2nd German-West African Conference on Sustainable, Renewable Energy Systems SusRES – Kara 2021 Machine Learning https://doi.org/10.52825/thwildauensp.v1i.27 © Authors. This work is licensed under a Creative Commons Attribution 4.0 International License Published: 15 June 2021 Design of an intelligent system for controlling and balancing renewable energy flows in an autonomous micro-grid K. R. Assilevi1, A.S. Ajavon1, and K. H. Adjallah2 1 CERME, Université de Lomé, 01 BP 1515 Lomé 1, Lomé, Togo 2 LCOMS EA-7306, Université de Lorraine, 57078, Metz, France Abstract. Pooling different renewable energy sources (hydrogen, solar, wind, geothermal, etc.) enables developing a standalone energy micro-grid. The energy flows from these various sources are neither constant nor equivalent. Therefore, control and balancing mechanisms should be established for optimal energy utilization through an intelligent system based on interconnected microcontrollers networked with sensors. Our contribution addresses this issue by proposing an original architecture of an intelligent and distributed control system based on a sensor network and a strategy to share the electric power through the micro-grid. In our work we consider a micro-grid powered by sources of wind turbine, pv panels and battery which energy flows are controlled and balanced through our system depending on power demand of the loads. Alternating Current (AC) bus and Direct Current (DC) bus are tied together by an inverter. A set of microcontroller-sensor-actuators (which we named S.A.D for Sensor/Actuator Device) are deployed at strategic points in the micro-grid providing constantly data from power generated and consumed, equipment health and status. A control algorithm developed in relation to a network control strategy is implemented by combining the performance different microcontroller boards. Relying on existing literature works, a review of solution approaches to the challenging problem, of the power flows balancing between the different energy sources and storage batteries embedding appropriate IoT technologies and exploiting energy big-data platforms, is presented. Keywords: Micro-grid, sensor network, flow balancing, microcontroller, intelligent system, IoT, 5G Introduction Renewable energy micro-grids have proven to be an excellent alternative in terms of meeting energy needs around the world and especially in Africa where the traditional grid is showing its limits in providing electricity [1], especially in hard-to-reach areas [2],[3]. Depending of energy needs [4], different architectures are implemented [5] taking into account the available renewables energies. Depending on the context, some micro-grids are deployed by pooling available energy sources. These micro-grids consisting of various types of the micro-generators as distributed generator [6] (wind turbine, photovoltaic (PV) array, fuel cells, diesel generator, and wave generator, CHP,…), [7] local storage elements (flywheel, energy capacitors and batteries) and loads. Storage devices play a key role in micro-grid control, reliability and stability. So, with these different types of energy sources, the micro-grid architecture consists in an alternating current (hybrid AC) micro-grid with a direct current (DC) micro-grid, tied 175 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ Assilevi et al. | TH Wildau Eng. Nat. Sci. Proc.1 (2021) “SusRES 2021” together by a bidirectional AC/DC converter [8]. Distributed generators can be connected to the AC or to the DC feeder. This architecture combines the advantages of the AC and DC micro-grid. Since, the energy flows from these various sources are neither constant nor equivalent. control and balancing mechanisms [9], [10], [11] should be established for optimal energy utilization through an intelligent system based on interconnected microcontrollers [12] networked with sensors [13]. Our work was carried out on a micro-grid installed in a peripheral area of Lomé in Togo (West Africa), in a district where wind and sun conditions favor the production of wind and solar energy. The contributions in our work are as follows: (1) Design of an improved system architecture for controlling and balancing energy flows. (2) Development of a strategy for controlling and balancing energy flows. (3) Development and implementation of a responsive, intelligent algorithm embedded on microcontrollers installed at strategic locations in the micro-grid. (4) Connection of the system to an IoT platform via 5G network to monitor, analyze, and process new data resulting from the algorithm's implementation. Related work Recent work in micro-grid focused on the management of various renewable energies sources. Giorgio Graditi et al. [14] developed a heuristic-based formulation of shiftable loads; Amin et al. [15] formulated a model predictive control; Lei Zhang et al. [16] projected two- scale dynamic programming strategy and subsequently Ashabani et al. [17] proposed nonlinear control for energy management in micro-grid. In [18], Jinsung Byun et al. envisioned intelligent cloud home energy management system (iCHEMS), in which the appliances shedding is fared in accordance with the assigned priority considering the renewable energy capacity. K. Venkatraman et al. [19] developed a micro-grid controller integrating the output from multiple types of renewable energy conversion systems, namely, wind and solar along with