Lots (EVPLs) is proposed in this paper, which leverages sizing, siting, and operation of EVPLs to enhance the distribution network flexibility. Also, to model the uncertain traffic flow of PEVs, a new model is proposed and is utilized to obtain a preliminary dispatch of PEV charging and, in turn, an estimated EVPL demand. Afterwards, this estimated demand is fed into the collaborative planning model to obtain the optimal expansion planning solution for PDN, and the size and location of EVPLs. Nonetheless, to provide the network operator with more flexibility sources, it is assumed that the operator can reschedule the charging pattern of some PEVs by compensating the EVPL owners for the difference in retail electricity prices of various hours. Finally, to illustrate the effectiveness of the proposed model, it is implemented on a test network, and the obtained results are discussed.
Electric vehicle (EV) has become one of the most critical components in the smart grid with the applications of the Internet of Things (IoT) technologies. Real-time charging control is pivotal to ensure the efficient operation of EVs. However, the charging control performance is limited by the uncertainty of the environment. On the other hand, it is challenging to determine a charging control strategy that is able to optimize multiple objectives simultaneously. In this paper, we formulate EV charging control model as a Markov decision process (MDP) by constructing state, action, transition function, and reward. Then, we propose a deep reinforcement learning-based approach: charging control deep deterministic policy gradient (CDDPG) to learn the optimal charging control strategy for satisfying user’s requirement of battery energy while minimizing user’s charging expense. We utilize the Long short-term memory (LSTM) network which extracts the information of previous energy price to determine the current charging control strategy. Moreover, Gaussian noise is added to the output of the actor network to prevent the agent from sticking into the non-optimal strategy. In addition, we address the limitation of sparse rewards by using two replay buffers, of which one is used to store the rewards during the charging phase, and another is used to store the rewards after the charging is completed. Simulation results prove that the CDDPG-based approach outperforms the deep Q learning-based approach (DQL) and the deep deterministic policy gradient-based approach (DDPG) in satisfying the user’s requirement for the battery energy and reducing the charging cost.
The integration of demand side management (DSM) with smart grid (SG) can facilitate residents' transfer into smart homes and sustainable cities by reducing the carbon emission. This manuscript reviews the recent works related to the application of DSM in SG through discussing the techniques and algorithms and their associated challenges for effective implementation. This paper also critically discusses the operation mode of DSM, the profile of energy production, storage and consumption, and finally the benefit obtained by the DSM implementation. Previous literature suggested that DSM practice reduced peak‐to‐average ratio, energy cost and carbon emission by approximately 10% to 65%, 5% to 50%, and 14%, respectively. The implementation of DSM in SG deals with a number of challenges such as security and privacy, tariff regulation, energy transmission, distribution, and effective utilization of energy resources. A number of international organizations have taken various measures and solutions to guarantee the security and privacy of the DSM in SG discussed. So far, a number of algorithms have been used as optimization approach to solve the DSM optimization problems; however hybrid algorithms have showed better performance than single algorithms due to their faster convergence speed. At the end, the paper presents the research gaps and future research directions.
In this study, a public parking-lot is assumed to schedule the charging of Electric Vehicles (EVs). Each EV owner upon arriving gives the energy demand as well as departure time to the system and immediately receives feedback; fulfilling or adjusting the request. The system designed in this study decides based on the previously admitted requests and the uncertain future demands in both Admission Control (AC) and Charge Scheduling (CS) mechanisms. We formulate a multi-stage stochastic programming model to minimize the expected total energy costs over the finite time horizon. Next, we approximate the model using a finite scenario tree. However, this model is computationally intractable, even for a moderate number of stages. Therefore, we customize a well-known decomposition procedure, Stochastic Dual Dynamic Programming (SDDP), to be matched the time-dependent charging conditions. Since the procedure takes several hours to obtain a high-quality solution, we run it once in the offline mode and employing the results for the online mode. The simulation results indicate that the proposed method outperforms the myopic approach, and obtains a close solution to the theoretical optimal value in terms of total costs and rejected demands.
