Microgrid Technologies. Группа авторов

optimization has been implemented by the authors of Ref. [43]. They have done the optimization for keeping power balanced in-between generation and utilisation, taking more than one objective functions. The objective functions can be taken, such as bus voltage stability, maximum power extraction from PV panel, maximum battery protection by keeping it in a suitable range of SOC. Reduction in hydrogen consumption minimized the fluctuation.

Figure 1.6 Micro-grid in Islanded mode.

The authors of Ref. [44] have expressed an MSE for the microgrid in standalone style such as solar power or wind power, etc. which has an objective function of cost reduction. The cost of an MG is consisting of the cost of energy injected by the distributed sources, the cost of energy storage unit like the cost of energy during charging or discharging, expenditure on the utilized energy, and the charges of penalty for undelivered power.

      In Ref. [14], the authors have developed an MSE for different distributed sources like wind turbine ‘WT’, photovoltaic ‘PV’, plug-in EV ‘PEV’, diesel generator system ‘DGS’ and battery in islanded microgrid. The objective function is to make the best use of the number of adjustable loads during the islanded operation manner. The problem of optimization has been utilised to balance power. The constraints are load, limitation of the power generator and also battery specification.

      In Ref. [45], the researchers have specified an idea about a cost-effective structure of thermal power plant. The goal of optimization is to decrease the cost of fuel of the thermal generators, taking consideration of the effect of the loading at the valve’s point. The constraints are power balancing, power generator’s limitations, and the ranges of operation of prohibited generators and its limits of ramp rate.

1.3.4 Micro-Grid Operation in Grid-Connected Mode (Figure 1.7)

       1.3.4.1 Objective Functions and Constraints of the Systems

      In Ref. [6], the authors have represented a cost minimization EMS of GC–HKT system with a storage system, which consists mainly of three types of costs. The energy purchasing cost from the grid satisfies the load requirement and also the battery charging is the initial cost. The second cost is during the high costing time the revenue comes from exporting electricity to the primary grid. The third one is wearing cost or maintenance cost in the system. The authors choose power balance, limitations of HKT’s production and SOC of the battery as optimization constraints.

The authors in Ref. [46] have optimized GC MG with generators, Energy Storage System and electrical load. The generators maybe detachable or not. The overall energy charges are assumed as the goal function. Such as the fuel utilization cost, cost of charging the battery, cost of discharging the battery, the energy cost generated by detachable and non-detachable generators, the energy cost generated by trading power in between MG and the main grid network, and the cost of conveying energy demand all over the arrangement. The authors have incorporated environmental and technological factors as constraints to get the optimized result. The total quantity of energy produced by the end of the generator should be the same as the total quantity of energy at the consumption. The consumer and supplier of electricity are taken as technical constraints. Energy storage constraints include charging/discharging limitations of battery, the energy balance of battery, energy storage limitations and SOC of battery. In the end, the constraint was grid limitations that the quantity of exchanged energy in between the main grid network and MG.

Figure 1.7 Microgrid in Grid Connected mode.

      The authors of Ref. [47] have given an optimized resolution for a mixed PV/WT/FC/HPC system, which is operating in the mode of grid connection. The authors have minimized the operational/running cost and maximized the system profits as system working cost includes (i) the fuel cost, (ii) the energy purchasing cost from the main gird, (iii) the installation cost of the system, (iv) the operation & maintenance cost of power generators. The authors have considered the system profits as the revenue in selling surplus energy (thermal and electrical) to the main grid when the net production of the distributed generation system go beyond the overall energy demand by the load.

The MSE for grid-connected HPC with both battery and thermal SSE, heat only boiler (HOB), thermal loads and electrical loads has evolved in Ref. [48]. This MSE technique has minimized the expenditure of the system operation. The authors have considered the expenditure of energy from HPC, HOB, CDG units, cost of importing energy from main grid and exporting back to the grid, and installation cost of different components of micro-grid. Now electrical power and thermal power balance are the main constraints for the optimization. The balancing of power guaranteed the electrical energy production and charging or discharging of the battery must be same as the consumption in electric load and energy tariff with the main grid. The thermal balance system is that when the amount of thermal energy produced at local thermal generators must be equivalent to local load (thermal) requirement and the energy (thermal) mutually shared with any other units of MGs. Other constraints are such as the capacity of the components of each MG. HPC, HOB and CDG must operate in their specified limitations. The quantity of energy exchanged with the main grid is limited by the capacity of the electric line. Also, the quantity of heat energy exchanged between two units of MG is limits with the thermal line capacity. The heat energy exchanged between thermal networks, and MGs is another constraint, which reduces thermal energy wastage when production of local thermal energy exceeds the demand of the local thermal load.

1.4 Algorithms Used in Optimizing Energy Management System

      Energy management in a micro-grid is addressed by applying different approaches. All the approaches have the common aim to optimize the MG operation. Some methods are supported on linear or non-linear programming such as in Ref. [49] where a MILP is used to optimize the system. The cost function solution is obtained by linear programming, which is based on GAMS (general algebraic modeling system).

In MG, the energy obtained from RES, load demands and market rate of energy are considered as stochastic variables because of uncertainty. So, it is good to use stochastic modeling to analyze any energy management strategies. Generally, researches have gone through stochastic algorithms or metaheuristic algorithms to solve problems of optimum power balancing in MG. According to Ref. [50], in stochastic programming, the data is stochastic, and the result or solution is dependent on the collection of variables that arbitrarily generated. Recently researchers are considering that the management system of energy in micro-grids stand on the implementation of advanced technology such as collaboration of a variety of optimization technologies or improving classical algorithms, to get the most suitable solution of a problem for MSE in MG.

      In Ref. [51] a multi-objective genetic algorithm was applied to a standalone system having an internal combustion engine and gas turbine with the PV module. In Ref. [52] the author represented a dynamic programming technique for a standalone micro-grid. The micro-grid is consisting of DG, PV panel and battery. Here the constraints of the problem are supply–load balancing and the capability of the supply generators. The main goal is to minimize the functioning cost and emission.

      The authors in Ref. [53] represented a relative analysis of the various objectives of the optimization methods for MSE of standalone micro-grids. The comparison is based on linear programming and genetic algorithms. The result was found out that the controllable power consumption can reduce the cost with renewable energies.

      In Ref. [54], the weight factor has been analyzed to increase the ability of PSO (Particle Swarm Optimization) technique and to balance the convergence.