An optimal sizing framework for renewable energy microgrids considering stationary batteries & electric vehicles as mobile energy storage systems: case study in Morocco

How to choose mobile energy storage or fixed energy storage in high proportion renewable energy scenarios: Evidence in China

In the face of future energy and environmental challenges, huge growths in transportation as well as its electrification have been witnessed in recent decades, which has brought a large number of potential mobile energy storage resources. Besides the electric vehicles (EVs) under plug-in charging mode, which have been extensively studied in the literature, newly emerging types of mobile energy storage resources such as the EV batteries under battery leasing and swapping mode, and truck-mounted mobile energy storage systems (MESSs) can also bring potential opportunities to further improve the reliability, flexibility and efficiency of future smart grids. However, as these emerging mobile energy storage resources also bring close couplings and interdependences between power and transportation systems, research gaps still exist in terms of mathematical modeling and management scheme designs, which makes these emerging resources generally underutilized. In order to achieve the coordination between these mobile energy storage resources and power systems, as well as the full utilization of their temporal-spatial mobility while considering certain transportation system characteristics, novel models and management schemes are urgently needed. To achieve the full utilization of the EV battery mobility under battery leasing and swapping mode while ensuring uninterrupted battery swapping services, and to fill in the gaps in the modeling and management of the coordinated operation between battery charging stations (BCSs) and battery swapping stations (BSSs), this thesis first proposes a novel closed-loop supply chain (CLSC) based battery swapping-charging system (BSCS) model. A novel battery logistics model based on the time-space network (TSN) technique is established to describe and utilize the temporal-spatial mobility of the EV batteries. The charging and discharging of the batteries in the BCSs and the battery logistics are optimally managed to maximize the revenue of the BSCS while satisfying the battery demand for battery swapping service at each BSS. A heuristic method based on a randomly permuted alternating direction method of multipliers (RP-ADMM) is adopted to solve the optimization problem in a more efficient and distributed way. Simulation results verify the feasibility of the proposed model and the heuristic solution with small optimality losses and less computation time. To take into account the large-scale deployments of BSCSs in the future, this thesis further extends the single BSCS model to a multi-region battery swapping and charging network (MBSCN) model to achieve efficient and collaborative management of the BSCSs located in different regions with different EV user behavior and local system features. A novel multilayer TSN-based network-wise battery logistics model is proposed to manage both the intra-BSCS and interregional battery logistics more efficiently, which also enables the sharing of the mobile energy storage resources within different BSCSs. A distributionally robust chance-constrained service model is established to address the regional battery demand uncertainties without requiring assumptions on the probability distributions or a large amount of historical data. The battery charging and discharging tasks are optimally allocated to each BSCS and the battery logistics are optimally managed according to the locational energy price and the battery demands. Simulation results are presented to verify that the proposed MBSCN model is more flexible and efficient when interregional battery exchanges are incorporated. For truck-mounted MESSs, to fully leverage their mobility for enhancing the operational flexibility of coupled distribution and transportation networks (CDTNs) while considering the system uncertainties from variable renewable energy (VRE) sources and the daily traffic demands in transportation networks, this thesis proposes a stochastic management scheme to achieve the coordination among MESSs, hybrid AC/DC microgrids (MGs) and CDTNs. A novel stochastic multi-layer multi-timescale TSN model that incorporates the hourly traffic user-equilibrium (UE) results obtained from the adopted traffic assignment problem (TAP) model is also proposed to facilitate the modeling and scheduling of different MESSs with various mobility features while considering the uncertainties in the traffic flows in the transportation network and the resulting congestion delays. The scheduling and management of MESSs and CDTNs are formulated as a two-stage stochastic optimization problem, where the uncertainties of traffic demands, VRE outputs and loads are depicted using scenarios. Case studies are performed to verify the effectiveness of the proposed two-stage stochastic management scheme and the UE-TSN model, as well as the effectiveness of MESSs to serve as mobile energy storage resources for MGs with mismatched generation and conversion capabilities.

Robust multi-objective optimal design of islanded hybrid system with renewable and diesel sources/stationary and mobile energy storage systems

The Doctoral Thesis is devoted to the study of a solution for improving the energy efficiency of electric public transport by using mobile supercapacitor-based energy storage systems. The modelling variants of electric transport overhead DC lines for the simulation of total energy consumption are analysed. It was found that the additional mass of a mobile energy storage system does not increase the energy consumption of the electric transport by more than 5 %, while the possibility of storing the energy recovered during braking reduces the total energy consumption from the supply substation by up to 40 %. A method for the design of a mobile energy storage system is described, considering the amount of energy to be recovered and the progressive deterioration of the supercapacitor cell parameters. It is found that the cost of energy saved during the lifetime of the supercapacitors is significantly higher than their cost of purchasing and installing, especially at higher electricity prices.

