**1. Introduction**

The traditional bulk power generation, transmission and distribution system is facing a lot of technological challenges to fulfil the growing demand and increased penetration of distributed energy resources. The existing infrastructures are also outdated, which hinders the integration of newer technology for capacity enhancement and sophisticated monitoring and control. Hence the need has arisen for distributed generation which can co-exist with existing bulk power networks [1]. In recent years, there has been significant growth in renewable energy generation through wind and solar resources. A microgrid is a miniature version of the bulk power system with distributed energy resources capable of serving as an independent electrical island separated from the bulk power system [2]. Microgrids employ environmentally benign energy sources like solar, wind, and fuel cells [3]. The higher the penetration of sustainable energy sources the more the socio-economic benefits will be. The recent advances in control and communication technology facilitate robust and intelligent control of microgrids [3–5]. In emerging economies, to encourage independent sustainable energy generation, there is a strong regulatory framework which in turn will constitute the microgrid building blocks.

The Figure 1 depicts the microgrid architecture under consideration for an energy management system (EMS). The proposed microgrid system comprises sources like the utility grid, a diesel generator, photovoltaic (PV) generator, and a battery energy storage system (BESS) [3,6]. The loads are classified

into secure and non-secure loads [7]. All secure loads are supplied from an uninterruptible power supply (UPS), while the rest of the loads are supplied directly either from the utility grid or from distributed energy sources (DES) [8,9]. All the sources and loads are connected through appropriate circuit breakers. The current and voltage feedback signals from the loads and local feeder lines are fed to the EMS controller. The control signals to circuit breakers are sent from the EMS controller. The input and output data of the EMS is shown in Figure 2. Figure 3 depicts the typical data flow between sources, load and controller. The main controller receives active power, reactive power, voltage, and current data from the local/embedded controller from the DES. Table 1 lists the specification of loads and sources used in this analysis. The cost of energy data from the grid is fed from the utility side. The cost of energy for local generation using DES within the microgrid are fed manually into the EMS for decision-making purposes to achieve economic operation. The user interface of the EMS will allow users to manually enter the specific parameters based on which the power flow decisions to be made. The central database which stores historical load demand, and the actual forecast data will be processed in the EMS for effective load management and power delivery.

**Figure 1.** Microgrid schematic diagram.

**Figure 2.** EMS controller Input and Output data.

**Figure 3.** EMS controller data flow between controller, load and sources.


**Table 1.** System specification considered for analysis.

The communication network will carry the control and feedback signals over the network. This will facilitate having proper co-ordination and control among the loads, sources and utility. All the measured critical parameters of the respective devices connected to the network will be transmitted to the central/local controller over the specified communication protocol for processing and take appropriate decisions and actions based on the control algorithm. The parameters to be measured

are defined in the EMS data flow diagram in Figure 2. The Modbus RTU protocol has been deployed to acquire the data from various sources and loads. The EMS controller gets the weather forecast and cost of energy from the utility and then computes the energy forecast based on the historical consumption patterns. The forecast of renewable energy generation is estimated by the EMS controller using the weather data input. The decisions for controlling loads and DES are sent to the respective devices through RS485 or the TCP/IP protocol based on the device compatibility. Table 2 lists various parameters that are acquired from the sources and loads connected in the microgrid system to EMS controller and the respective output command. Figure 3 presents the single line representation of the data flow from all the connected devices in the microgrid network to the EMS controller. Figure 4 presents the communication architecture used in the microgrid system. The communication is divided into three parts: (i) device level; (ii) unit level and (iii) system level. Device level communication is point to point data transfer, unit level communication is controller to controller data exchange, and system level communication is like unit level, but over a long distance and bulk data exchange between microgrid networks. For system level communication, the IEC 61850 protocol has been considered, whereby the IEC 61850 9-2 process bus protocol facilitates Generic Object Oriented System Event (GOOSE) messages for data exchange with the EMS controller. For device level, since it is shorter distance the RS485 Modbus protocol has been considered. Modbus TCP has been considered for unit level communication. Further, this proposed communication architecture has a provision to be expanded for ZigBee and Wi-Fi protocols as per IEEE 802.15.4.

**Figure 4.** EMS controller data flow between controller, load and sources.

**Table 2.** List of parameters acquired from loads and sources to EMS controller and corresponding output control from EMS.


Presently there are many microgrid architectures under research, and the focus is predominantly on developing energy management solutions through sophisticated artificial intelligence technologies [5] for achieving superior economic benefits, but the same amount of focus is not present in developing coordinated control of DER, grid and loads with centralized controllers [10]. Having precise control at the individual device or source level and at the network controller level will facilitate the faster response, seamless transition of load sharing between sources, and more reliable operation of microgrids [4]. Keeping this in mind, the authors proposed a microgrid energy management system (EMS) to establish control at the device level and overall system level with the help of state of art communication technology [11,12]. In load level control, the proposed EMS enables precise management of power flows by forecasting renewable energy generation, estimating the availability of energy at storage batteries, and invoking the appropriate mode of operation, based on the load demand to achieve efficient and economic operation. The predefined mode of operation is derived out of an expert rule set and schedules the load and distributed energy sources along with the utility grid. In system level control, the focus is mainly on system stability and power sharing. The proposed new controller ensures the stability of the system during transition modes and steady state operating conditions which are validated with different load and source dynamics within the microgrid system. The connection and disconnection of PV generator from the grid in islanded mode and corresponding power sharing of diesel generator (DG) and battery energy storage system (BESS) are recorded and validated for conformance to the intended operation to ensure optimum power flow from different sources to loads.

#### **2. Load Management**