diesel generator as well as battery storage with source and load control features using Field Programmable Gate Arrays. Another renewables energies sources system energy management is done by using PI controller in [20], [21],[22],[23]. Somnath Das et al. in [10], implemented a control strategy with fuzzy logic controller for smoothing of the power fluctuation and at the same time to maintain the battery state of charge with in allowable limits. Betha et al. [24] combined an autonomous PV and a wind turbine using a DC bus, and the generated output power from the system is fed to all connected loads, while the extra power is injected into the electric grid. Hajizadeh and Aliakbar Golkar [25] introduced an approach for active power sharing in a hybrid fuel cell/battery power source in order to improve the system’s efficiency and battery’s lifetime with an acceptable load. Elmouatamind et al. [26] introduced a micro-grid system platform for efficient integration and management of renewable energy sources and storage devices. Hangaragi [27] proposed a hybrid PV–wind system, which provides a sophisticated integration of the wind turbine and solar PV, in order to extract the optimum energy from the two sources, PV and wind. In [28] a microcontroller network is implemented, interconnected to micro-grid sources. In this architecture, microcontrollers are connected to key elements of the micro-grid: collectors, energy storage devices and the energy management system among others. 176 Assilevi et al. | TH Wildau Eng. Nat. Sci. Proc.1 (2021) “SusRES 2021” These microcontrollers are the entry point to the sensor network. They are responsible for collecting the data and information produced at the level of the sensors, and for sending them via a VPN (Virtual Private Network) connection to the gateways. The latter proceed to transfer the data after the authentication and authorization procedures. These data, which are encrypted, are then conveyed to the heart of the sensor network for analysis and exploitation. In our work, we used the performance of microcontroller boards, arduino and raspberry in order not only to make the system for controlling and balancing energy flows more efficient but also in case of addition of new components in the network and implementation of algorithms in arduino and python programming language for a dialogue between the platform and the cloud. Micro-grid architecture Figure 1. Micro-grid architecture The micro-grid, subject of our study pools four energy sources. The priorities come from solar panels and wind turbines. The energy generated by these two sources is each stored in a specific battery. The two batteries, combined constitute a battery bank which is the third source of energy. This source is used in the event of a shortfall in solar and wind power generation. The fourth source of energy which is used upon in the ultimate event is the diesel generator. The architecture of the micro-grid is shown in Figure 1. The energy sources of this micro-grid are used to meet the needs of three different loads. Proposed system and strategy The proposed system interconnects with the existing micro-grid through specific nodes that we named S.A.D (for Sensor / Actuator Device). It is nothing more than a set of sensors and actuators linked to a microcontroller. The S.A.D are placed in specific places depending on what we want to monitor and control. The S.A.D. are linked to a control and management center thus forming a network of 177 Assilevi et al. | TH Wildau Eng. Nat. Sci. Proc.1 (2021) “SusRES 2021” microcontrollers. This control center is connected to the cloud to which it sends data for monitoring needs and which is also stored in a database which is analyzed for future uses. A. Proposed architecture Figure 2. Control and management system architecture Figure 3. Control and management system architecture details 178 Assilevi et al. | TH Wildau Eng. Nat. Sci. Proc.1 (2021) “SusRES 2021” Figure 2. shows us the general architecture of the micro-grid connected to the network of microcontrollers formed by the S.A.D.. Thus presenting the architecture of the control and management system of energy flows. The Sensor / Actuator association for each S.A.D depends on the source or the load to be controlled; therefore varying the number of elements to be connected to the microcontroller as shown in Figure 3. The microcontroller network as well as the control strategy are shown in the following sections. B. Microcontroller network 1. Sensor / Actuator Device (S.A.D.) Figure 4 is an internal image of the S.A.D showing the connections of the microcontroller to the sensors on the one hand and to the actuators on the other hand. Figure 4. Sensor / Actuator Device Each S.A.D in the network has a specific role to play depending on its location in the network. Table 1 summarizes the roles of each sensor and actuator of each S.A.D. Table 1. S.A.D roles in the network N° S.A.D Source / Load Sensor / Actuator Roles 1 PV Pannel Sensor 1 Sense power from PV pannel Sensor 2 Sense voltage and current between DC/DC converter and DC Bus Sensor 3 Sense voltage and current between DC/DC converter and Battery 1 Actuator 1 Control link between PV panel and DC/DC converter Actuator 2 Control link between DC/DC converter and AC Bus 179 Assilevi et al. | TH Wildau Eng. Nat. Sci. Proc.1 (2021) “SusRES 2021” Actuator 3 Control link between DC/DC converter and Battery 