Research oriented to harness energy from the renewable resources has gained importance in the past few years. The renewable energy can be used as distributed generated (DG) units to obtain balance between supply and demand. The limitation in implementation of these sources lies in their intermittency and operational difficulties, variation in voltage, magnitude, and imbalance between active and reactive power. Implementation of integration of these resources into the conventional grid involves enhanced automation techniques, advanced control strategies and efficient voltage management techniques. The emerging potential of DG can be efficiently utilized by using system approach which views generation and loads as a sub-system, termed as a microgrid (MG). A MG can be operated along with the utility grid in grid connected mode or independently in islanded. The proposed microgrid consists of an Energy storage unit (ESU) and distributed generation unit including PV panels, wind turbines, sea water desalination generators, a water tank and few loads as shown in

A microgrid compresses of multiple generation units, Energy storage units and critical/non-critical loads as shown in^{1, 2} is used to connect microgrid to the utility grid. All the distributed generation units have PEI connection in order to attain control protection, metering objectives in addition to plug-in feature, whether in grid connected or islanded mode. If the microgrid is connected to utility grid, it can give away the surplus power to the utility. The microgrid can change its mode of operation from grid connected to islanded mode in case of disturbance or failure in the utility grid. The critical loads are first supplied by the microgrid under any circumstance. A microgrid central controller (MGCC) controls all these operations along with local controllers (LC) ^{3}. The system performance and sustainable development has been considerably improved by effective coordination of Distributed Energy Resources (DERs) and energy management in microgrid ^{4}.

Microgrid can operate in grid connected or islanded mode. Controlling of microgrid in grid connected mode is easier than islanded mode since the frequency of microgrid in grid connected mode is regulated by utility bus frequency. Another important criterion to be monitored in islanded operation is the nature of ADG resources. If the resources of energy used are renewable in nature, their intermittent and dilute nature pose problems in the working of the grid ^{5, 6}. For the effective energy management of micro grid, a large number of studies and models have been developed referring to all previously carried out research work, the research can be categorized into 3 types: Day- Ahead Scheduling (DAS), ^{7, 8}, real time dispatch energy optimization ^{9}, and model predictive control (MPC) ^{10, 11}. In implementing the strategy of real time power adjustment, the changes in future is not considered and only the present status of the grid is taken for account. In DAS approach, it is open-loop scheduling method, and as is the case with open-loop systems, Due to prediction error it will deviate the original values over a large period of time in optimal resultant result. To enhancement of energy management by adopting closed loop feedback time scheduling methods where the updated iteration probability is saved during the energy optimization procedure. Performance of optimal time period for microgrid comparison with conventional DAS it shows the better result in terms of efficiency, flexibility with respect to economy point of view ^{12, 13}. The limitation of uncertainties & intermittency of renewable energy sources are addressed in previous works by stochastic programming approach. However, this approach has a limitation that the input data entered should abide by certain rules which is not possible in reality ^{14}. In this paper, a study is made on energy management scheme of islanded micro grids which consider the uncertainties in the renewable energy sources.

The islanded Microgrid consists of Arbitrary distributed generators (ADG), Energy Storage Unit (ESU) and loads. The Energy Management System [EMS] controls and co-ordinates all these components of Microgrid ^{15, 16}.

t | Timing Index |

i | Equipment Index |

y | Predictive zone (h) |

Dt | Time Interval of each period (h) |

αESU,i | Energy Storage unit self- discharge rate (kW) |

Ncload | Number of flexible loads |

γcloadmax , γcloadmin | Maximum and Minimum Restriction of flexible loads (%). |

σrload,i (t) | Penalty factor (₹/kWh) |

EESUmax, EESUmin | Energy Storage Unit Maximum, Minimum energy level (kWh) |

CESUmax, CESUmin | Maximum Charge / Discharge levels of Energy Storage Unit (kW) |

ηESUc , ηESUd | Energy Storage unit charging and Discharging efficiency |

UMESU | Utilities and maintenance of Energy Storage Unit (₹/kWh) |

NADG | Number of arbitrary distributed generator |

PADG,i , maxPADG,imin | Maximum and minimum power outputs with respect to ADG ( kW) |

VADG,i (t) | Fuel utilization cost function of ADG (₹) |

∆PADG,i | ADG Ramp power (kW) |

xi, yi, zi | Cost coefficient of VADG,i(t) |

τiup, τidown | Tiniest up/Down time interval (h) |

UMADG,i | ADG Utilities and maintenance costs (₹) |

VADG,iup, VADG,idown | Startup, shutdown cost of ADG (₹) |

Spower (t) | Solar power production (kW) |

floadt | Flexible load demand (kW) |

Cload(t) | Critical load demand (kW) |

Wpower(t) | Wind power production (kW) |

βADG,it | ADG on/off status |

δcload(t) | Flexible loads curtailment (%) |

PADG,it | Power output of ADG (kW) |

βESU(t) | ESU charge / discharge status |

CDESU(t) | ESU charge / discharge rate (kW) |

EESU(t) | ESU energy level (kWh) |

Where

ESU plays a major role in the Microgrid. It acts as a load to store excess power generated and as a standby power supply during deficiency of power from energy sources. The energy storage charging / Discharging power limit, energy storage capacity, supercapacitors storage limit, relation between charging and discharging power are the parameters used for modeling the ESU the constraints of ESU can be described by the following equations (3), (4), (5), (6).

where

A battery, is the basic unit in the battery storage system shown in^{17, 18}. If there is shortage of power generated due to intermittency of ADG units, the storage energy in ESU can be made use of the ESU also helps in achieving reliable power transfer to all the loads connected. The components in a battery storage system includes battery, monitory and control equipment, power converters and auxiliary units and simulation result of energy storage unit as shown in

The mixed logic dynamic (MLD) approach makes the reduction of prediction control error by adopting the scrambling process into a mixed integer programming problem ^{19}. Equation (5) is thus equivalent to the following equations (7), (8), (9), (10), (11), (12), (13).

