Search

The energy produced from various sources in modern power systems needs to be optimally planned for scheduling and managing the power system under specific conditions. In recent times, the world’s growing population, increasing energy needs, technological advancements, environmental concerns, and global climate change have led to a rise in the demand for electric energy. One significant solution to meeting this energy demand is the use of renewable energy sources (RESs) in power systems. However, integrating RESs has made the construction of power systems more complex. Moreover, Plug-in electric vehicles (PEVs) stand out as a highly promising technology for reducing carbon emissions in the transportation sector, aligning with the global Net-zero target. The optimal power flow (OPF) is a challenging problem when dealing with integrating renewable energy sources (RESs) and plug-in electric vehicles (PEVs) into modern power systems. In this paper, we address the OPF problem that arises from power systems with a high penetration of controllable renewable sources and PEVs. We simulate uncertain solar irradiance, wind speed, and PEV behavior using Weibull, lognormal, and normal probability distribution functions (PDFs) respectively. The proposed stochastic OPF problem, which incorporates controllable renewable sources and Vehicle-to-Grid (V2G) technology, is addressed using the hybrid metaheuristic optimization algorithm (AHMRFO). The AHMRFO hybrid algorithm combines the exploratory power of the manta ray foraging optimization (MRFO) with the exploiting strength of artificial hummingbird algorithm (AHA) resulting in a more balanced, robust, and efficient optimization tool. It can effectively handle complex problems, avoid premature convergence, and provide high-quality solutions across a wide range of optimization challenges. Initial evaluations for the proposed algorithm are executed on 7 Congress on Evolutionary Computation (CEC) test functions and its obtained results was compared with recent optimization algorithms such as original MRFO, AHA, supply demand-based optimization (SDO), hunter prey optimization (HPO), weighted mean of vectors (INFO), and northern goshawk optimization (NGO). Moreover, simulations carried out on different test systems demonstrate the effectiveness and efficiency of the proposed AHMRFO algorithm when compared to other algorithms such as the MRFO and AHA. The AHMRFO technique proves to be highly effective, yielding the lowest fitness values of 781.5836 ${\$}$ /h and 808.3611 ${\$}$ /h in the first two scenarios for the modified IEEE 30-bus system with RES and it has the minimum fitness values of 790.2783 ${\$}$ /h and 819.6573 ${\$}$ /h in their respective scenarios for the modified IEEE 30-bus system with stochastic Vehicle-to-grid and RES. These results highlight the precision and strength of AHMRFO technique in effectively addressing various instances of the OPF problem. Additionally, the Wilcoxon signed-rank test has been employed to demonstrate the superiority, effectiveness, and robustness of the proposed AHMRFO algorithm.

Over the past ten years, global electricity networks have undergone rapid advancements, primarily driven by the widespread adoption of renewable energy resources (RES). While these sources have accompanied in a gathering of advantages, such as cost-effective operation of wind and solar photovoltaic (PV) installations and the mitigation of environmental hazards linked with traditional power sources, they have also introduced a host of challenges to planning and operation of power systems. Also, Plug-in electric vehicles (PEVs) stand out as a highly promising technology for reducing carbon emissions in the transportation sector, aligning with the global Net-zero target. The standard optimal power flow (OPF) problem, which is naturally nonlinear, has become even more complex with the addition of renewable energy sources and plug-in electric vehicles along with traditional thermal power generators. This increased complexity comes from the unpredictable and intermittent nature of these new resources. Monte Carlo techniques are used to estimate the production costs of renewable energy sources and plug-in electric vehicles (PEVs) and analyze their viability. The uncertainty of the renewable sources and PEVs is modeled using Weibull, lognormal, and normal probability distribution functions (PDFs). The comprehensive OPF, incorporating renewable energy components and PEVs, is cast as a single objective problem encompassing various objectives, such as decreasing fuel costs, total emissions, voltage deviations, and real transmission losses. This research shares a common objective by introducing a novel hybrid metaheuristic optimization algorithm (MRGTO) to address the OPF challenge. Additionally, the study explores the impact of renewable energy resources and Vehicle-to-Grid (V2G) on the stochastic OPF problem. The MRGTO employs artificial gorilla troops optimizer (GTO) with manta ray foraging optimization (MRFO) algorithm to achieve the optimal results for the OPF problem with stochastic RES and V2G. The developed technique is expected to increase the solution accuracy through increasing the solution diversity through an optimization procedure. Initial assessments are executed on benchmark functions. After that, a combined model of wind and PV-integrated IEEE 30-bus system are executed by the proposed MRGTO algorithm and other well-known optimization algorithms. Additionally, the effectiveness of the proposed method is assessed using the IEEE 30-bus test system under different scenarios. The evaluations have shown the proposed MRGTO technique to be superior at attainment a best solution for the OPF problem considering stochastic wind and PV power and V2G. Moreover, the obtained solutions confirmed the MRGTO technique would be more effective in optimization with quicker convergence rate, and higher convergence precision. After testing, the effectiveness of the MRGTO algorithm has been found to be much robust than the conventional artificial gorilla troops optimizer, manta ray foraging optimization, and other well-known published heuristics, metaheuristics, and hybrid optimization techniques.INDEX TERMS Optimal power flow, plug-in electric vehicle, renewable energy sources, V2G, artificial gorilla troops optimizer, manta ray foraging optimization.