1 2 Wind Turbine Sensor 1 Sense power from Wind turbine Sensor 2 Sense voltage and current between AC/DC converter and DC Bus Sensor 3 Sense voltage and current between AC/DC converter and Battery 2 Actuator 1 Control link between Wind turbine and AC Bus Actuator 2 Control link between AC/DC converter and DC Bus Actuator 3 Control link between AC/DC converter and Battery 2 3 Battery Sensor 1 Sense voltage and current from Battery 1 Sensor 2 Sense voltage and current from Battery 2 Sensor 3 Sense voltage and current between DC/DC converter and DC Bus Actuator 1 Control flow from Battery 1 to battery bank Actuator 2 Control flow from Battery 2 to battery bank Actuator 3 Control link between DC/DC converter and DC Bus 4 Diesel Generator Sensor Sense power from Diesel Generator Actuator Control link between Diesel Generator and AC Bus 5 Load 1 Sensor Sense Load 1 voltage and current Actuator Switch ON/OFF Load 1 6 Load 2 Sensor Sense Load 2 voltage and current Actuator Switch ON/OFF Load 2 7 Load 3 Sensor Sense Load 3 voltage and current Actuator Switch ON/OFF Load 3 2. S.A.D. Management and Control Center The S.A.D. Management and Control Center consists of an Arduino MEGA board to which all the S.A.D. of the network are connected. It exchanges data with the raspberry board which is connected to the cloud through a 5G mini router as shown in Figure 5. 180 Assilevi et al. | TH Wildau Eng. Nat. Sci. Proc.1 (2021) “SusRES 2021” Figure 5. S.A.D. Management and Control Center C. Flows control strategy The S.A.D senses the flows generated and calculates the powers of energy supplied by the sources on the one hand, and the powers of energy required by the loads on the other hand. These data are the inputs for the strategy of control and balancing of energy flows in the micro-grid. The strategy is summarized as follows: First, the powers generated by the sources and calculated are compared to the powers of the loads: Psolar : PV pannel power Pwind : Wind power PDG : Diesel Generator power PS-W : Solar and Wind total generated power The voltage at the output of the battery bank is also taken into account. Vbat : Battery Bank Voltage Vbat-min : Minimum Battery Bank Voltage Vbat-max : Maximum Battery Bank Voltage In principle, the power of each load is taken into account the total power is then calculated. PL1 : Load 1 power PL2 : Load 2 power 181 Assilevi et al. | TH Wildau Eng. Nat. Sci. Proc.1 (2021) “SusRES 2021” PL3 : Load 3 power PL : Loads Total power At the initial state, powers are computed as follows: PL = PL1 + PL2 + PL3 PS-W = Psolar + Pwind For the control flow balancing: If PL > PS-W then Check Vbat If Vbat > Vbat-min then switch ON link between DC/DC converter and DC Bus If still PL > PS-W and Vbat = Vbat-min then switch OFF link between DC/DC converter and DC Bus and switch ON DG If PL ≥ PDG then switch OFF Load 3 If still PL ≥ PDG then switch OFF Load 2 If Psolar available and Vbat ≤ Vbat-min then swith ON link between DC/DC converter and Battery1 If still Vbat ≤ Vbat-min then check Pwind If Pwind available and Vbat ≤ Vbat-min then swith ON link between AC/DC converter and Battery2 If Vbat > Vbat-min and Vbat ≤ Vbat-min then swith OFF link between DC/DC converter and Battery1 and swith OFF link between AC/DC converter and Battery 2 If PL < PS-W then switch OFF DG switch OFF Load 2 and OFF Load 3 This strategy is translated into an algorithm and implemented in a python script that runs on the raspberry board and arduino code on arduino board. The information is displayed on a monitoring interface accessible via the cloud. Figure 6. Monitoring Screen On this screnn, Psolar and Pwind are at maximum of power. In this case, the generated power PS-W can easily supply all of loads (Load 1, Load 2 and Load 3). So, Battery bank and Diesel Generator are not used. 182 Assilevi et al. | TH Wildau Eng. Nat. Sci. Proc.1 (2021) “SusRES 2021” Simulations and results Table 2. Sources informations N° Sources Values (Voltage – Current – Power) 1 PV Pannel 137 V - 5 A - 685 W 2 Wind Turbine 126 V - 14.5 A -1827 W 3 Battery 124 V - 3 A 4 Diesel Generator 240 - 3.5 A - 840 W A. Case 1: Wind Turbine OFF Figure 7. Monitoring Screen with PV panel Source and battery bank ON The power generated is not sufficient to supply the loads, the battery bank is then used. B. Case 2: PV pannel OFF In this case, Wind turbine and PV panel are OFF and Battery voltage is not enough, so we swith ON Diesel Generator. 183 Assilevi et al. | TH Wildau Eng. Nat. Sci. Proc.1 (2021) “SusRES 2021” Figure 8. Monitoring Screen with DG ON and all loads ON C. Case 3: Diesel Generator ON With the DG being ON if the power generated does not cover the loads, we switch Load 3 OFF (Figure 9.) and if it is still not sufficient, we switch Load 2 off (Figure 10) Figure 9. Monitoring Screen with DG ON and Load 3 OFF 184 Assilevi et al. | TH Wildau Eng. Nat. Sci. Proc.1 (2021) “SusRES 2021” Figure 10. Monitoring Screen with DG ON and Load 2 and Load 3 OFF Conclusion In our work, we designed a microcontroller architecture interconnected to the renewable energy micro-grid. A strategy is then implemented for the control and balancing of energy flows in the micro-grid. The set of microcontroller-sensor-actuators (S.A.D) are deployed at strategic points in the micro-grid providing constantly data from power generated and consumed, equipment health and status. 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