The large adoption of electric vehicles (EVs), hybrid renewable energy systems (HRESs), and the increasing of the loads shall bring significant challenges to the microgrid. The methodology to model microgrid with high EVs and HRESs penetrations is the key to EVs adoption assessment and optimized HRESs deployment. However, considering the complex interactions of the microgrid containing massive EVs and HRESs, any previous single modelling approaches are insufficient. Therefore in this paper, the methodology named Hierarchical Agent-based Integrated Modelling Approach (HAIMA) is proposed. With the effective integration of the agent-based modelling with other advanced modelling approaches, the proposed approach theoretically contributes to a new microgrid model hierarchically constituted by microgrid management layer, component layer, and event layer. Then the HAIMA further links the key parameters and interconnects them to achieve the interactions of the whole model. Furthermore, HAIMA practically contributes to a comprehensive microgrid operation system, through which the assessment of the proposed model and the impact of the EVs adoption are achieved. Simulations show that the proposed HAIMA methodology will be beneficial for the microgrid study and EV’s operation assessment and shall be further utilized for the energy management, electricity consumption prediction, the EV scheduling control, and HRES deployment optimization.
The worldwide constructions of the smart grid technology bring significant development to microgrids, HRES, and EV technology. The microgrid is an integrated energy system consisting of interconnected loads, energy resources, and storages that can operate in parallel with the grid or in the island mode. Due to the needs of distributed generation, such microgrids have been popular over the years of development of the smart grid. And with the integration of HRESs including photovoltaic, wind, and biomass generators, the microgrids can deliver many advantages including reduced cost, increased reliability and security, renewable power generation, and power system optimization, bringing significant benefits to the grid enterprises and the public [
Therefore, considering the complex operations and interactions of EVs and HRESs in the microgrid, the proper HRESs deployment assessment and EV-recharging prediction are the keys for minimizing the microgrid operation cost and maximizing the utilization of the HRES power volatility. And the modelling methodology for the microgrid with high penetrations of HRES and EVs is fundamental and critical to enable those abilities [
Considering the model of microgrid power system with HRES and loads from EVs and from original consumers, this paper develops the methodology named Hierarchical Agent-based Integrated Modelling Approach (HAIMA). HAIMA also constitutes a flexible simulation system to further investigate the operation and interactions of the microgrid and EVs.
The analysis of the microgrid architecture is critical for the development of its model. The microgrid consists of multiple components of HRESs and power load. The HRESs of the microgrid include photovoltaic, wind energy, biomass, and battery storage systems. Meanwhile the multiple load of the microgrid includes the original load, that is, the operation of industrial, commercial electric equipment and residents’ home appliances, and the load of EV-recharging consumptions. All the components are connected together through a grid network and under the monitor of the microgrid management center.
Without loss of generality, the schematic figure of the proposed system under study is shown in Figure
Typical HRES structure.
The structure in HAIMA is hierarchically divided into three layers: management layer, component layer, and event layer. The management layer consists of the control center and the sensors in the microgrid. It is responsible for communication with the smart grid control center and monitors and controls the flows on the microgrid bus with multiple energy inflows and outflows from the component layer. The component layer is formed by major power generation and consumption components of the microgrid including HRES devices, original electricity consumers, EVs, and service stations. And the event layer mainly focuses on equipment operation and processes in the EV service stations, that is, recharging and battery-changing stations.
The proposed modelling methodologies of HAIMA are based on the ABM. As a computational approach to study multiagent systems (MASs), ABM has been a rapidly growing area for analyzing the electricity market in the past decade. The theoretical foundation of ABM mainly lies in complex system modelling (CSM), artificial life (AL), and swarm intelligence (SI). The fundamental approach of ABM is to simulate real-world systems with a group of interacting autonomous agents modelled as computer programs, and the agents shall interact with each other in the MAS [
To further enhance the effectiveness of the proposed agents, in the modelling approach the statechart is adopted to specify their states and behaviors. The statechart is a state machine that consists of states containing corresponding actions and transitions that can be triggered by events. Statecharts in HAIMA usually graphically capture the operations and conditions of certain agents and enable fast and convenient structuring of the microgrid model.
The DEM and SDM are also adopted in HAIMA. As a modelling approach based on entities, resources, and block charts, DEM is capable of describing the entity flow and resource sharing. Such process-centric modelling is a medium-low abstraction level modelling approach and is well suited for exactly describing the EV refuel activities in both EV-recharging stations and battery-changing stations because refuel services in those stations can naturally be described as a sequence of operations. SDM is a rigorous modelling method that enables the building of complex systems simulations and the design of more effective policies and organizations [
In the management layer, SDM is adopted in the study of the energy supply in the microgrid. In this model, the stock of the microgrid energy comprises inflows and outflows indicating energy generation and consumptions, and those flows are generated by the corresponding components in the component layer while being under the control of the microgrid management center. Figure
Logical setup of the management layer.