Where

The ADG units can have diesel, steam generation, renewable sources like solar, wind, biomass etc. equation (15) and (16) represent the power output limit and ramp power limit. The minimum up and minimum down time constraints are satisfied by using equation (17) and (18).

Where

The ADG units have initial cost, Utilization and maintenance costs equation (19) shows the fuel consumption cost function of ADG ^{20, 21, 22}. The startup cost and shut down loss of the DG units should satisfy (20)-(21). The DG operation cost is represented by equation (22).

Where

Ac microgrid is a popular type and follows the traditional electric power system structure. Ac microgrids can be easily designed and implemented by utilizing the available AC network infrastructure which includes distribution transformers and protection this AC microgrid also proves to be reliable in operation. An example of AC microgrid structure is shown in

A microgrid comprises multiple generation units, Energy storage units and critical/non-critical loads as shown in

The critical loads are first supplied by the microgrid under any circumstance. A microgrid central controller (MGCC) controls all these operations along with local controllers (LC) ^{23, 24, 25}. The system performance and sustainable development has been considerably improved by effective coordination of Distributed Energy Resources (DERs) and energy management in microgrid. A battery, is the basic unit in the battery storage system shown in

During all the time internals considered, the electricity generated should satisfy corresponding demand equation (26) represents the power balance constraint.

To accomplish energy management in islanded Microgrid it is important to maintain a low cost of the entire operation in the whole “predictive zone” equation (27) gives the objective functions and its solutions provide the control action.

^{26, 27, 28}. In this work, we have define a parameter ζ , as the degree of uncertainty . ζ takes values in the interval [0, |U|] where U represents set of uncertain parameters. If ζ is varied, then the robustness of the video can be adjusted against conservation levels of the solution. For the constraint presented in (25), the uncertain parameter

At the optimal solution, the optimization ensures that the constraint becomes equality first stage operational equation (28) can be reformulated as follows second stage operational equations (29), (30), (31), (32), (33), (34), (35), (36), (37), (38):

Where

In the similar manner, the objective function (27) has to be dealt for the uncertain parameter

To summarize the robust counterpart of the model can be defined as:

s.t. (1)-(2), (5)-(26).

Here

Where

To summarize, the proposed model has the robust counterpart shown in section II which can be described as:

The parameters representing the degree of uncertainty in the model are subjected to normalization and uniformly represented by

Step_1: The high-speed reconfigurable energy management optimization model has been derived.

Step_2: The probabilities values updating of all automotive equipment’s in the islanded Microgrid (Including ESU energy level _{ESU}(t), DC_{ADG,i }(t)

Step_3: The control sequence is acquired by solving the limited zone ideal issue (43).

Step_4: The main thing of the control grouping must be applied the control procedure must be balanced in agreement to the real estimation of forecast parameters.

Step_5: Step_2 is implemented by making t=t+1.

The access to the values of solar and wind energy generation output and load demand is obtained from global energy forecasting competition 2019.Here we have taken two days data.

SI.NO | Power out limit | Ramp Power limit | Min up/ Down time | Cost co-efficient | ADG Startup, Shutdown cost |

1 | 600/10 | 510 | 2/1 | 0.00048/3.2 | 3.2/3.38 |

2 | 760/20 | 550 | 2/1.5 | 0.00055/0.56/3.7 | 3.53/4.3 |

3 | 40/0.4 | 40 | 0.4/0.4 | 0.0015/0.73/2.4 | 1.01/1.99 |

Curtailment of loads, during the operation of Microgrid should be avoided as far as possible unless it is an emergency. 0.2 is the maximum curtailment and the penalty cost is 5 times the average loss of power generation.

Risk Level | 1 | 2 | 3 | 4 |

Max predictive deviation | 4.98% | 9% | 14% | 19% |

Risk Level
Level_1
Level_2
Level_3
Level_4
EHMPC
14103.9
14208.8
14395.8
14657.8
MPC
14156.8
14478.1
14865.3
14656.56

From the analysis and observation of the data in

The results shown in

This work proposes the EHMPC based optimization strategy for optimal scheduling of the microgrid with PV and wind energy sources. Internal forecasting models are used to obtain the data from PV panels, wind turbines load and water demand. The primary robust optimization model can be transformed into ESU model by using robust linear methods and further the solution can be obtained using suitable software. The case study has the following observations:

The EHMPC strategy has better predictive output efficiency with 98.23% in comparison to the conventional MPC. .

sage of ESU increases the reliability of operation, reduces 19% of actual operation cost and capital cost of arbitrary distributed generation units.

The reliability, flexibility and efficiency of the microgrid has increased with EMS techniques and devices being integrated into the microgrid.

The authors acknowledge with thanks Technical Education Quality Improvement Programme (TEQIP) of Visvesvaraya Technological University (VTU), Belagavi for technical and financial support.