Dual Active Bridge (DAB) converter is well suited for off-board fast Electric Vehicle Charging Stations (EVCS) and in DC microgrid-connected EVCS applications. However, proper modification in DAB converter topology is crucial for fast charging, as the charging current is very high, and any oscillations would shorten the EV battery life. As per literature, DAB converter topology integrated with a series inductor at the output for EV battery charging reduces the output current ripple by 40%. However, as per our observation, potentially unstable dynamics are introduced in the system with the inclusion of an LC filter. This affects the controller stability; hence, these dynamics must be damped using active or passive damping. Passive damping is less efficient due to the considerable losses it produces, partly induced by low-frequency noise and partly from switching-frequency noise. Also, placing the damping at the selective resonant frequency is very complex. In comparison, active damping offers selective placement and loss reduction. This paper presents the DAB converter mathematical modeling using the generalized average model (GAM) and the average output current linearization model (AOCLM). The paper presents stability analysis and proposes a novel and robust approach to control the dynamics in the current and voltage control loops during EV charging, thereby reducing battery degradation. The charge flow to the battery is controlled using Constant Current Constant Voltage (CCCV) algorithm. The proposed scheme also eliminates the need for extra sensors by selectively attenuating the oscillations at the frequency of interest. The performance of the proposed model for EV fast charging application is verified using MATLAB/Simulink and experimentally validated in a real-time simulator OPAL-RT hardware platform.

The rapid development of renewable energy power has improved global energy and environmental problems. However, with the high volatility of renewable energy, it is an important challenge to guarantee the consumption of renewable energy and the reliable operation of high percentage renewable energy power systems. To solve this problem, this paper proposes a tracking absorption strategy for renewable energy based on the interaction between the supply side and the demand side, which adjusts the charging process of electric vehicles (EVs) through electric vehicle aggregator (EVA) to realize the tracking absorption of renewable energy abandoned electricity. In view of this process, we analyze the interaction among power grid, EVA and renewable energy generation (REG) as well as their market characteristics. The master-slave game model of EVA and REG was constructed considering the charging behavior characteristics of EVs and the output characteristics of REGs. Then the model solving strategy based on soft actor-critic (SAC) algorithm is proposed, and the REG pricing strategy and EVA scheduling strategy are calculated to optimize the mutual benefits. The case analysis shows that, under the same scale of electric vehicles, the proposed method can promote about 93.89% of the power abandonment consumption of wind power system, 96.00% of the photovoltaic system, and 97.41% of the wind-solar system. This strategy reduces the electricity purchase cost of EVA, promotes the interaction among renewable energy, vehicles and power grid, and improves the utilization efficiency of renewable energy.

The tendency towards carbon dioxide reduction greatly stimulates the popularity of electric vehicles against conventional vehicles. However, electric vehicle chargers represent a huge electric burden, which affects the performance and stability of the grid. Various optimization methodologies have been proposed in literature to enhance the performance of the distribution grids. However, existing techniques handle the raised issues from individual perspectives and/or with limited scopes. Therefore, this paper aims to develop a distributed controller-based coordination scheme in both medium and low voltage networks to handle the electric vehicles’ charging impact on the power grid. The scope of this work covers improving the network voltage profile, reducing the total active and reactive power, reducing the load fluctuations and total charging cost, while taking into consideration the random arrivals/departures of electric vehicles and the vehicle owners’ preferred charging time zones with vehicle-to-grid technology. Simulations are carried out to prove the success of the proposed method in improving the performance of IEEE 31-bus 23 kV system with several 415 V residential feeders. Additionally, the proposed method is validated using Controller Hardware-in-the-Loop. The results show that the proposed method can significantly reduce the issues that appear in the electric power grid during charging with minor changes in the existing grid. The results prove the successful implementation of different types of charging, namely, ultra-fast, fast, moderate, normal and vehicle-to-grid charging with minimum charging cost to enhance the owner’s satisfaction level.