We propose an optimal operation strategy for an electric vehicle (EV) aggregator (AGG), which performs energy arbitrage in the energy market and provides ancillary services from aggregated EVs, while providing charging services to EVs to maximize the profit in a future energy market. We group EV batteries as several virtual batteries (VB) with respect to their departure time and stages to implement multiple schedules simultaneously in the multi-stage stochastic optimization (MSSO). They are continuously grouped and regrouped as EVs arrive and depart. We predict VB scenario trees of stepwise EV driving routes and optimize the decisions in responding to uncertain EV movements. The VB states change as EVs enter and exit through the stages, so we should indirectly track time-varying VB characteristics at each stage. Then, we distribute the regulation bids for the VBs to the individual EVs. To reduce mismatches between the bidding amounts for VBs and actual transacted amounts for EVs, we suggest a novel binary progressive hedging algorithm to quickly determine the VB operations. Based on data from historical vehicle statistics, we verify that our strategy with the MSSO model provides a higher profit to the AGG than two-stage stochastic models.
The increasing penetration of electric vehicles (EVs) brings challenges and opportunities for power systems. One particular opportunity concerns the use of parked EVs to provide energy and associated services to the grid. In this work, the potential energy storage capacity of parking lots (PLs) of EVs is computed using the proposed stochastic model which considers the sporadic nature of the EV’ behaviours (i.e. arrival/departure, battery degradation, travel pattern, charge/discharge rates). The analysis was performed for two types of PLs with very different occupancy distributions, i.e. a shopping centre PL, and a workplace PL. In both cases, the available energy storage capacity of EVs was estimated hourly using real household travel data, i-MiEV data and car park occupancy records. The results show that the aggregated energy storage capacity closely follows the occupancy of EVs in the PLs, and is substantial, with little sensitivity to charging rate. The proposed stochastic modelling, considered the variations in energy consumption, battery degradation, and user behaviour, predicted 13.4% less peak capacity than deterministic modelling. Moreover, we conclude that the shopping centre parking lot is a viable energy resource to the grid, with their scale and throughput compensating for the relatively low occupancy.
This paper proposes a new planning framework for optimal allocation of parking-lot (PL)-based charging infrastructures to facilitate the efficient integration of plug-in electric vehicles (PEVs). Unlike existing works, the present study explicitly considers the uncertain implications of incentive policy on PEV owners charging behaviors and its effects in PL planning. For this aim, a regret-matching technique is introduced to model the bounded rationality of PEV owners in deciding the choice to use different charging options for recharging their vehicle, as a dependency with respect to the incentive value and the accessibility of the charging service in long-term horizon. Such endogenous uncertainties are considered simultaneously with the inherent exogenous randomness of PEV demand and captured by the proposed PL planning model using a proper scenario generation method. The resulting model turns out to be a two-stage stochastic programming with decision-dependent uncertainties and it is solved by using the genetic algorithm. Numerical studies based on a modified IEEE 12-bus distribution network verify the effectiveness of the proposed model and the approach.
Progressive popularization of plug-in electric vehicles (PEVs) calls for synergistic deployment of public charging infrastructures to serve the recharging needs in future urban systems. For this end, a new planning framework to facilitate efficient accommodation of PEVs via gridable parking lots (PLs) is proposed in this study. Different from most previous works, the presented method explicitly considers the potential uncertainties in the users' behaviors in the PL planning, which will allow decision-makers to better assess the effect of human factors on the efficiency of their investment. Moreover, the concerned PL allocation problem has been investigated in conjunction with the optimal contract offer for PEV users to obtain the final planning scheme. The overall problem is formulated as a two-stage stochastic programming model, where the first-stage mainly deals with the planning decisions associated with PLs, including siting and sizing of PLs and contractual arrangements; while the second stage evaluates the operational performance of the suggested PL system under different scenario realizations of PEV behaviors. A combinatorial optimization algorithm is used to solve the problem. The proposed framework is demonstrated on an illustrative testing system and numerical results verify the effectiveness of our method.
The large-scale penetration of electric vehicles (EVs) into the power system will provoke new challenges needed to be handled by distribution system operators (DSOs). Demand response (DR) strategies play a key role in facilitating the integration of each new asset into the power system. With the aid of the smart grid paradigm, a day-ahead charging operation of large-scale penetration of EVs in different regions that include different aggregators and various EV parking lots (EVPLs) is propounded in this study. Moreover, the uncertainty of the related EV owners, such as the initial state-of-energy and the arrival time to the related EVPL, is taken into account. The stochasticity of PV generation is also investigated by using a scenario-based approach related to daily solar irradiation data. Last but not least, the operational flexibility is also taken into consideration by implementing peak load limitation (PLL) based DR strategies from the DSO point of view. To reveal the effectiveness of the devised scheduling model, it is performed under various case studies that have different levels of PLL, and for the cases with and without PV generation.
