Multiobjective Optimization for Frequency Support Using Electric Vehicles: An Aggregator-Based Hierarchical Control Mechanism

Electric vehicles (EVs) as distributed storage devices have the potential to provide frequency regulation services due to the fast adjustment of charging/discharging power. In our previous research, decentralized vehicle-to-grid (V2G) control methods for EVs were proposed to participate in primary frequency control. In this paper, our attention is on bringing a large number of EVs into the centralized supplementary frequency regulation (SFR) of interconnected power systems. An aggregator is the coordinator between EVs and the power system control center. The aggregator calculates the total frequency regulation capacity (FRC) and expected V2G (EV2G) power of EVs based on the data communicated between the aggregator and individual EVs or EV charging stations. With FRC and EV2G power, a V2G control strategy is proposed for the aggregator to dispatch regulation requirements to EVs and EV charging stations. In individual EV charging stations, the FRC is calculated on the basis of the V2G power at present time, and EV2G power is presented considering both frequency regulation and charging demands. Besides, V2G control strategies are developed to distribute regulation requirements to each EV. Simulations on an interconnected power grid based on a practical power grid in China have demonstrated the effectiveness of the proposed strategies.

Many EU countries have used financial incentives to promote electric light commercial vehicles and decarbonise urban freight transport. However, the high costs of electric light commercial vehicles still pose a significant adoption barrier. This paper evaluates the impact of financial incentives on the total cost of ownership of electric light commercial vehicles in the European Union and their role in achieving the cost-competitiveness of electric vehicles. A pairwise comparison is performed for three weight categories of electric and diesel vehicles in twelve countries. Our analysis shows that financial incentives have a high impact on the total cost of ownership and have proven necessary to achieve electric vehicles' cost-competitiveness in most cases. However, in specific cases, financial incentives may no longer be needed as the cost of electric vehicles' ownership would have been lower than diesel vehicles even without them. This paper demonstrates the importance of adjusting government incentives to avoid unnecessary funding allocations while promoting the decarbonisation of light commercial vehicles, particularly as the number of incentives does not directly correlate with electric vehicles penetration.

In recent years, the integration of renewable energy has caused problems of grid frequency quality. However, Electric vehicles (EVs) which can be a mobile energy storage and controllable load provide a new way to solve the problems. This article addressed a fuzzy self-adaptive PID power response strategy and established a model considering EV frequency regulation with the software MATLAB. With the model, several different scenarios were studied to find out how frequency deviation is reduced with EV participating in frequency regulation. It turned out that the frequency deviation and recovery time were reduced when EVs offered primary and secondary frequency regulation. And the fuzzy self-adaptive PID power response strategy had better performance in reducing frequency deviation and recovery time. Therefore, the power system could be more stable and economic based on the strategy.

Increased use of renewable energy sources in energy sector as well as improvements and electrification in transportation sector significantly contribute to reduction of green-house gasses emissions and mitigation of problems with fossil fuel dependency. Optimal integration of electric vehicles (EVs) into the grids and their charging/discharging schedules have to be realized in accordance with electricity demand, day-ahead electricity market prices and intermittency of photovoltaic and wind generators electricity production. A microgrid that includes non-deferrable loads, renewable energy sources, EV fleet and its charging station is analyzed in this paper. Its Vehicle-to-Grid (V2G) service is optimized with the aim of minimizing the operational costs and obtaining peak load shaving and valley filling of the load curve, which is especially effective in the case of EVs fleet with occupational time intervals known in advance. Optimized schedule of EVs charging and discharging is obtained as a result of the procedure that uses multi-criteria optimization function. These criteria include minimization of microgrid electricity costs as the local aggregator’s benefit, maximization of the flattening of total microgrid demand curve as main grid operator’s benefit, and minimization of battery degradation (due to a number of charging/discharging cycles) as EVs owner’s benefit which is the novelty of this paper. Experimental analysis is performed on several scenarios and program Lingo is used to solve the optimization problem.