The paper proposes using a predictive model to optimize the use of electricity in the V2G (vehicle to grid) service. The novelty of the mechanism as a kind of model predictive control (MPC) is that it seeks an effective way of managing electric energy in an Electric Vehicle (EV). Additionally, it proposes a new method of predicting the electricity consumption which allows the battery of an electric vehicle to reconcile two sides: both the system's and the user's demand will be met at the same time. The model allows for very precise determination of the vehicle's demand for the energy related to the progressive movement, taking into account the parameters characteristic of a given vehicle model, its suspension structure and aerodynamics. In addition, the machine learning algorithm was proposed for the prediction model as a hybrid (offline and online) of supervised learning. As the first part of the research, by using Matlab/Simulink/dSpace software, a prediction of EV energy consumption was created on a selected route at different times of the day (offline data matrix). At the same time, the simulated route was travelled by a BMW i3 EV (online data matrix). Based on the developed machine learning algorithm the results of the electric energy consumption were compared. The research confirms that if the correct mechanism for prediction of energy consumption by the EV is used, it is possible to define the amount of energy needed for a V2G service. The measurement error was obtained at 0.5%. The added value is setting up the EV energy security of customers after the V2G service and a correct WIN-WIN relation between the Low Voltage grid and EV customers' needs.

With the development of new energy technology, the number of electric vehicles (EVs) will continue to increase, which will seriously threaten the safe operation of the power grid. The V2G information interaction system can help the power grid to achieve safe and reliable operation. Therefore, under the framework of the V2G information interaction system, this paper establishes the communication system between EVs and charging piles based on power line communication (PLC), which can reduce the cost and avoid the problem that the incompatible communication interface caused by different specifications of charging piles. Due to the severe impulse noise (IN) communication environments between EVs and charging piles, the transmission performance would be seriously degraded. Hence, an improved-priori-aided sparsity adaptive matching pursuit (IPA-SAMP) is proposed to reconstruct IN. In the process of IN cancellation, the measurement vector is obtained from the null sub-carriers. In order to efficiently reconstruct IN, as the input of the IPA-SAMP algorithm, partial support are improved by the optimized threshold, which significantly improve the accuracy and robustness of the recovered IN. The proposed IPA-SAMP algorithm can effectively recover IN in the channel between EVs and charging piles, improves the reliability of the information exchange system, which is validated by computer simulations.

This article presents a two-stage framework for optimal Electric Vehicle (EV) charging/discharging strategy for DC Microgrid (MG) with Distributed Generators (DGs). A multi-objective optimisation task aimed at minimising system losses and EV battery degradation with Vehicle-to-Grid (V2G) peak shaving service has been realised. This coordinated EV integration into the DCMG was formulated as a directed weighted single source shortest path problem that was solved using a modified Dijkstra's algorithm. The weights of the edges were obtained using primal-dual interior point method. The proposed framework has been experimentally verified using simulations with a test DCMG system with practical IEEE European low voltage test feeder load profiles. Results show realisation of peak demand shaving leveraging on EV discharge with minimal on-board battery degradation as well as reduced system losses. It is also shown that the proposed two-stage framework reduces the battery state of charge (SOC) sample space requirements in the analysis, thus, reducing the computational burden.

With electric vehicle's charging information, the utilities can increase the efficiency and reliability of Vehicle-to-Grid (V2G) while the electric vehicle consumers can better manage their energy consumption and costs. However, since charging information are commonly transmitted via public network or wireless link, their communications lack trusted third party for identity authentication and key distribution, and are constantly exposed to traffic analysis attacks. In this article, we propose a secure and anonymous communication scheme for delivering charging information in V2G. Different from previous works, the scheme in this article creatively incorporates identity authentication into key distribution without trusted third party, which improves the security of communication in public networks. Moreover, the proposed scheme is more resistant to traffic analysis since it preserves the anonymity of charging information by splitting and forwarding them pseudo-randomly. As the performance analysis reveals, our scheme is able to provide better anonymity without the support of trusted third party, and what's more, it can achieve high cryptography capability as traditional communication schemes do. Therefore, it is more practical in real-world V2G.

This paper presents a switching bi-directional buck-boost converter (SBBBC) for vehicles-to-grid (V2G) system. The topology can provide an energy bi-directional flow path for energy exchange between the Li-battery/supercapacitor (SC) hybrid energy storage system (HESS) of the electric vehicle and the grid. This topology not only has buck-boost capability, but also has the function of energy management. In this paper, the state-space averaging method is used to analyze the stability of the topology in boost and buck modes. The control strategy is given according to the state of charge (SOC) of the energy storage system to ensure that the output voltage and current are stable. And the Li-battery is charged in constant current (CC) and constant voltage (CV) mode. The voltage and current controllers are designed in the frequency domain based on bode plots. Finally, the electrical feasibility of the topology, the suitability of the design controller and control strategy are verified by simulation and experiment.