In this paper, we show that a group of Demand Response (DR) providing customers will benefit by transferring into a cooperative. While the benefit of aggregation is definite when unconstrained, this paper is devoted to studying benefits of a DR cooperative with a nonlinear piecewise objective function of minimizing net cost comprising energy charges and demand charges while being constrained by power system constraints and other physical constraints. We propose a two-level stochastic optimization formulation that provides a model for DR cooperatives to aggregate and schedule participating resources with the objective of minimizing the total electricity bill comprising energy and demand charges. It also provides a settlement tool for the cooperative to share costs and benefits among its members. In the first level, a stochastic mixed integer linear optimization formulation to minimize the total resource cost for the electricity market is solved considering various probabilistic scenarios to find locational marginal prices (LMPs) for 24-hours and the corresponding amount of DR scheduled for DR participants including the cooperative. In the second level, a linear optimization formulation is solved to schedule DR of cooperative members with the objective of minimizing the total electricity bill of the cooperative comprising energy and demand charges. Finally, we present a settlement tool to share the costs and benefits amongst the cooperative members, whereby all members benefit from reduced electricity bills. The proposed model was tested on modified IEEE 6-bus and 118-bus systems. The results conclusively demonstrate the benefit of the proposed cooperative model for DR where scheduling DR in concert by members has a pronounced effect in reducing demand charges for all more than that achieved by individual members minimizing their own electricity bills.
Electric vehicles (EVs) are expected to become widespread in future years. Thus, it is foreseen that EVs will become the new high-electricity-consuming appliances in the households. The characteristics of the extra power load that they impose on the distribution grid follow the patterns of people's random usage behaviors. In this paper, we seek to provide answers to the following question: assigning real-world randomness to the EVs' availability in the households and their charging requirements, how can EVs' demand response (DR) help to minimize the peak power demand and, in general, shape the aggregated demand profile of the system? We present a general demand-shaping problem applicable for limit order bids to a day-ahead (DA) energy market. We propose an algorithm for distributed DR of the EVs to shape the daily demand profile or to minimize the peak demand. Additionally, we put these problems in a game framework. Extensive simulations show that, for certain practical distributions of EVs' usage, it is possible to accommodate EVs for all the users in the system and yet achieve the same peak demand as when there is no EV in the system without any changes in the users' commuting behaviors.
In this paper, we propose a centralized electric vehicles (EVs) recharge scheduling system for parking lots using a realistic vehicular mobility/parking pattern focusing on individual parking lots. We consider two different types of EV based on their mobility/parking patterns: 1) regular EVs; and 2) irregular EVs. An extensive trace-based vehicular mobility model collected from the Canton of Zurich is used for the regular EVs, and a probabilistic pattern built on top of this trace is used for modeling the behavior of irregular EVs. To the extent of our knowledge, this is the first EV charging scheduling study in the literature that takes into account a realistic vehicular mobility pattern focusing on individual parking lots. We compare the performance of our proposed system with two well-known basic scheduling mechanisms, first come first serve and earliest deadline first, with regard to two objective functions: 1) maximizing the total parking lot revenue; and 2) maximizing the total number of EVs fulfilling their requirements. Comparison results show that our proposed system outperforms well-known basic scheduling mechanisms with regards to both objectives. Parking lots managing the recharging of a high number of EVs will greatly benefit from using such recharge scheduling systems in the context of smart cities.
Simulations predict that the introduction of PHEVs could impact demand peaks, reduce reserve margins, and increase prices. The type of power generation used to recharge the PHEVs and associated emissions will depend upon the region and the timing of the recharge.
This paper presents a hierarchical optimal control framework to coordinate the charging of plug-in electric vehicles in multifamily dwellings. A particular scenario is considered where distributed urban residential communities access electric power supplies through a common primary distribution transformer. We first formulate a centralized finite-horizon control problem. The proposed multistage mixed-integer program seeks to maximize the total utility of the charging service provider while satisfying customers' charge demands and transformer capacity constraints. By exploiting the structure of the centralized model, we decompose the centralized problem with respect to each parking deck based on the Lagrangian relaxation method and design an effective heuristic method to find feasible solutions to speed up convergence. Case studies on operations of five parking decks following different charging strategies are carried out. Simulation results demonstrate that the proposed distributed hierarchical charging strategy outperforms the centralized charging strategy from the perspective of computational requirements. System reliability and customer privacy protection are also discussed.
