An Efficient Technique-Based Distributed Energy Management for Hybrid MG System: A Hybrid SBLA-CGO Technique

This manuscript presents an optimal energy management on microgrid (MG) connected to the grid that chooses the energy scheduling based on the proposed method. The present method is the joint implementation of the Side-Blotched Lizard Algorithm (SBLA) and the Chaos Game Optimization Algorithm (CGO) and hence it is named as SBLA-CGO method. Here, the MG system contains a photovoltaic system (PV), wind turbine (WT), battery storage (BS) and fuel cell (FC). Constantly, the necessary load demand of MG system connected to the grid is measured with SBLA method. The CGO increases the perfect match of MG with the expected load requirement. Moreover, renewable energy forecasting errors are evaluated twice by MG energy management for minimizing the control. Through the operation of MG schedule of several RES to decrease the electricity cost using the first method. Balancing the energy flow and minimize the effects of prediction errors according to the rule presented as planned power reference is second method. The main aim of the present method is evaluated with connection of fuel cost, the variation of energy per hour of the electrical grid, the cost of operation and maintenance of MG system connected to the network. According to RES, the energy demand and SOC of the storage elements are the conditions. Renewable energy system units use batteries as energy sources to allow them to operate continuously on stable and sustainable power generation. The analysis of the present method is analyzed by comparing with the other systems. The results of the comparison assess the strength of the present system and confirm their potential for solving the issues.

(DR) was assessed and extra codes were also evaluated. To calculate the generation, storage and receptive load offers, the ALO system was introduced to rectify the economic dispatch problems.
A distributed energy management system of MG community was presented by G. . In all repetitions, the central controller MG aligns the DER and ESS programming at MG level. Optimization occurs while the disturbed power of entire buses approaches zero. The dynamic thermal model house was interconnected with the HEMS to control heating, ventilation and air conditioning system through clients. Y. Liu et al. have established a safe distributed phrase energy management (S-DTEM) strategy of multiple interrelated MGs .
Every MG was maintained through a distributed MG ESS that only converts the commercial quantity and price information with other MGs for preserving the privacy of information. While every MG involves a buyer, S-DTEM will change the energy selling price and working period for reducing their local cost by trading energy with MGs / main stage. This method could reduce the cost of integrated MGs. With quadratic barrier functions, the finite time integration of S-DTEM could analyze certain difficult operating conditions. An intentional MG-EMS can penalize the prohibition of maximum function; the misbehavior detection mechanism was also developed by the finite-time convergence property. X. Yang et al. have described a bilayer game theoretic method  for IEM of multi-MG (MMG). The maximum layer of the method has maintained the energy trade and consumption behavior of every MG; the cost model was intended based on economic factors and the will of the users. The minimized segment executes at high frequency and regulates the MG operations for reducing the larger and actual segments. Also, the supply and demand variables and the output events could be managed correctly and the energy trading behind the MGs may gain directly devoid of disturbance. J.
Zeng et al. have developed a completely distributed operating method of MEMS with maximum penetration and demand for renewable energy (Zeng et al. 2018). The iterative best methodresponse algorithm was developed for choosing the Nash equilibrium game. Finally, to validate the effective and validity MG method was evaluated.

Background of Research Work
A review of current investigation operation illustrates that, energy management of hybrid RES devices for MG. At energy management system, the power generator persistence is a great challenge. The energy source management is established using energy supervision system and committed to solve the energy sources of RES and cost factors included in the problem.
Additionally, there are several methods that are used: fuzzy, neuro-fuzzy and optimization methods on MG energy management system. Through fuzzy logic controller provides best outcomes hence it does not categorize fuzzy system theory unique nature. On contrary, it has been evaluated that PSO has a better optimal search capacity. Moreover, the PSO algorithm, there are random variations in velocity equation, thus varying the better value from unstably. Also, a RES control method is primarily evaluated for tracking the power requirement and control the DC bus voltage. Finally, the integrated MG system is evaluated to defeated this challenge and offer a hopeful solution. Several work-based methods are established at bibliography to remedy this issue; these demerits and troubles have encouraged this investigation work.

Energy Management Configurations with MG Connected System
This section introduces the energy management system with the MG system connected. EMS is used to assess and verify the MG power management is evaluated on grid-connected mode for the purpose of performance. The MG may be organized through corresponding classifications subsequent to the control tasks have been performed. Below the grid mode during MG task, load demand should be met at entire time. To gain the main goal, the MGs goal may be set and imagined using optimization method. The main objective is to establish with the production of wind / solar energy, Fc and battery. Resources are energy storage devices and unassignable / assignable resources. The control factors considered the dynamic power and operating position of ESS and transmitting units to determine energy management tasks. Moreover, a power equilibrium condition, the power of the controllable units and optimal start-up time of the transmitting units are evaluated. At upcoming time steps the necessary increase/reductions for future time steps is stable. The power of transmissible / non-transmissible resource units and ESS is coordinated premature. By choosing the most optimal control group of controllable units, assuming the market prices, the prediction power of non-transmissible units load level, the proposed method is improving the revenues in a provided time horizon. Using the proposed method, the cost of fuel for dispatchable resources is described as energy management problems.