The blend of Internet of Things (IoT) and social networking has introduced the emerging notion of social Internet of Things, which is bringing advancements in the operation of concerned industries. There are various prevailing applications of social internet of things; smart grid is one of them. The smart grid is considered as economical robust and intuitive replacement of the conventional grid. However, smart grid experiences two significant challenges, i.e. privacy and security. This article is dedicated to resolve the privacy and security concerns for the vehicle to grid networks to facilitate their large-scale integration with smart grids. As anticipation, a vigorous key agreement protocol is introduced to achieve mutual authentication with an aided feature of user anonymity. Moreover, efficiency in terms of computation, communication and storage needs to be taken care for resource-constrained infrastructure like vehicle to grid network. We have introduced a lightweight key agreement protocol using lightweight cryptographic operations such as exclusive-OR and hash etc. This protocol is validated through a formal security model. An informal security analysis is also elaborated to present the security strength of our protocol against well-known attacks. Furthermore, we have implemented all the cryptographic operations used at trusted agent's side on a desktop system, while the operations used at battery vehicle unit's side are implemented on an Arduino to get the experimental results. In the end, we have presented a performance analysis to compare the performance of our protocol with related ones. This comparison highlights that our protocol is not only lightweight but also efficient in terms of communication and storage cost of related protocols.

In the Vehicle-to-Grid (V2G) network, the electric vehicles (EVs) need to report their respective information, such as vehicle identity, battery capacity, battery state of charge (SoC), current location, and driving direction, to the power grid through the distributed local aggregators (LAGs), so that the power grid can dispatch power reasonably. However, the information has much to do with privacy of the EVs. The issue of how to solve the contradictory needs of privacy protection and information report is a challenging problem in the V2G network. This paper proposed a privacy preserving authentication mechanism together with a key exchange between the EVs and the LAGs, in which the EVs do not use any pseudonyms. Besides satisfying the requirements of anonymity, confidentiality, unlinkability, nonrepudiation, traceability and revocation, the scheme can further support forward security. The performance analysis shows that the proposed scheme is efficient in terms of computation and communication overhead. The experimental results show that the proposed scheme has fewer exchanged messages in the authentication process among compared schemes.

In order to realize peer-to-peer (P2P) transactions between electric vehicles (EVs) in vehicle-to-grid (V2G) networks, we propose an EV power trading model based on blockchain and smart contract. Firstly, based on the blockchain and smart contract technology, a decentralized power trading model is proposed to realize the information equivalence and transparent openness of power trading. Then, considering the randomness and uncertainty of EV charging and discharging, the EV trading parties use the reverse auction mechanism based on dynamic pricing strategy to complete the transaction matching, which can not only improve the profit of the less competitive power seller, but also it can reduce the cost of the electricity purchaser. Finally, in order to verify the feasibility of our proposed scheme, V2G's EV power trading smart contract was designed, and the smart contract was released to Ethereum and simulated experiments were carried out. The effectiveness of the proposed scheme is verified by simulation experiments and comparison with traditional power trading schemes.

This paper introduces a planning model that can be used by an investor who would like to provide ancillary services (AS) to electricity markets. The proposed model helps the investor evaluate two potential options: aggregating distributed batteries in electric vehicles (EVs) or using a dedicated energy storage system (ESS). For EV aggregations in AS markets, the targeted EV fleet size, which is a function of energy tariff charged by the EV aggregator, is a key planning consideration. In the case of ESS, the physical size is the main planning consideration. Using the proposed model, the two options for maximizing the investor's long-term payoffs are analyzed and compared by assuming the same initial investment cost for both options. Accordingly, the investor's daily bidding strategy in AS markets is optimized for each case. The net present worth is used as the basis for comparing the two investment options, and sensitivity analyses are carried out to study the impact of planning and operation variables on the feasibility of the two options. Simulation results consider both options and discuss the results for providing AS to electricity markets.

The popularization of electric vehicles and new forms of power generation has led to a change in the architecture of the residential microgrid. As the mode of power distribution on the user side must be replanned, the optimization of user costs for a residential building in Harbin was selected as the research focus. From local electricity prices and building characteristics, this paper uses a linear programming algorithm to calculate the optimization of power distributions. The results show that users can save an average of 15.6% on their electric bills annually throughout the life cycle of residential microgrid equipment and save up to 55.3% in the summer. The results of this paper can guide structural designs and modes of power distribution for residents of Harbin.