This paper presents a hierarchical demand response (DR) bidding framework in the day-ahead energy markets which integrates customer DR preferences and characteristics in the ISO’s market clearing process. In the proposed framework, load aggregators submit aggregated DR offers to the ISO which would centrally optimize final decisions on aggregators’ DR contributions in wholesale markets. The hourly load reduction strategies include load shifting and curtailment and the use of onsite generation and energy storage systems. The ISO applies mixed-integer linear programming (MILP) to the solution of the proposed DR model in the day-ahead market clearing problem. The proposed model is implemented using a 6-bus system and the IEEE-RTS, and several studies are conducted to demonstrate the merits of the proposed DR model.
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.
An electric vehicle (EV) aggregation agent, as a commercial middleman between electricity market and EV owners, participates with bids for purchasing electrical energy and selling secondary reserve. This paper presents an optimization approach to support the aggregation agent participating in the day-ahead and secondary reserve sessions, and identifies the input variables that need to be forecasted or estimated. Results are presented for two years (2009 and 2010) of the Iberian market, and considering perfect and naïve forecast for all variables of the problem.
Plug-in electric vehicle (PEV) charging could cause significant strain on residential distribution systems, unless technologies and incentives are created to mitigate charging during times of peak residential consumption. This paper describes and evaluates a decentralized and “packetized” approach to PEV charge management, in which PEV charging is requested and approved for time-limited periods. This method, which is adapted from approaches for bandwidth sharing in communication networks, simultaneously ensures that constraints in the distribution network are satisfied, that communication bandwidth requirements are relatively small, and that each vehicle has fair access to the available power capacity. This paper compares the performance of the packetized approach to an optimization method and a first-come, first-served (FCFS) charging scheme in a test case with a constrained 500 kVA distribution feeder and time-of-use residential electricity pricing. The results show substantial advantages for the packetized approach. The algorithm provides all vehicles with equal access to constrained resources and attains near optimal travel cost performance, with low complexity and communication requirements. The proposed method does not require that vehicles report or record driving patterns, and thus provides benefits over optimization approaches by preserving privacy and reducing computation and bandwidth requirements.
Advancements in smart grid technologies have made it possible to apply various options and strategies for the optimization of demand response (DR) in electricity markets. DR aggregation would accumulate potential DR schedules and constraints offered by small- and medium-sized customers for the participation in wholesale electricity markets. Despite various advantages offered by the hourly DR in electricity markets, practical market tools that can optimize the economic options available to DR aggregators and market participants are not readily attainable. In this context, this paper presents an optimization framework for the DR aggregation in wholesale electricity markets. The proposed study focuses on the modeling strategies for energy markets. In the proposed model, DR aggregators offer customers various contracts for load curtailment, load shifting, utilization of onsite generation, and energy storage systems as possible strategies for hourly load reductions. The aggregation of DR contracts is considered in the proposed price-based self-scheduling optimization model to determine optimal DR schedules for participants in day-ahead energy markets. The proposed model is examined on a sample DR aggregator and the numerical results are discussed in the paper.
Plug-in electric vehicles in the future will possibly emerge widely in city areas. Fleets of such vehicles in large numbers could be regarded as considerable stochastic loads in view of the electrical grid. Moreover, they are not stabled in unique positions to define their impact on the grid. Municipal parking lots could be considered as important aggregators letting these vehicles interact with the utility grid in certain positions. A bidirectional power interface in a parking lot could link electric vehicles with the utility grid or any storage and dispersed generation. Such vehicles, depending on their need, could transact power with parking lots. Considering parking lots equipped with power interfaces, in more general terms, parking-to-vehicle and vehicle-to-parking are propose here instead of conventional grid-to-vehicle and vehicle-to-grid concepts. Based on statistical data and adopting general regulations on vehicles (dis)charging, a novel stochastic methodology is presented to estimate total daily impact of vehicles aggregated in parking lots on the grid. Different scenarios of plug-in vehicles' penetration are suggested in this paper and finally, the scenarios are simulated on standard grids that include several parking lots. The results show acceptable penetration level margins in terms of bus voltages and grid power loss.