Due to the rapid increase in electric vehicles (EVs) globally, new technologies have emerged in recent years to meet the excess demand imposed on the power systems by EV charging. Among these technologies, a mobile energy storage system (MESS), which is a transportable storage system that provides various utility services, was used in this study to support several charging stations, in addition to supplying power to the grid during overload and on-peak hours. Thus, this paper proposes a new day-ahead optimal operation of a single MESS unit that serves several charging stations that share the same geographical area. The operational problem is formulated as a mixed-integer non-linear programming (MINLP), where the objective is to minimize the total operating cost of the parking lots (PLs). Two different case studies are simulated to highlight the effectiveness of the proposed system compared to the current approach.

The widespread adoption of electric vehicles introduces significant challenges to power grid stability due to uncoordinated large-scale charging and discharging behaviors. By addressing these challenges, mobile energy storage systems emerge as a flexible resource. To maximize the synergistic potential of jointly scheduling electric vehicles and mobile energy storage systems, this study develops a collaborative scheduling model incorporating the prediction of geographically and chronologically varying distributions of electric vehicles. Non-dominated sorting genetic algorithm-III is then applied to solve this model. Validation through case studies, conducted on the IEEE-69 bus system and an actual urban road network in southern China, demonstrates the model’s efficacy. Case studies reveal that compared to the initial disordered state, the optimized strategy yields a 122.6% increase in profits of the electric vehicle charging station operator, a 44.7% reduction in costs to the electric vehicle user, and a 62.5% decrease in voltage deviation. Furthermore, non-dominated sorting genetic algorithm-III exhibits superior comprehensive performance in multi-objective optimization when benchmarked against two alternative algorithms.

Due to the short-term large-scale access of renewable energy and residential electric vehicles in residential communities, the voltage limit in the distribution network will be exceeded, and the quality of power supply will be seriously reduced. Therefore, this paper introduces the mobile energy storage system (MESS), which effectively solves the problem of overvoltage limit caused by the large number of distributed power sources and household electric vehicles in the distribution network. This paper proposes an optimal scheduling model for distribution network based on mobile energy storage system. First, the space-time energy transfer model of mobile energy storage is established, and the transfer cost of MESS and the income of peak shaving and valley filling are considered. According to daily driving data of household electric vehicle (EV), a prosumer group electric vehicle charging and discharging model is established; After that, by establishing a target model for maximizing MESS operating income and penalizing voltage overshoot under different scenarios such as low peak load of electric vehicles and different initial capacities of MESS, a multi-scenario multi-objective collaborative optimization model for the distribution network is established; Finally, an improved IEEE33-bus system is used to analyze the calculation example. The results of the calculation example show that the optimal scheduling model in this paper can improve the photovoltaic consumption capacity and improve the voltage limit problem, which verifies the effectiveness and economy of the scheduling model.

The paper presents a method for managing the energy storage and use of a mobile supercapacitor energy storage system (SC ESS) on a tram vehicle for the purpose of active voltage stabilization of the power grid. The method is based on an algorithm that identifies the need to utilize the energy of the SC ESS depending on changes in the voltage of the power grid caused by the driving of other nearby tram vehicles. The waveform of the current flowing into or out of the SC ESS during this control is determined based on Pontryagin’s minimum principle, which optimizes the minimum change in the voltage level at the pantograph and the minimum temperature of the supercapacitor. In this way, this approach aims to minimize the changes in the voltage of the power grid caused by other vehicles and to maximize the lifespan of the supercapacitor. The algorithm was tested within the MATLAB/Simulink R2022b programming environment and experimentally validated with an HIL simulation experiment in a laboratory setup to emulate the rail vehicle system, the supercapacitor, and the power supply network.