Problem Formulation
The mathematical modelling of renewable sources like WT, PV, FC and Battery are articulated as follows,

Modelling of Photovoltaic (PV)
In RES one of the sources is PV, it can able to occupy the sunlight and directly transform the sunlight into electricity. Based on the solar radiation, output power produced by PV is expressed as follows,

Modelling of Wind Turbine (WT)
The WT output power is evaluated through power curve shown at below equation.
From the above equation (2), the optimum unit of wind power expressed as WG p , then the wind speed, minimum wind speed, cut out and cut in wind speed is expressed as

Modelling of Battery Energy Storage System (BESS)
Depending on weather conditions, the energy production of RES is evaluated. To defend the frequency and system voltage for saving the excess power the BESS is employed and the MG system load changes or power production of Res is less means the BESS will provide the power to the load. The variation of PV and WT was smoothening by the BESS. Compared to the MG system, the load is low while the power is produced by RES then for succeeding usages the extra power will save in BESS. Related to the earlier SOC, generated power of RES and specified overall loads of the system, the energy saved in the BESS at time t which is expressed from below, e Ch Ac Dc here the efficiency of battery charging is expressed as , based on RES, the overall energy produced is represented as ) (T e RES , in the MG system, and the overall provided energy is expressed as Load e .

Constraints
The overall power produced from the DER, the energy saved at BESS and energy exchanged through the grid will fulfill the overall demand for MG. At time t output constraints of DER convey the generated power of each DER which will be in upper and lower bounds of production of power for all kinds of DER. The constraints of DER is described as follows, The increased and decreased suitable saving ability of BESS in energy storage is expressed as follows, From the above equation, the DOD is expressed as the Depth of battery discharge. The overall produced power by DER, energy storage in BESS and the interchanged energy at all time by grid will be fulfill the overall demand of MG.

Formulations of Objective Function
The main purpose of the work is to reduce the annualized cost of system. The overall cost of the system comprises: replacement cost, overall capital cost, operating cost, power purchasing cost and maintenance cost. In the capital cost the updating cost is also occurred. The cost minimization of the objective function is shown below, here the annualized cost of system is expressed as c, the annual cost of the PV, WT and Fc is represented as PV c , WT c , FC c . The overall cost of electricity is expressed as GS c , GP c . The annualized cost of each element is the capital, operational and replacement cost.

Annualized Cost
The cost of capital of the element comprises the cost of installation and purchase. The annualized cost of all elements like PV, WT and FC which s expressed as follows, From the above equation, the initial capital cost of WT is expressed as  

Annual Replacement Cost
Replacing the WT final life is denoted as annual replacement cost. The overall replacement cost is expressed as follows,

Proposed Approach of SBLA-CGO Based on Energy Management System
Based on proposed method is presented in this paper. The present method is the joint implementation of both the Side-Blotched Lizard Algorithm and Chaos Game Optimization and hence it is named as SBLA-CGO method. Here, the MG system contains a Photo-Voltaic (PV) system, Wind Turbine (WT), Battery Storage (BS) and Fuel cell (FC). The required load requirement of grid associated MG system is measured using SBLA method. The exact combination of MG is increased with the load demand condition forecasted using CGO. The energy management system with HIRES is a figure 3. A step-by-step process of the proposed method

Load demands using SBLA
Step 1: Initialization The input parameters like PV, WT, FC and battery

Step 2: Random Generation
After the process of initialization arbitrarily create the distributed solutions along lower and upper bounds.

Step 3: Fitness Function
Utilize the below functionality to establish cost reduction.
Step 4: Subpopulation Produce the frequencies of subpopulation and for deflect the depletion of morph a less number of population is maintained.

Step 5: Population Changes
Based on the fitness function if the initial population is produced and then the subpopulation colors are dispatched. In the population changes it has many subpopulations which are described as follows,

 Delete Function
If the population of morph interchanged as negative then the algorithm cells delete the function of lizard.

 Transform Function
When the changes in the population of the color with the described population has a positive change and the one affected by it has a negative one the methods conducts the transform lizard function.

 Add Function
In one population if there is a positive change in the population index or one is affected and it did not have negative change means the algorithm will add a population function.
Step 6: Termination After completing the above process the population will give best position and to enhance more values the iterative process will repeat.

Chaos Game Optimization for MG System
Step 1: The preliminary location of applicant's solution or the previous eligible points on search space is specified interms of random process of selection approach Step 2: Previous points are measured to the fitness values of the previous applicant's solution based on self-similarity.
Step 3: Global best process: In this process the maximum stage of qualification is evaluated.

Fig 4: Flowchart of Proposed SBLA-CGO
Step 4: The mean group is evaluated at the search point for the qualification point based on the random process.
Step 5: In the search space for the qualification point with three vertices a transitory triangle is evaluated.
Step 6: The fitness values of the new candidate solution are measured depends on selfcompatibility problems.
Step 7: The early qualifying points with poor fitness values equivalent with poor self-similarity levels are replaced through novel seeds.
Step 8: The termination criteria is evaluated.

Result and Discussion
The simulation consequences of proposed and other method are evaluated on this section. To reduce the overall cost creation and enlarge PV and WT usage.   Figure 20 illustrates that comparison of CO2 emissions with the proposed and existing system. Figure 21 shows the comparison of COE through proposed and existing method. Figure 22 shows a comparison of proposed and existing system. Figure 23 shows the amount of fuel consumption compared to the proposed and existing method.   Table 1 illustrates that statistic investigation of proposed and existing method based on mean, median and standard deviation (SD).

Conclusion
In this paper presents the optimal energy management of PV-WT, FC and ESS hybrid energy systems connected to grid using the SBLA-CGO method. The paper evaluates the modelling of