Photovoltaic (PV)-powered electric vehicle charging stations (EVCS) are expected to play a critical role in achieving carbon neutrality in the transport sector, as PV power has a great ability to reduce CO 2-emission. By integrating PV into an EVCS, solar energy can supply a considerable portion of the electric vehicle (EV) energy demand. Besides a good energy management strategy (EMS) for the system, the efficiency of the system is an important aspect that needs to be discussed. Be it EV charging/discharging, energy exchange with the grid or battery use - power losses occur in the system. Previous studies have mostly only discussed the effects of EMS on energy losses in the system. In this paper we conduct a systematic analysis of the converter efficiency and its effect on the energy losses for an EVCS integrated with PV, stationary battery (BAT), and grid connection. A DC- and an AC-coupled EVCS are compared on the basis of example systems. Converter efficiency for PV, grid, BAT, and EVs and BAT efficiency are considered for the analysis. A comprehensive comparison of system efficiency and energy losses over different seasons and a full year is presented and discussed. Finally, we discuss the financial losses influenced by the efficiency losses in the system over the year. It is shown, that the overall system efficiency is almost the same for both EVCS with about 92%. The DC-coupled EVCS's higher number of converters leads to a higher sum of energy loss compared to the AC-coupled EVCS, even including the onboard-charger of the EVs. For the AC-coupled EVCS, more than 50% of the energy loss occurs due to the EV's onboard-charger. These losses are paid for by the EV user. However, even if the EVCS operator also owns the EV fleet, regarding the example system looked at, the AC-coupled EVCS is about 10% more profitable.

The promotion of electric vehicles (EVs), triggered by environmental concerns, can also increase the flexibility of power systems through ancillary services, such as frequency regulation among others. Nevertheless, EV penetration has increased the concern regarding voltage drops in distribution grids. This concern has motivated researchers to examine EV reactive power provision to mitigate such problems. This work proposes a local market perspective to promote EV reactive power provision, enabling distribution system operators to control the voltage level using cost‐effective solutions. The authors propose the extension of a centralised control framework that schedules EV frequency regulation to optimise the reactive power provided by the same EVs. Additionally, extra power losses in chargers while EVs provide reactive power were investigated, and the associated cost in the economic evaluations were considered. A test‐case with the IEEE 33‐node distribution grid is used to assess the market potential of EV reactive power provision. This new service extends EV penetration in a cost‐efficient way without causing voltage problems. The simulation concludes that it is economically feasible to use EVs for reactive local provision with efficient chargers. These outcomes sustain the potential of promoting such new EV services through a proper market in distribution grids.

This paper presents the day-ahead energy planning of passenger cars with 100% electric vehicle (EV) penetration in the Nordic region by 2050. EVs will play an important role in the future energy systems which can both reduce the greenhouse gas (GHG) emissions from the transport sector and provide the demand side flexibility required by smart grids. On the other hand, the EVs will increase the electricity consumption. In order to quantify the electricity consumption increase due to the 100% EV penetration in the Nordic region to facilitate the power system planning studies, the day-ahead energy planning of EVs has been investigated with different EV charging scenarios. Five EV charging scenarios have been considered in the energy planning analysis which are: uncontrolled charging all day, uncontrolled charging at home, timed charging, spot price based charging all day and spot price based charging at home. The demand profiles of the five charging analysis show that timed charging is the least favorable charging option and the spot priced based EV charging might induce high peak demands. The EV charging demand will have a considerable share of the energy consumption in the future Nordic power system.

An agent-based simulation study for escaping the “chicken-egg” dilemma between electric vehicle penetration and charging infrastructure deployment

Recent advancements in battery technology have made them more economically viable than ever before, making them suitable for various grid-scale applications. Due to their rapid response, batteries are attractive for providing ancillary services (AS), such as frequency regulation and reserve services, to the bulk power grid. On the other hand, an electric vehicle (EV) is viewed as a moving battery; consequently, EVs are also suitable for those services. This research proposes a linear planning model for an Electric Vehicle Aggregator (EVA) within a distribution network (DN) to offer Ancillary Services (AS) to the bulk power grid. The model takes into account contract design by identifying optimal incentives or charging tariffs that Electric Vehicle (EV) owners would be willing to pay the EVA for charging their vehicles. Additionally, the model takes into account the size of the electric vehicle (EV) fleet as a crucial planning factor for EV aggregation that depends on the energy pricing set by the EVA. The proposed model is developed to maximize the overall profit of bidding capacities in the energy and AS markets while supporting the operation of an unbalanced DN by maintaining the DN limits. Simulation results and sensitivity analyses on the model have been carried out to support the investment model and investigate the change in the optimal solution across different case studies. Simulations show that the optimal charging tariff ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> ) is ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.02~{\$}/kWh$ </tex-math></inline-formula> )when considering scenarios where Distribution Network (DN) limits, such as thermal and voltage constraints, are ignored for all EV participation versus <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> relations. When including the DN, the optimal payoffs vary based on the relationship between the charging tariff and the number of participating EVs.