The agent modelling in HAIMA can be divided into two types, where HRES components are modelled as single, complete, static agents operating under the control of the management layer, and the load from residential, commercial, and industrial consumers as well as EVs is modelled as separated autonomous dynamic agents with unique statechart, electricity usage distribution, and so forth in order to utilize the advantages of ABM to generate a bottom-to-top electricity consumption phenomenon.
power generating: the generator is generating power to the microgrid; power discarding: the generated power is discarded; shut down: the generator is stopped for regular maintenance; failed: the generator is temporarily out of work and needs repairs.
Statechart of the HRES generation devices.
The six states of the energy storage system are as follows, while the corresponding statecharts are illustrated in Figures recharging: the battery storage system is being recharged by the microgrid; discharging: the battery storage system is discharging energy to the microgrid; fully recharged: the maximum SOC of the battery storage system has been reached; depleted: the minimum SOC of the battery storage system has been reached; shut down: the battery storage system is stopped for regular maintenance; failed: the battery storage system is temporarily out of work and needs repairs.
Statechart of the HRES energy storage system.
Distribution of the trip length.
As an active agent, the EV modelling in the microgrid should consider both the temporal and spatial aspects and the EVs’ behaviors. The main parameters impacting the EV energy requirement can be classified into three aspects: environment characters, driver characters, and EV characters. Environment characters include time, weather, climate, distribution of the EV-recharging and battery-changing station, and transportation conditions; driver characters include distributions of driving time and trip length; and EV characters include EV types, rated battery storage, recharging/discharging power, and efficiency. Given those characters, the SOC of the EV agent can be derived in the following equation:
With the proposed key agent parameters, the EV agents’ behavior can be derived through a series of functions. And the main behavior influencing the microgrid is the EV refuelling decision between EV recharging and battery changing. And with plenty of EVSEs available at parking lots of office buildings as well as a residential area, an ideal decision process for EV drivers is shown in Figure
Decision process for EV driver.
Based on the proposed agent's behavior, the EV agents’ states in the model can be described in the statechart as shown in Figure go tripping: the EV starts one trip of a day; trip ending: the EV ends one trip of a day; heading for BCS: the EV goes to the battery-changing station for refuelling; heading for RS: the EV goes to the EV-recharging station for refuelling; battery changing: the EV is under the service of battery changing; recharging: the EV is under the recharging service; standby: the EV ends all series of trips of a day; failed: the EV encounters a breakdown and needs repairs.
Statechart of the EV agent.
The original load of the microgrid is mainly formed by residential, commercial, and industrial load and is mainly influenced by characters including population, electrical appliance types, and energy consumption per capita. In HAIMA, with the preset population and proportion of the microgrid, static agents have been adopted to represent the residential houses, commercial and industrial buildings, and other electrical appliances. Apart from the recharging load from EVs, the massive introduction of HRES and EVs does not apparently impact the load distribution and power consumption of a certain area, and the original load curves from residential, commercial, and industrial load can be adopted to generate the original load agent.
DEM is proposed to model the inner operation in EV-recharging stations as well as battery-changing stations to capture the detailed refuel event of EVs. The service ability, congestion situation, can therefore be observed and further improved to raise EV customer service satisfaction. In HAIMA, EVs and their batteries are all passive entities that travel in the stations through a series of blocks representing processes of the recharging/battery-changing service sequence, as illustrated in Figure
Process of the EV agent in ERS and BCS.
The interconnections and interactions are critical to the integrated operation of HAIMA. The communications between management layer and component layers are established in the model to transmit the data and control messages; therefore in component layer the component agents’ behaviors can be controlled and their key parameters can then feedback to update the flow variables in the SDM of the management layers, which enables HAIMA to simulate the monitoring of the grid bus and the real-time control of the HRES. The EV agent in the component layer also serves as triggers to the event layers. With the EV agent states changing, relative blocks of the DEM will trigger the corresponding process in response, and the entities process of the DEM shall in turn trigger the transitions of the relevant EV agent statechart. The synchronization is also important for the model and should be guaranteed through time triggers.
In order to support systematically in-depth exploration of the microgrid operation and energy consumers’ behavior, the comprehensive simulation system is built which enables microgrid components to operate and interact with arbitrary virtual environments, enabling broader experimentation for the study of the HRESs and EVs in the microgrid.