It is widely agreed that an increased participation of the demand side in the electricity markets would produce benefits not only for the individual consumers but also for the market as a whole. This paper proposes a method for quantifying rigorously the effect that such an increase would have on the various categories of market participants. A new centralized complex-bid market-clearing mechanism has been devised to take into consideration the load shifting behavior of consumers who do submit price-sensitive bids. The effects of the proportion of demand response on the market are illustrated using a test system with ten generating units scheduled over 24 periods.
Recently, a massive focus has been made on demand response (DR) programs, aimed to electricity price reduction, transmission lines congestion resolving, security enhancement and improvement of market liquidity. Basically, demand response programs are divided into two main categories namely, incentive-based programs and time-based programs. The focus of this paper is on Interruptible/Curtailable service (I/C) and capacity market programs (CAP), which are incentive-based demand response programs including penalties for customers in case of no responding to load reduction. First, by using the concept of price elasticity of demand and customer benefit function, economic model of above mentioned programs is developed. The proposed model helps the independent system operator (ISO) to identify and employ relevant DR program which both improves the characteristics of the load curve and also be welcome by customers. To evaluate the performance of the model, simulation study has been conducted using the load curve of the peak day of the Iranian power system grid in 2007. In the numerical study section, the impact of these programs on load shape and load level, and benefit of customers as well as reduction of energy consumption are shown. In addition, by using strategy success indices the results of simulation studies for different scenarios are analyzed and investigated for determination of the scenarios priority.
This study establishes a new approach to analyzing the economic impacts of vehicle-to-grid (V2G) regulation reserves by simulating the restrictions arising from unpredictable mobility requests by vehicle users. A case study for Germany using average daily values (in the following also called the “static” approach) and a dynamic simulation including different mobility use patterns are presented. Comparing the dynamic approach with the static approach reveals a significant difference in the power a vehicle can offer for ancillary services and provides insights into the necessary size of vehicle pools and possible adaptations required in the regulation market to render V2G feasible. In the static approach it is shown that negative secondary control is economically the most beneficial for electric vehicles because it offers the highest potential for charging with “low-priced” energy from negative regulation reserves. A Monte Carlo simulation using stochastic mobility behavior results in a 40% reduction of the power available for regulation compared to the static approach. Because of the high value of power in the regulation market, this finding has a strong impact on the resulting revenues. Further, we demonstrate that, for the data used, a pool size of 10 000 vehicles seems reasonable to balance the variation in each individual's driving behavior. In the case of the German regulation market, which uses monthly bids, a daily or hourly bid period is recommended. This adaptation would be necessary to provide individual regulation assuming that the vehicles are primarily used for mobility reasons and cannot deliver the same amount of power every hour of the week.
This paper proposes a robust optimization (RO) model for bidirectional dispatch coordination of large-scale plug-in electric vehicles (PEVs) in a power grid in which the PEVs are aggregated to manage. The PEV aggregators are considered as a type of dispatchable demand response and energy storage resource with stochastic behaviors, and can supply load or provide ancillary services such as regulation reserve to the grid. The proposed RO model is then reformulated as a mixed-integer quadratic programming model, which can be solved efficiently. Computer simulations are performed for a power grid with ten generators and three PEV aggregators to validate the economic benefit of the RO model for bidirectional dispatch coordination of the PEVs and the robustness of the RO model to the uncertainty of the PEVs’ stochastic mobility behaviors.
A recent solution to tackle environmental issues is the electrification of transportation. Effective integration of plug-in electric vehicles (PEVs) into the grid is important in the process of achieving sustainable development. One of the key solutions regarding the need for charging stations is the installation of PEV parking lots (PLs). However, contrary to common parkings, PLs are constrained by various organizations such as municipalities, urban traffic regulators, and electrical distribution systems. Therefore, this paper aims to allocate PLs in distribution systems with the objective of minimizing system costs including power loss, network reliability, and voltage deviation as possible objectives. A two-stage model has been designed for this purpose. PLs' behavior considering market interactions is optimized at the first stage to provide profit to the PL owner. At the second stage, the PL allocation problem is solved considering various network constraints. Conclusions are duly drawn with a realistic example.
This paper proposes a novel online coordination method for the charging of plug-in electric vehicles (PEVs) in smart distribution networks. The goal of the proposed method is to optimally charge the PEVs in order to maximize the PEV owners' satisfaction and to minimize system operating costs without violating power system constraints. Unlike the solutions reported in the literature, the proposed charging architecture guarantees the feasibility of the charging decisions by means of a novel prediction unit that can forecast future PEVs power demand and through an innovative two-stage optimization unit that ensures effective charging coordination. Coordinated PEV discharging also enables improved utilization of power system resources. Simulation results for a typical distribution network are provided as a demonstration of the effectiveness of the proposed architecture.