In this study, the stochastic energy management, and scheduling of a renewable microgrid involving energy sources and dynamic storage is performed considering energy resource and demand uncertainties and demand response (DR) using the two-point estimation method (2 m + 1 PEM). The three-dimensional objective function is defined as maximizing the renewable hosting capacity and minimizing the operation cost, and emission cost minimization. The decision variables include installation location and size of the renewable resources and mobile energy storage system (MESS), determined using a multi-objective enhanced grey wolf optimizer (MOEGWO) improved based on the logistic chaotic mapping integrated with fuzzy decision-making approach. The simulations are implemented for several cases of employing MESS, DR, and uncertainties to investigate the proposed approach's efficacy. The MOEGWO performance is confirmed to solve the ZDT and CEC'09 functions according to some well-known algorithms. Then, the performance of the MOEGWO is evaluated on the stochastic energy management and scheduling of the renewable microgrid. The results indicate that considering the dynamic MESS causes reducing the operation and emission costs by 23.34% and 34.78%, respectively, and increasing the renewable hosting capacity by 7.62% in contrast to using the static MESS. Also, the stochastic problem-solving considering uncertainties showed that operation and emission costs are raised, the renewable hosting capacity is decreased, and the uncertainty impact is reduced in the condition of DR application. So, the results validated the proposed methodology's effectiveness for minimizing the operation and emission costs and maximizing the renewable hosting capacity. Moreover, the superior capability of the MOEGWO is confirmed in comparison with the multi-objective particle swarm optimization to obtain lower operation and emission costs and higher renewable hosting capacity.

Renewable energy is a key technology in reducing global carbon dioxide emissions. Currently, penetration of intermittent renewable energies in most power grids is low, such that the impact of renewable energy's intermittency on grid stability is controllable. Utility scale energy storage systems can enhance stability of power grids with increasing share of intermittent renewable energies. With the grid communication network in smart grids, mobile battery systems in battery electric vehicles and plug-in hybrid electric vehicles can also be used for energy storage and ancillary services in smart grids. This paper will review the stationary and mobile battery systems for grid voltage and frequency stability control in smart grids with increasing shares of intermittent renewable energies. An optimization algorithm on vehicle-to-grid operation will also be presented.

As transportation electrification increases globally, new technologies emerged in the past few years to meet the growth of the electricity demand. Mobile Energy Storage Systems (MESS) offer versatile solutions, aiding distribution systems with reactive power, renewables integration, and peak shaving. An MESS can be utilized to serve electric vehicles (EVs) in different parking lots (PLs), in addition to supplying power to the grid during overloads. The task of multiple stationary units can be achieved using MESS at a relatively lower cost. This paper proposes an MESS owned by multiple PLs sharing the same geographical area and sharing its capital and operational cost. The main objective of the proposed approach is to dispatch the MESS in conjunction with optimal EVs’ charging coordination to minimize operational costs and address the extra demand of PLs. A mixed‐integer nonlinear programming (MINLP) problem is formulated and solved. Considering electricity price variations and EVs uncertainties, three different case studies are performed to highlight the efficiency and success of the proposed approach. The simulation results in a huge reduction in the total operation cost and the savings reach up to 27.51% in comparison with the base case.

Enhancing solar energy generation utilization along highways: optimizing electric vehicle charging-swapping schemes and scheduling mobile energy storage systems

Microgrids with AC/DC architecture benefit from advantages of both AC and DC power. In this paper, daily operation problem for a zero-carbon AC/DC microgrid in presence of electric vehicles (EVs) is considered. In this framework, EVs' batteries are mobile energy storage systems, which allow desirable operation of the microgrid during peak demand hours. This study shows in absence of storage system, EVs' batteries can be properly managed to satisfy the system requirements. In the case studies, several sensitivity analyses based on variations in battery degradation costs, solar irradiance, and inverter capacity are investigated.

A mobile energy storage system (MESS) is a localizable transportable storage system that provides various utility services. These services include load leveling, load shifting, losses minimization, and energy arbitrage. A MESS is also controlled for voltage regulation in weak grids. The MESS mobility enables a single storage unit to achieve the tasks of multiple stationary units at different locations. The MESS is connected to the grid at specific substations (or buses) known as MESS stations. This paper proposes an optimization algorithm for sizing and allocation of a MESS for multi-services in a power distribution system. The design accounts for load variation, renewable resources intermittency, and market price fluctuations. A realistic dynamic model for the MESS is adopted to consider the capacity and lifetime constraints. A detailed network power flow model is utilized to include voltage constraints, feeders, and transformers ampacity in the problem formulation. By considering all these constraints, the resulting sizing problem is a mixed-integer nonlinear problem. This paper presents the problem formulation and proposes a solution using a hybrid optimization technique. The adopted technique is based on the particle swarm algorithm and mixed-integer convex programming. A case study is conducted on a real 41-bus radial feeder to validate the proposed sizing technique, and investigate the MESS profitability to the system operator.

Chapter 8 - Application of electric vehicles as mobile energy storage systems in the deregulated active distribution networks