Plug-in electric vehicle (PEVs) in demand response programs (DRPs) have indicated a great potential to enhance power system reliability by reducing their charging demand. However, PEVs often fail to reduce their load due to some external factor. Herein, an analytical approach for reliability modeling of aggregated PEVs, the so-called electric vehicle fleet (EVF), is presented. A systematic method based upon frequency and duration approach is utilized to model an EVF similar to that of a multi-state conventional unit, where the probability, frequency of occurrence, and departure rate of each state can be acquired. The proposed methodology is capable of considering uncertainty of customer participation in DRPs for both flexible and inflexible PEVs. Interval data, which is the core deliverable of advanced metering infrastructure (AMI), is essential for PEVs to participate in DRPs. Thus, the impact of AMI failure is investigated on the implementation of EVFs in reliability assessment. Several important factors such as different penetration level of EVFs are taken into account in the analysis. The hourly historical data is employed to achieve the customers participation regime. The Roy Billiton test system and IEEE 24-bus system are used to trace the capability of proposed EVF reliability model.

The government policies and cost benefits led to the surge in penetration of Plug-in Hybrid Electric Vehicles (PHEVs) and Battery Electric Vehicles (BEVs) into both public and private sectors, challenging the electric grid's potential to maintain its stability and efficiently serve the purpose. The penetration of PHEVs brings technological advancements and environmental benefits to the traditional transportation and electrical power systems. Designing of appropriate charging methods to satisfy PHEV owners helps to improve system performance and avoid transmission and distribution system expansion.;The primary objective of this thesis is to model a coordinated charging algorithm to charge maximum number of PHEVs with the existing infrastructure at a Charging Station (CS) located in residential locality. The stochastic driving patterns of PHEVs are studied and different charging strategies are developed. The Optimized charging scheme is obtained for American Electric Power utility distribution feeder modeled in OpenDSS. Further, PHEV customers are modeled into price responsive consumers capable of participating in Demand Response programs to economically charge their vehicles and render support to the stabilization of power system. Consumer behavior modeling has been done by developing extensive demand-price elasticity matrices (PEMs) which are utilized to calculate the level of demand response.;This thesis also proposes a concept of converting the existing Personal Rapid Transit (PRT) system from guideway power rail propelling vehicles to Battery Electric Vehicles (BEVs) by developing a novel routing algorithm based on the data provided by West Virginia University's Department of Transportation and Parking. This turns into a feasibility analysis of implementing new EV technologies in electrification of public transportation. The routing algorithm involves Mixed Integer Linear Programming (MILP) Optimization resulting in hourly schedule of all the vehicles satisfying the passenger demand. The effect of charging load of BEVs on the electrical distribution system is analyzed by performing a time series simulation on AEP distribution feeder.;Additionally, the impact of PHEV charging on power market prices i.e. Locational Marginal Prices (LMP) in a distribution microgid scenario is presented. Locational marginal pricing is a market based wholesale pricing approach adopted by six out of ten national electricity markets. The uncoordinated charging of EVs results in unplanned peak demand causing system changes and fluctuation of LMP. The stabilizing effect of coordinated charging scheme is demonstrated on IEEE 13 Node test system.

Aggregated electrical vehicles (EVs) can flexibly serve grid frequency regulation (FR) with adjustable FR capacity (FRC) via vehicle-to-grid (V2G) technology. Therefore, current research applies integral reinforcement learning (IRL) to FR as it can easily solve difficult modeling and optimization problems. However, communication between EVs is vulnerable to denial of service (DoS) attacks, which can significantly degrade FR performance and even destabilize V2G FR systems. This paper proposes an adaptive V2G FRC-based FR scheme with IRL to improve FR resilience and mitigate FR performance degradation against DoS attacks. The proposed scheme optimizes V2G control by IRL without analytical models, integrating an event-triggered mechanism to reduce communication burden. It adapts V2G FRC to attack intensity to minimize frequency deviation and mitigate the impact of DoS attacks. The analytical estimation of convergence rate establishes the quantitative relationships among control performance, V2G FRC, and attack intensity, thereby deriving an adaptive mechanism. The theoretical analysis of the proposed scheme yields stability conditions that guarantee the FR performance. The simulations were performed on the IEEE 39-bus test system to verify the effectiveness and advantages of the proposed scheme. The results indicate that V2G FRC should be scaled down to mitigate performance degradation when intensive attacks occur.