The main interface of the simulation system in HAIMA is implemented by the AnyLogic software. As the only simulation tools that support DEM, ABM, and SDM, AnyLogic’s object-oriented model design paradigm provides for modular, hierarchical, and incremental construction of large models [
Main process of the battery-changing station.
To make the system visual and interactive, many charts and graphics are adopted in the model. Animations including elementary graphical shapes as well as various types of indicators and graphs are developed to enable real-time illustration of the operation data in HAIMA. The user interaction is also considered in the simulation system. Interactive elements such as sliders, buttons, and text inputs are used to control the model's execution at run time, while the control panel and user interfaces of them are well designed. Moreover, the simulation system also prepares the open architecture and sockets for the database connection. This enables the real-time data exchange to the other software, databases, and microgrid utilities. With the advantages of AnyLogic to incorporate spatial data, GIS maps are used in HAIMA simulation system to generate the location of recharging stations and battery-changing stations.
With the reusable active objects, visual interactive elements, and function to import database and GIS data, the constructed simulation system is visual, flexible, and extensible, and besides HAIMA model, the component-based design of the system allows it for the efficient creation of new case studies including centralized/decentralized control design, microgrid islanding mode, and dynamic power flow computing.
The Monte Carlo simulation is carried out to verify the effectiveness of the proposed microgrid model and simulation system. The electricity grid load consults are from the real load of the Electric Reliability Council of Texas (ERCOT) [
HRES components and EV parameters in the microgrid.
HRES component | Type | Total number | Rate power |
---|---|---|---|
Wind turbine | Endurance E3120 | 10 | 50 kW |
Photovoltaic panel | Sharp ND-62RU1 | 1000 | 1.1 kW |
Battery storage | Hoppeche 600 | 1000 | 0.2 kW |
Biomass generator | Grate stoker furnace | 1 | 700 kW |
Electric vehicle | Nissan LEAF | 500 | 3.3 kW |
Considering the reduction of the carbon emission, wind turbines and photovoltaic panels are firstly enabled to feed the energy consumptions, and extra generations can be absorbed by the unfulfilled battery storage system. The biomass generator is enabled only when the energy requirement of the load exceeds the outflow of the wind, photovoltaic, and battery storage systems. And the main grid shall supply insufficient energy when the total required energy goes beyond all the HRES generations in the microgrid.
Firstly, assume the EVSEs have been fully adopted in the proposed microgrid, and EVs shall immediately be recharged once parked. Therefore the simulation of microgrid energy supply and consumption is shown in Figures
HRES energy supply with 100% EVSE penetration.
HRES energy consumption with 100% EVSE penetration.
Then reduced the EVSE penetration ratio to be 60% and the corresponding simulation results are shown in Figures
HRES energy supply with 60% EVSE penetration.
HRES energy supply with 60% EVSE penetration.
Besides, the recharging and battery-changing times in continuous 48 hours are shown in Table
Statistics of PEV traveling in the microgrid.
EVSE penetration | Total EVs | Simulation time | Recharging times | Battery-changing times |
---|---|---|---|---|
100% | 1700 | 48 h | 4820 | 10 |
60% | 1700 | 48 h | 1950 | 110 |
Multiple modelling approaches have been developed in the research of the microgrid with EVs and HRESs, and by integrating them this paper proposes the hybrid agent-based integrated modelling methodology while developing a comprehensive simulation system. A hierarchical microgrid structure is proposed and with the integration of ABM, DEM, and SDM, the modelling methodology for each layer is proposed. Furthermore, based on the integrated modelling methodology, a visual, flexible, and extensible microgrid simulation system is designed to verify its effectiveness. The simulation examples indicate that the proposed model can well reflect the energy generation and consumptions in the microgrid, and HRES configuration defects, EV facility utilization, and the impact of EVs recharging load in a different time and penetrations can be reflected in detail. With the establishment of the proposed HAIMA and combining with the actual grid data and latest travel information, the impact of various parameters imported by the energy policy, scheduling management, weather and climate, population, and personal behaviors can be tested in risk-free space at a very low cost, which will help to strategize the microgrid management, HRES optimized configuration, and EVs optimized scheduling as well as refuel facility construction.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work is supported by the National Natural Science Foundation of China under Grants no. 61374097 and no. 201202073 and Science and Technology Research Project of Higher Education of Hebei Province under Grant no. QN20132010. The authors would also like to sincerely thank the AnyLogic Company (formerly XJ Technologies) and Beijing Greena Tech Ltd. for providing the necessary software in accomplishing the modelling and simulation work.