A stochastic modeling and simulation technique for analyzing impacts of electric vehicles charging demands on distribution network is proposed in this paper. Different from the previous deterministic approaches, the feeder daily load models, electric vehicle start charging time, and battery state of charge used in the impact study are derived from actual measurements and survey data. Distribution operation security risk information, such as over-current and under-voltage, is obtained from three-phase distribution load flow studies that use stochastic parameters drawn from Roulette wheel selection. Voltage and congestion impact indicators are defined and a comparison of the deterministic and stochastic analytical approaches in providing information required in distribution network reinforcement planning is presented. Numerical results illustrate the capability of the proposed stochastic models in reflecting system losses and security impacts due to electric vehicle integrations. The effectiveness of a controlled charging algorithm aimed at relieving the system operation problem is also presented.
This paper proposes a novel load management solution for coordinating the charging of multiple plug-in electric vehicles (PEVs) in a smart grid system. Utilities are becoming concerned about the potential stresses, performance degradations and overloads that may occur in distribution systems with multiple domestic PEV charging activities. Uncontrolled and random PEV charging can cause increased power losses, overloads and voltage fluctuations, which are all detrimental to the reliability and security of newly developing smart grids. Therefore, a real-time smart load management (RT-SLM) control strategy is proposed and developed for the coordination of PEV charging based on real-time (e.g., every 5 min) minimization of total cost of generating the energy plus the associated grid energy losses. The approach reduces generation cost by incorporating time-varying market energy prices and PEV owner preferred charging time zones based on priority selection. The RT-SLM algorithm appropriately considers random plug-in of PEVs and utilizes the maximum sensitivities selection (MSS) optimization. This approach enables PEVs to begin charging as soon as possible considering priority-charging time zones while complying with network operation criteria (such as losses, generation limits, and voltage profile). Simulation results are presented to demonstrate the performance of SLM for the modified IEEE 23 kV distribution system connected to several low voltage residential networks populated with PEVs.
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.
This paper studies risk-aware day-ahead scheduling and real-time dispatch for electric vehicle (EV) charging, aiming to jointly optimize the EV charging cost and the risk of the load mismatch between the forecast and the actual EV loads, due to the random driving activities of EVs. It turns out that the consideration of the load mismatch risk in the objective function significantly complicates the risk-aware day-ahead scheduling problem (indeed it involves nonconvex optimization). A key step taken here is to utilize a hidden convexity structure to recast this problem as a two-stage stochastic linear program, and then solve it by using the L-shaped method. Since the computational complexity grows exponentially in the number of EVs, an estimation algorithm is developed based on importance sampling to mitigate the computational complexity. Further, a distributed risk-aware real-time dispatch algorithm is developed, in which the aggregator needs to compute only the shadow prices for each EV to optimize its own charging strategy in a distributed manner. It is shown, based on real data, that the proposed risk-aware day-ahead scheduling algorithm using importance sampling can significantly reduce the overall charging cost with a small number of samples.
The anticipation of a large penetration of EVs (electric vehicles) into the market brings up many technical issues. The power system may put at risk the security and reliability of operation due to uncontrolled EV charging and discharging. It is necessary to carry out intelligent scheduling for charging and discharging of EVs. In this paper, a smart management and scheduling model is proposed for large number of EVs parked in an urban parking lot. The proposed model considered practical constraints such as desired charging electricity price, remaining battery capacity, remaining charging time and age of the battery. The results show that the proposed parking lot energy management system satisfies both financial and technical goals. Moreover, EV owners could earn profit from discharging their vehicles as well as having desired SOC (state of charge) in the departure time.
Alternative vehicles, such as plug-in hybrid electric vehicles, are becoming more popular. The batteries of these plug-in hybrid electric vehicles are to be charged at home from a standard outlet or on a corporate car park. These extra electrical loads have an impact on the distribution grid which is analyzed in terms of power losses and voltage deviations. Without coordination of the charging, the vehicles are charged instantaneously when they are plugged in or after a fixed start delay. This uncoordinated power consumption on a local scale can lead to grid problems. Therefore, coordinated charging is proposed to minimize the power losses and to maximize the main grid load factor. The optimal charging profile of the plug-in hybrid electric vehicles is computed by minimizing the power losses. As the exact forecasting of household loads is not possible, stochastic programming is introduced. Two main techniques are analyzed: quadratic and dynamic programming.