This paper investigates the impacts of increasing levels of electric vehicle (EV) penetration in an urban distribution network of Surabaya, Indonesia. Surabaya is the second most populous city in Indonesia where the network is operated by national electric company of Indonesia (PT PLN Disjatim) The investigation is conducted under concentrated and distributed EV penetration (each with 10% and 20% penetration calculated as a percentage of residential load). Loads are modeled as dependent variable of the hour selection within the day and added with a normally distributed uncertainty on each of selected load value to better replicate uncertainty within the daily loading. Whereas, EV charging pattern is modelled as probabilistic uncertain sources using Markov Chain Monte Carlo to better replicate the practical condition. Voltage at each node and aggregate losses are employed to quantify and contextualize the impact. Maximum EV penetration where it violates the statutory threshold is also explored in this paper. Simulation result shows that Surabaya distribution network able to withstand EV penetration in both scenarios with the maximum EV penetration is almost doubled than the highest penetration level from simulated cases.

With the explosive penetration of Electric Vehicles (EVs) in the last decade, the load on the existing grids have exaggerated manifold. This has led to severe demand-supply imbalances causing grid instability and reliability issues. Towards this end, Demand Reponse (DR) management has been identified as an important means to tackle this problem. Thus, in this paper, a robust Stackelberg Game has been proposed wherein the Utility Provider (UP) and fleet of EVs are assumed to be playing the roles of a competitive leader and followers, respectively. In the considered game, EVs charging problem has been formulated as a non-cooperative game; in which EVs decide their charging slot in accordance with the real-time electricity prices announced by the UP. The existence and uniqueness of the formulated Stackelberg Game has been theoretically proved in the paper. Further, the efficacy of the formulated game has been validated on real-time data traces obtained from Haryana State Electricity Board, India.

The aim of this thesis is to investigate novel recharging schemes for energy demand management (DM) of electric vehicles (EVs). While there has been a lot of work highlighting the importance of energy DM of EVs, most of the reported works do not expand on suggesting how such a DM system may be implemented. In this thesis the focus is on two aspects of DM system implementation. At the instantaneous control time scale, an alternative mechanism for frequency regulation with the aim of neutralising sudden changes in output power of electric generators is presented. At the recharge planning time scale, the aim is to avoid congestion and undesirable voltage drops in the distribution system, and a novel approach is presented that can improve voltage profiles. The problem of considering both voltage congestion and frequency regulation in a composite DM framework is also addressed. At the instantaneous control time scale, a novel distributed recharging rate controller is presented that is based on non-linear control and that yields a real time and distributed solution. This controller minimises communication overheads and allows EVs to join and leave at arbitrary times. From the perspective of recharging rate allocation, the controller achieves a Pareto efficient allocation which is also proportionally fair. The proposed controller is then applied to a system with a single, isolated, and unregulated synchronous machine and it is shown that the frequency can be used as proxy to the imbalance between produced and consumed electric power and hence communication overhead can be eliminated in such cases. A protocol is also discussed that can modify the controller and can implement the modified controller in a multi-machine system. Simulation is used to show the frequency regulation and fairness of recharging rates of EVs when the protocol and the modified controller are used. Subsequently, the integration of the recharging rate controller with the legacy protection system is also discussed. At the recharge planning time scale, the problem of congestion in the distribution system is addressed. Most of published literature on distribution system voltage issues deals with control of various network elements, for instance, on-load tap changers or banks of shunt capacitors on the distribution feeders. In this thesis, a complementary approach 7 8 is presented that can also improve voltage profile by scheduling EV load in such a manner that undesirable voltage drops are avoided or their severity is diminished. In this context, a novel approach is presented for recharging EVs in the same geographic neighbourhood that share the same secondary circuits when recharging. The approach is based on a numerical method called Smoothed Particle Hydrodynamics (SPH) that has been previously used by other researchers to solve the equations of fluid dynamics. The characteristics of the method used for the proposed approach as well as its performance in term of improvement in the reduction of voltage drops and its adaptation to elastic and non-elastic loads is highlighted via simulation. Finally, the approach is extended to also provide a frequency control reserve service.