Plug-in hybrid electric vehicles (PHEVs) represent one option for the electrification of private mobility. In order to efficiently integrate PHEVs into power systems, existing organizational structures need to be considered. Based on procedures of power systems planning and operation, actors are identified whose operational activities will be affected by PHEV integration. Potential changes and challenges in the actors’ long- and short term planning activities are discussed.Further, a PHEV operation state description is developed which defines vehicle operation states from the power system point of view integrating uncontrolled, controlled recharging and vehicle to grid (V2G) utilization in one single framework. Future PHEV managing entities, such as aggregators, can use this framework for planning and operation activities including load management and V2G. This operational state description could provide a solution for future short term planning challenges of PHEVs and an aegis for various routes of current research, which to date have been weakly linked to each other.
As the light vehicle fleet moves to electric drive (hybrid, battery, and fuel cell vehicles), an opportunity opens for “vehicle-to-grid” (V2G) power. This article defines the three vehicle types that can produce V2G power, and the power markets they can sell into. V2G only makes sense if the vehicle and power market are matched. For example, V2G appears to be unsuitable for baseload power—the constant round-the-clock electricity supply—because baseload power can be provided more cheaply by large generators, as it is today. Rather, V2G's greatest near-term promise is for quick-response, high-value electric services. These quick-response electric services are purchased to balance constant fluctuations in load and to adapt to unexpected equipment failures; they account for 5–10% of electric cost—$ 12 billion per year in the US. This article develops equations to calculate the capacity for grid power from three types of electric drive vehicles. These equations are applied to evaluate revenue and costs for these vehicles to supply electricity to three electric markets (peak power, spinning reserves, and regulation). The results suggest that the engineering rationale and economic motivation for V2G power are compelling. The societal advantages of developing V2G include an additional revenue stream for cleaner vehicles, increased stability and reliability of the electric grid, lower electric system costs, and eventually, inexpensive storage and backup for renewable electricity.
This paper presents a summary of Demand Response (DR) in deregulated electricity markets. The definition and the classification of DR as well as potential benefits and associated cost components are presented. In addition, the most common indices used for DR measurement and evaluation are highlighted, and some utilities’ experiences with different demand response programs are discussed. Finally, the effect of demand response in electricity prices is highlighted using a simulated case study.
Because of the revolution of power system structure nowadays, operation and control of generating units must be modified. Energy price becomes an important parameter to make a decision in this restructured system. Unit commitment (UC) in such a competitive environment is no longer the same as the traditional one. The objective of UC is not to minimize production cost as before but to find the solution that produces a maximum profit for generation company (GENCO). This paper presents a new profit-based UC formulation under competitive environment considering both power and reserve generation. A hybrid method between Lagrange relaxation (LR) and evolutionary programming (EP) is applied to solve this new UC problem. The proposed approach is applied to a test system. Simulation results are compared with those obtained from traditional UC.
The estimated probability that spinning reserve is called and generated is considered crucial in the formulation of generation scheduling to simulate the spinning reserve uncertainty. Artificial Neural Network (ANN) is applied for forecasting the spinning reserve probability considering line limits, line and generator outages, market prices, bidding strategy, and load and spinning reserve patterns.
The work was conducted with the aim of finding a general method
for solving the unit commitment (UC) problem. The proposed algorithm
employs the evolutionary programming (EP) technique in which populations
of contending solutions are evolved through random changes, competition,
and selection. In the subject algorithm an overall UC schedule is coded
as a string of symbols and viewed as a candidate for reproduction.
Initial populations of such candidates are randomly produced to form the
basis of subsequent generations. The practical implementation of this
procedure yielded satisfactory results when the EP-based algorithm was
tested on a reported UC problem previously addressed by some existing
techniques such as Lagrange relaxation (LR), dynamic programming (DP),
and genetic algorithms (GAs). Numerical results for systems of up to 100
units are given and commented on
Vehicle-to-grid (V2G) power flow regulations and building codes review by the AVTA
- A Briones
A. Briones et al., "Vehicle-to-grid (V2G) power flow regulations and
building codes review by the AVTA," Idaho Nat. Lab., U.S. Dept. Energy,
Idaho Falls, ID, USA, Tech. Rep. INL/EXT-12-26853, 2012. [Online].